ISPRS International Journal of Geo-Information

26 pages, 10177 KiB

Open AccessEditor’s ChoiceArticle

A Neural Networks Approach to Detecting Lost Heritage in Historical Video

by Francesca Condorelli, Fulvio Rinaudo, Francesco Salvadore and Stefano Tagliaventi

ISPRS Int. J. Geo-Inf. 2020, 9(5), 297; https://doi.org/10.3390/ijgi9050297 - 5 May 2020

Cited by 13 | Viewed by 3998

Documenting Cultural Heritage through the extraction of 3D measures with photogrammetry is fundamental for the conservation of the memory of the past. However, when the heritage has been lost the only way to recover this information is the use of historical images from [...] Read more.

Documenting Cultural Heritage through the extraction of 3D measures with photogrammetry is fundamental for the conservation of the memory of the past. However, when the heritage has been lost the only way to recover this information is the use of historical images from archives. The aim of this study is to experiment with new ways to search for architectural heritage in video material and to save the effort of the operator in the archive in terms of efficiency and time. A workflow is proposed to automatically detect lost heritage in film footage using Deep Learning to find suitable images to process with photogrammetry for its 3D virtual reconstruction. The performance of the network was tested on two case studies considering different architectural scenarios, the Tour Saint Jacques which still exists for the tuning of the networks, and Les Halles to test the algorithms on a real case of an architecture which has been destroyed. Despite the poor quantity and low quality of the historical images available for the training of the network, it has been demonstrated that, with few frames, it was possible to reach the same results in terms of performance of a network trained on a large dataset. Moreover, with the introduction of new metrics based on time intervals the measure of the real time saving in terms of human effort was achieved. These findings represent an important innovation in the documentation of destroyed monuments and open new ways to recover information about the past. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)

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20 pages, 6648 KiB

Open AccessEditor’s ChoiceArticle

Affective Communication of Map Symbols: A Semantic Differential Analysis

by Silvia Klettner

ISPRS Int. J. Geo-Inf. 2020, 9(5), 289; https://doi.org/10.3390/ijgi9050289 - 1 May 2020

Cited by 11 | Viewed by 5071

Abstract

Maps enable us to relate to spatial phenomena and events from viewpoints far beyond direct experience. By employing signs and symbols, maps communicate about near as well as distant geospatial phenomena, events, objects, or ideas. Besides acting as identifiers, map signs and symbols [...] Read more.

Maps enable us to relate to spatial phenomena and events from viewpoints far beyond direct experience. By employing signs and symbols, maps communicate about near as well as distant geospatial phenomena, events, objects, or ideas. Besides acting as identifiers, map signs and symbols may, however, not only denote but also connote. While most cartographic research has focused on the denoting character of visual variables, research from related disciplines stresses the importance of connotative qualities on affect, cognition, and behavior. Hence, this research focused on the connotative character of map symbols by empirically assessing the affective qualities of shape stimuli. In three stimulus conditions of cartographic and non-cartographic contexts, affective responses towards a set of eight shape stimuli were assessed by employing a semantic differential technique. Overall findings showed that shape symbols lead to, at times, highly distinctive affective responses. Findings further suggest two particular stimulus clusters of affective qualities that prevailed over all stimulus conditions, i.e., a cluster of asymmetric stimuli and a cluster of symmetric stimuli. Between the intersection of psychology, cartography, and semiotics, this paper outlines theoretical perspectives on cartographic semiotics, discusses empirical findings, and addresses implications for future research. Full article

(This article belongs to the Special Issue Geovisualization and Map Design)

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29 pages, 5759 KiB

Open AccessEditor’s ChoiceArticle

Geological Map Generalization Driven by Size Constraints

by Azimjon Sayidov, Meysam Aliakbarian and Robert Weibel

ISPRS Int. J. Geo-Inf. 2020, 9(4), 284; https://doi.org/10.3390/ijgi9040284 - 24 Apr 2020

Cited by 9 | Viewed by 4044

Abstract

Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and [...] Read more.

Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological map generalization is not yet available. In particular, while in other areas of map generalization constraint-based techniques have become the prevailing approach in the past two decades, generalization methods for geological maps have rarely adopted this approach. This paper seeks to fill this gap by presenting a methodology for the automation of geological map generalization that builds on size constraints (i.e., constraints that deal with the minimum area and distance relations in individual or pairs of map features). The methodology starts by modeling relevant size constraints and then uses a workflow consisting of generalization operators that respond to violations of size constraints (elimination/selection, enlargement, aggregation, and displacement) as well as algorithms to implement these operators. We show that the automation of geological map generalization is possible using constraint-based modeling, leading to improved process control compared to current approaches. However, we also show the limitations of an approach that is solely based on size constraints and identify extensions for a more complete workflow. Full article

(This article belongs to the Special Issue Map Generalization)

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19 pages, 497 KiB

Open AccessEditor’s ChoiceReview

A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

by Hung Cao and Monica Wachowicz

ISPRS Int. J. Geo-Inf. 2020, 9(4), 272; https://doi.org/10.3390/ijgi9040272 - 21 Apr 2020

Cited by 6 | Viewed by 3277

Abstract

The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices [...] Read more.

The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings on multiple spatio-temporal scales, interact with each other across a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently, most of the geospatial applications of IoMT systems are developed for abnormal detection and control monitoring. However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities. This paper examines the state of the art of IoMT systems and discusses their crucial role in supporting anticipatory learning. The maximum potential of IoMT systems in future smart cities can be fully exploited in terms of proactive decision making and decision delivery via an anticipatory action/feedback loop. We also examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to GIS. The holistic overview provided in this paper highlights the guidelines and directions for future research on this emerging topic. Full article

(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)

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24 pages, 2553 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Representing Complex Evolving Spatial Networks: Geographic Network Automata

by Taylor Anderson and Suzana Dragićević

ISPRS Int. J. Geo-Inf. 2020, 9(4), 270; https://doi.org/10.3390/ijgi9040270 - 20 Apr 2020

Cited by 15 | Viewed by 3425

Abstract

Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of [...] Read more.

Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling. Full article

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24 pages, 19751 KiB

Open AccessEditor’s ChoiceReview

A Systematic Review into Factors Influencing Sketch Map Quality

by Kateřina Hátlová and Martin Hanus

ISPRS Int. J. Geo-Inf. 2020, 9(4), 271; https://doi.org/10.3390/ijgi9040271 - 20 Apr 2020

Cited by 13 | Viewed by 5213

Abstract

Spatial perception is formed throughout our entire lives. Its quality depends on our individual differences and the characteristics of the environment. A sketch map is one way of visualising an individual’s spatial perception. It can be evaluated like a real map, in terms [...] Read more.

Spatial perception is formed throughout our entire lives. Its quality depends on our individual differences and the characteristics of the environment. A sketch map is one way of visualising an individual’s spatial perception. It can be evaluated like a real map, in terms of its positional accuracy, content frequency and choice of cartographic methods. Moreover, the factors influencing the sketch map and/or its individual parameters can be identified. These factors should be of interest to geographers, cartographers and/or (geography) educators. The aim of this paper is to identify and describe the factors that clearly affect sketch map quality, by conducting a systematic review of 90 empirical studies published since 1960. Results show that most empirical studies focus on individual differences more than on environmental characteristics or information sources, even though the importance of these overlooked factors, especially source map characteristics and geographical education, has been proven in most analysed studies. Therefore, further research is needed in the field of sketch map quality parameters, especially in the use of cartographic methods. This paper could serve as a framework for such research. Full article

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19 pages, 4284 KiB

Open AccessEditor’s ChoiceArticle

Household Level Vulnerability Analysis—Index and Fuzzy Based Methods

by Martina Baučić

ISPRS Int. J. Geo-Inf. 2020, 9(4), 263; https://doi.org/10.3390/ijgi9040263 - 19 Apr 2020

Cited by 6 | Viewed by 2922

Abstract

Coastal vulnerability assessment due to climate change impacts, particularly for sea level rise, has become an essential part of coastal management all over the world. For the planning and implementation of adaptation measures at the household level, large-scale analysis is necessary. The main [...] Read more.

Coastal vulnerability assessment due to climate change impacts, particularly for sea level rise, has become an essential part of coastal management all over the world. For the planning and implementation of adaptation measures at the household level, large-scale analysis is necessary. The main aim of this research is to investigate and propose a simple and viable assessment method that includes three key geospatial parameters: elevation, distance to coastline, and building footprint area. Two methods are proposed—one based on the Index method and another on fuzzy logic. While the former method standardizes the quantitative parameters to unit-less vulnerability sub-indices using functions (avoiding crisp classification) and summarizes them, the latter method turns quantitative parameters into linguistic variables and further implements fuzzy logic. For comparison purposes, a third method is considered: the existing Index method using crisp values for vulnerability sub-indices. All three methods were implemented, and the results show significant differences in their vulnerability assessments. A discussion on the advantages and disadvantages led to the following conclusion: although the fuzzy logic method satisfies almost all the requirements, a less complex method based on functions can be applied and still yields significant improvement. Full article

(This article belongs to the Special Issue GI for Disaster Management)

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22 pages, 5217 KiB

Open AccessEditor’s ChoiceArticle

Comparison of Relief Shading Techniques Applied to Landforms

by Marianna Farmakis-Serebryakova and Lorenz Hurni

ISPRS Int. J. Geo-Inf. 2020, 9(4), 253; https://doi.org/10.3390/ijgi9040253 - 18 Apr 2020

Cited by 15 | Viewed by 4241

Abstract

As relief influences disposition of all the other objects displayed on maps, terrain representation plays one of the key roles in the map creation process. Originally a manual technique, relief shading creates the three-dimensional effect and allows the user to read the terrain [...] Read more.

As relief influences disposition of all the other objects displayed on maps, terrain representation plays one of the key roles in the map creation process. Originally a manual technique, relief shading creates the three-dimensional effect and allows the user to read the terrain in an intuitive way. With the advent of digital elevation models (DEMs) analytical relief shading came into a wider use, since it is faster, requires less effort, and delivers reproducible results. In contrast to manual relief shading, however, it often lacks clarity when representing heterogeneous landscapes with diverse landforms. The aim of this work is to evaluate analytical hillshading methods against a set of landforms within an online survey. The responses revealed that the clear sky model performs best applied to most of the landforms included in the survey, in particular all the mountain and valley types. Cluster shading proved to work well for the mountainous and hilly areas but less so in the depiction of valleys. Texture shading and the multidirectional, oblique-weighted (MDOW) method deliver too much detail for most of the landforms presented. Glaciers were depicted in the best way using the aspect tool. For alluvial fans, the standard relief shading with custom lighting direction proved to work best compared to the other methods. Full article

(This article belongs to the Special Issue Geovisualization and Map Design)

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20 pages, 6913 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Replication Mechanisms on Selected Database Systems

by Tomáš Pohanka and Vilém Pechanec

ISPRS Int. J. Geo-Inf. 2020, 9(4), 249; https://doi.org/10.3390/ijgi9040249 - 17 Apr 2020

Cited by 6 | Viewed by 3100

Abstract

This paper is focused on comparing database replication over spatial data in PostgreSQL and MySQL. Database replication means solving various problems with overloading a single database server with writing and reading queries. There are many replication mechanisms that are able to handle data [...] Read more.

This paper is focused on comparing database replication over spatial data in PostgreSQL and MySQL. Database replication means solving various problems with overloading a single database server with writing and reading queries. There are many replication mechanisms that are able to handle data differently. Criteria for objective comparisons were set for testing and determining the bottleneck of the replication process. The tests were done over the real national vector spatial datasets, namely, ArcCR500, Data200, Natural Earth and Estimated Pedologic-Ecological Unit. HWMonitor Pro was used to monitor the PostgreSQL database, network and system load. Monyog was used to monitor the MySQL activity (data and SQL queries) in real-time. Both database servers were run on computers with the Microsoft Windows operating system. The results from the provided tests of both replication mechanisms led to a better understanding of these mechanisms and allowed informed decisions for future deployment. Graphs and tables include the statistical data and describe the replication mechanisms in specific situations. PostgreSQL with the Slony extension with asynchronous replication synchronized a batch of changes with a high transfer speed and high server load. MySQL with synchronous replication synchronized every change record with low impact on server performance and network bandwidth. Full article

(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)

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15 pages, 7608 KiB

Open AccessEditor’s ChoiceArticle

A Method for Generating Variable-Scale Maps for Small Displays

by Rong Zhao, Tinghua Ai and Chen Wen

ISPRS Int. J. Geo-Inf. 2020, 9(4), 250; https://doi.org/10.3390/ijgi9040250 - 17 Apr 2020

Cited by 5 | Viewed by 2366

Abstract

With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes [...] Read more.

With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes a novel variable-scale method that can allow users to properly specify the size, shape, and number of the focus area(s). Our method first establishes a hierarchical data structure for representing geographic data and then the client-side can request and represent the information according to only the operational command input by users. Experimental results show that this method can realize the variable-scale representation of real geographic data on a single screen. It can effectively solve the contradiction between a small-screen display and a large quantity of information. Full article

(This article belongs to the Special Issue Geovisualization and Map Design)

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15 pages, 3539 KiB

Open AccessEditor’s ChoiceArticle

Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds

by Krzysztof Bakuła, Magdalena Pilarska, Adam Salach and Zdzisław Kurczyński

ISPRS Int. J. Geo-Inf. 2020, 9(4), 248; https://doi.org/10.3390/ijgi9040248 - 17 Apr 2020

Cited by 13 | Viewed by 2904

Abstract

This paper presents a methodology for levee damage detection based on Unmanned Aerial System (UAS) data. In this experiment, the data were acquired from the UAS platform, which was equipped with a laser scanner and a digital RGB (Red, Green, Blue) camera. Airborne [...] Read more.

This paper presents a methodology for levee damage detection based on Unmanned Aerial System (UAS) data. In this experiment, the data were acquired from the UAS platform, which was equipped with a laser scanner and a digital RGB (Red, Green, Blue) camera. Airborne laser scanning (ALS) point clouds were used for the generation of the Digital Terrain Model (DTM), and images were used to produce the RGB orthophoto. The main aim of the paper was to present a methodology based on ALS and vegetation index from RGB orthophoto which helps in finding potential places of levee failure. Both types of multi-temporal data collected from the UAS platform are applied separately: elevation and optical data. Two DTM models from different time periods were compared: the first one was generated from the ALS point cloud and the second DTM was delivered from the UAS Laser Scanning (ULS) data. Archival and new orthophotos were converted to Green-Red Vegetation Index (GRVI) raster datasets. From the GRVI raster, change detection for unvegetation ground areas was analysed using a dynamically indicated threshold. The result of this approach is the localisation of places, for which the change in height correlates with the appearance of unvegetation ground. This simple, automatic method provides a tool for specialist monitoring of levees, the critical objects protecting against floods. Full article

(This article belongs to the Special Issue Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies)

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20 pages, 7033 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

by Yi **ao, Tinghua Ai, Min Yang and **ang Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(4), 239; https://doi.org/10.3390/ijgi9040239 - 11 Apr 2020

Cited by 8 | Viewed by 4378

Abstract

As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor [...] Read more.

As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor map** has been a rapidly develo** area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives. Full article

(This article belongs to the Special Issue Map Generalization)

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24 pages, 4374 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

General Method for Extending Discrete Global Grid Systems to Three Dimensions

by Benjamin Ulmer, John Hall and Faramarz Samavati

ISPRS Int. J. Geo-Inf. 2020, 9(4), 233; https://doi.org/10.3390/ijgi9040233 - 10 Apr 2020

Cited by 8 | Viewed by 4218

Abstract

Geospatial sensors are generating increasing amounts of three-dimensional (3D) data. While Discrete Global Grid Systems (DGGS) are a useful tool for integrating geospatial data, they provide no native support for 3D data. Several different 3D global grids have been proposed; however, these approaches [...] Read more.

Geospatial sensors are generating increasing amounts of three-dimensional (3D) data. While Discrete Global Grid Systems (DGGS) are a useful tool for integrating geospatial data, they provide no native support for 3D data. Several different 3D global grids have been proposed; however, these approaches are not consistent with state-of-the-art DGGSs. In this paper, we propose a general method that can extend any DGGS to the third dimension to operate as a 3D DGGS. This extension is done carefully to ensure any valid DGGS can be supported, including all refinement factors and non-congruent refinement. We define encoding, decoding, and indexing operations in a way that splits responsibility between the surface DGGS and the 3D component, which allows for easy transference of data between the 2D and 3D versions of a DGGS. As a part of this, we use radial map** functions that serve a similar purpose as polyhedral projection in a conventional DGGS. We validate our method by creating three different 3D DGGSs tailored for three specific use cases. These use cases demonstrate our ability to quickly generate 3D global grids while achieving desired properties such as support for large ranges of altitudes, volume preservation between cells, and custom cell aspect ratio. Full article

(This article belongs to the Special Issue Global Grid Systems)

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18 pages, 6923 KiB

Open AccessEditor’s ChoiceArticle

When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning

by Izabela Karsznia and Karolina Sielicka

ISPRS Int. J. Geo-Inf. 2020, 9(4), 230; https://doi.org/10.3390/ijgi9040230 - 9 Apr 2020

Cited by 15 | Viewed by 3379

Abstract

Effective settlements generalization for small-scale maps is a complex and challenging task. Develo** a consistent methodology for generalizing small-scale maps has not gained enough attention, as most of the research conducted so far has concerned large scales. In the study reported here, we [...] Read more.

Effective settlements generalization for small-scale maps is a complex and challenging task. Develo** a consistent methodology for generalizing small-scale maps has not gained enough attention, as most of the research conducted so far has concerned large scales. In the study reported here, we want to fill this gap and explore settlement characteristics, named variables that can be decisive in settlement selection for small-scale maps. We propose 33 variables, both thematic and topological, which may be of importance in the selection process. To find essential variables and assess their weights and correlations, we use machine learning (ML) models, especially decision trees (DT) and decision trees supported by genetic algorithms (DT-GA). With the use of ML models, we automatically classify settlements as selected and omitted. As a result, in each tested case, we achieve automatic settlement selection, an improvement in comparison with the selection based on official national map** agency (NMA) guidelines and closer to the results obtained in manual map generalization conducted by experienced cartographers. Full article

(This article belongs to the Special Issue Map Generalization)

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14 pages, 2765 KiB

Open AccessEditor’s ChoiceArticle

GIS-Based Statistical Analysis of Detecting Fear of Crime with Digital Sketch Maps: A Hungarian Multicity Study

by Ákos Jakobi and Andrea Pődör

ISPRS Int. J. Geo-Inf. 2020, 9(4), 229; https://doi.org/10.3390/ijgi9040229 - 9 Apr 2020

Cited by 18 | Viewed by 5280

Abstract

This study evaluates fear of crime perception and official crime statistics in a spatial context, by applying digital sketch maps and statistical GIS methods. The study aims to determine explanatory motives of fear of crime by comparing results of selected large, medium and [...] Read more.

This study evaluates fear of crime perception and official crime statistics in a spatial context, by applying digital sketch maps and statistical GIS methods. The study aims to determine explanatory motives of fear of crime by comparing results of selected large, medium and small sized Hungarian cities. Fear of crime information of residents were collected by using a web application, which gave the possibility to mark regions on a map, where respondents have a sense of safety or feel fear. These digital sketch maps were processed by GIS tools, and were converted to grid data, in order to calculate comparable explanatory variables for fear of crime analysis. The grid-based normalised model reflected some similarities and differences between the observed cities. According to the outcomes, examples were found both in coincidences and opposite correlations of crime statistics and perception of unsafe places, highlighting the importance of locality in fear of crime research. Additionally, the results mirrored that the size of the city or the respondent’s sex does not significantly influence the overall judgment of places, rather the absolute number of safe markings and the local number of registered crime events could affect local results. Full article

(This article belongs to the Special Issue Urban Crime Map** and Analysis Using GIS)

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13 pages, 1406 KiB

Open AccessEditor’s ChoiceArticle

The Effects of Length and Orientation on Numerical Representation in Flow Maps

by Yun Lin, Chengqi Xue, Yafeng Niu, **aozhou Zhou and Yanfei Zhu

ISPRS Int. J. Geo-Inf. 2020, 9(4), 219; https://doi.org/10.3390/ijgi9040219 - 6 Apr 2020

Cited by 3 | Viewed by 2280

Abstract

Flow maps are a common type of geographic information visualization in which lines that symbolize flow are typically varied in width to represent differences in the magnitude of the flow. An accurate perception of thickness is critical to numerical representation in flow maps. [...] Read more.

Flow maps are a common type of geographic information visualization in which lines that symbolize flow are typically varied in width to represent differences in the magnitude of the flow. An accurate perception of thickness is critical to numerical representation in flow maps. Previous studies have identified some of the factors, such as horizontal–vertical visual illusions and color size effects, that affect the perceived size of objects. However, the question of whether multiple visual variables that encode flow lines, such as length, orientation, and shape, interfere with their perceived thicknesses, remains unanswered. In this study, we performed a user study to determine the effect of length and orientation on thickness perception. The result indicates that the horizontal orientation is perceived to be thicker than the vertical orientation, and a short length is perceived to be thicker than a long length. Furthermore, we report and discuss other results (e.g., on adjustment direction) that are consistent with previous work. Although this study constitutes basic research, accumulating evidence on thickness perception is essential to this field of science. This study may contribute to our understanding of the factors that influence the perception of the thickness of lines on a flow map. We provide some concrete guidelines for the design of flow maps that may be beneficial to map designers. Full article

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19 pages, 3465 KiB

Open AccessEditor’s ChoiceArticle

The Influence of Map Projections on People’s Global-Scale Cognitive Map: A Worldwide Study

by Lieselot Lapon, Kristien Ooms and Philippe De Maeyer

ISPRS Int. J. Geo-Inf. 2020, 9(4), 196; https://doi.org/10.3390/ijgi9040196 - 26 Mar 2020

Cited by 9 | Viewed by 12384

Abstract

Map projections are required to represent the globe on a flat surface, which always results in distorted representations of the globe. Accordingly, the world maps we observe in daily life contexts, such as on news sites, in news bulletins, on social media, in [...] Read more.

Map projections are required to represent the globe on a flat surface, which always results in distorted representations of the globe. Accordingly, the world maps we observe in daily life contexts, such as on news sites, in news bulletins, on social media, in educational textbooks or atlases, are distorted images of the world. The question raises if regular contact with those representations of the world deforms people’s global-scale cognitive map. To analyze people’s global-scale cognitive map and if it is influenced by map projections, a short playful test was developed that allowed participants to estimate the real land area of certain regions, countries, and continents. More than 130,000 people worldwide participated. This worldwide dataset was used to perform statistical analyses in order to obtain information on the extent that map projections influence the accuracy of people’s global-scale cognitive map. The results indicate that the accuracy differs with the map projection but not to the extent that one’s global-scale cognitive map is a reflection of a particular map projection. Full article

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20 pages, 3978 KiB

Open AccessEditor’s ChoiceArticle

Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO

by Levente Juhász, Tessio Novack, Hartwig H. Hochmair and Sen Qiao

ISPRS Int. J. Geo-Inf. 2020, 9(4), 197; https://doi.org/10.3390/ijgi9040197 - 26 Mar 2020

Cited by 24 | Viewed by 11723

Abstract

User-generated map data is increasingly used by the technology industry for background map**, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As [...] Read more.

User-generated map data is increasingly used by the technology industry for background map**, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As a result of OSM’s increased popularity, the worldwide audience that uses OSM through external applications is directly exposed to malicious edits which represent cartographic vandalism. Multiple reports of obscene and anti-semitic vandalism in OSM have surfaced in popular media over the years. These negative news related to cartographic vandalism undermine the credibility of collaboratively generated maps. Similarly, commercial map providers (e.g., Google Maps and Waze) are also prone to carto-vandalism through their crowdsourcing mechanism that they may use to keep their map products up-to-date. Using PGO as an example, this research analyzes harmful edits in OSM that originate from PGO players. More specifically, this paper analyzes the spatial, temporal and semantic characteristics of PGO carto-vandalism and discusses how the map** community handles it. Our findings indicate that most harmful edits are quickly discovered and that the community becomes faster at detecting and fixing these harmful edits over time. Gaming related carto-vandalism in OSM was found to be a short-term, sporadic activity by individuals, whereas the task of fixing vandalism is persistently pursued by a dedicated user group within the OSM community. The characteristics of carto-vandalism identified in this research can be used to improve vandalism detection systems in the future. Full article

(This article belongs to the Special Issue OpenStreetMap as A Multi-Disciplinary Nexus: Perspectives, Practices and Procedures)

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16 pages, 16481 KiB

Open AccessEditor’s ChoiceArticle

Quantification Method for the Uncertainty of Matching Point Distribution on 3D Reconstruction

by Yuxia Bian, Xuejun Liu, Meizhen Wang, Hongji Liu, Shuhong Fang and Liang Yu

ISPRS Int. J. Geo-Inf. 2020, 9(4), 187; https://doi.org/10.3390/ijgi9040187 - 25 Mar 2020

Cited by 5 | Viewed by 2412

Abstract

Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching [...] Read more.

Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching points. This study mainly focuses on the uncertainty of matching point distribution. First, horizontal dilution of precision (HDOP) is used to quantify the feature point distribution in the overlap** region of multiple images. Then, the quantization method is constructed.

\bar{H D O P^{*}}

, the average of

2 \times \arctan (H D O P \times \sqrt{\frac{n}{5}} - 1) / π

on all images, is utilized to measure the uncertainty of matching point distribution on 3D reconstruction. Finally, simulated and real scene experiments were performed to describe and verify the rationality of the proposed method. We found that the relationship between

\bar{H D O P^{*}}

and the matching point distribution in this study was consistent with that between matching point distribution and 3D reconstruction. Consequently, it may be a feasible method to predict the quality of 3D reconstruction by calculating the uncertainty of matching point distribution. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)

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18 pages, 5041 KiB

Open AccessEditor’s ChoiceArticle

Recent Sea Level Change in the Black Sea from Satellite Altimetry and Tide Gauge Observations

by Nevin Betül Avşar and Şenol Hakan Kutoğlu

ISPRS Int. J. Geo-Inf. 2020, 9(3), 185; https://doi.org/10.3390/ijgi9030185 - 24 Mar 2020

Cited by 13 | Viewed by 5979

Abstract

Global mean sea level has been rising at an increasing rate, especially since the early 19th century in response to ocean thermal expansion and ice sheet melting. The possible consequences of sea level rise pose a significant threat to coastal cities, inhabitants, infrastructure, [...] Read more.

Global mean sea level has been rising at an increasing rate, especially since the early 19th century in response to ocean thermal expansion and ice sheet melting. The possible consequences of sea level rise pose a significant threat to coastal cities, inhabitants, infrastructure, wetlands, ecosystems, and beaches. Sea level changes are not geographically uniform. This study focuses on present-day sea level changes in the Black Sea using satellite altimetry and tide gauge data. The multi-mission gridded satellite altimetry data from January 1993 to May 2017 indicated a mean rate of sea level rise of 2.5 ± 0.5 mm/year over the entire Black Sea. However, when considering the dominant cycles of the Black Sea level time series, an apparent (significant) variation was seen until 2014, and the rise in the mean sea level has been estimated at about 3.2 ± 0.6 mm/year. Coastal sea level, which was assessed using the available data from 12 tide gauge stations, has generally risen (except for the Bourgas Station). For instance, from the western coast to the southern coast of the Black Sea, in Constantza, Sevastopol, Tuapse, Batumi, Trabzon, Amasra, Sile, and Igneada, the relative rise was 3.02, 1.56, 2.92, 3.52, 2.33, 3.43, 5.03, and 6.94 mm/year, respectively, for varying periods over 1922–2014. The highest and lowest rises in the mean level of the Black Sea were in Poti (7.01 mm/year) and in Varna (1.53 mm/year), respectively. Measurements from six Global Navigation Satellite System (GNSS) stations, which are very close to the tide gauges, also suggest that there were significant vertical land movements at some tide gauge locations. This study confirmed that according to the obtained average annual phase value of sea level observations, seasonal sea level variations in the Black Sea reach their maximum annual amplitude in May–June. Full article

(This article belongs to the Special Issue GI for Disaster Management)

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19 pages, 7773 KiB

Open AccessEditor’s ChoiceArticle

Development and Application of an Intelligent Modeling Method for Ancient Wooden Architecture

by Yonghui Jiang, Aiqun Li, Linlin **e, Miaole Hou, Ying Qi and Haoyu Liu

ISPRS Int. J. Geo-Inf. 2020, 9(3), 167; https://doi.org/10.3390/ijgi9030167 - 11 Mar 2020

Cited by 14 | Viewed by 5793

Abstract

Building-information-modeling for cultural heritage (HBIM), which is established using surveying data, can be used to conserve architectural heritage. The development of an HBIM model for ancient wooden architecture (AWA) structures requires interdisciplinary integration. A parametric model for the main components that intelligently integrates [...] Read more.

Building-information-modeling for cultural heritage (HBIM), which is established using surveying data, can be used to conserve architectural heritage. The development of an HBIM model for ancient wooden architecture (AWA) structures requires interdisciplinary integration. A parametric model for the main components that intelligently integrates the historical knowledge, as well as an intelligent modeling method for these components, are two critical issues required to bridge the existing gap and improve the application of HBIM. Taking an AWA structure constructed during the Liao and Song Dynasties as an example, the parametric model for the typical components, with emphasis on commonality and characteristics, were first proposed. Subsequently, an intelligent automated modeling method was developed and programmed using Dynamo, which can intelligently identify the component type and determine the invisible dimensions. A complicated dou-gong was successfully established with surveying data using the proposed method within five minutes, thereby validating the reliability and efficiency of this method. Furthermore, the proposed method was used to establish the HBIM model of Yingxian Wood Pagoda, which is the oldest and tallest AWA structure in China with a height of 65.88 m. The research findings will provide an essential reference for the conservation of wooden architectural heritage structures. Full article

(This article belongs to the Special Issue BIM for Cultural Heritage (HBIM))

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31 pages, 3300 KiB

Open AccessEditor’s ChoiceReview

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

by Margarita Kokla and Eric Guilbert

ISPRS Int. J. Geo-Inf. 2020, 9(3), 146; https://doi.org/10.3390/ijgi9030146 - 1 Mar 2020

Cited by 27 | Viewed by 5903

Abstract

The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The [...] Read more.

The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research. Full article

(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)

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15 pages, 24263 KiB

Open AccessEditor’s ChoiceArticle

Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Map** Using FOSS and Open Data

by Christoph Hütt, Guido Waldhoff and Georg Bareth

ISPRS Int. J. Geo-Inf. 2020, 9(2), 120; https://doi.org/10.3390/ijgi9020120 - 21 Feb 2020

Cited by 15 | Viewed by 4467

Abstract

Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably [...] Read more.

Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during image acquisition. However, processing of SAR is more complicated and the sensors have development potential. Dealing with such a complexity, current studies should aim to be reproducible, open, and built upon free and open-source software (FOSS). Thereby, the data can be reused to develop and validate new algorithms or improve the ones already in use. This paper presents a case study of crop classification from microwave remote sensing, relying on open data and open software only. We used 70 multitemporal microwave remote sensing images from the Sentinel-1 satellite. A high-resolution, high-precision digital elevation model (DEM) assisted the preprocessing. The multi-data approach (MDA) was used as a framework enabling to demonstrate the benefits of including external cadastral data. It was used to identify the agricultural area prior to the classification and to create land use/land cover (LULC) maps which also include the annually changing crop types that are usually missing in official geodata. All the software used in this study is open-source, such as the Sentinel Application Toolbox (SNAP), Orfeo Toolbox, R, and QGIS. The produced geodata, all input data, and several intermediate data are openly shared in a research database. Validation using an independent validation dataset showed a high overall accuracy of 96.7% with differentiation into 11 different crop-classes. Full article

(This article belongs to the Special Issue Free and Open Source Software and Tools for Environmental Applications)

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26 pages, 12480 KiB

Open AccessEditor’s ChoiceArticle

A Harmonized Data Model for Noise Simulation in the EU

by Kavisha Kumar, Hugo Ledoux, Richard Schmidt, Theo Verheij and Jantien Stoter

ISPRS Int. J. Geo-Inf. 2020, 9(2), 121; https://doi.org/10.3390/ijgi9020121 - 21 Feb 2020

Cited by 11 | Viewed by 4023

Abstract

This paper presents our implementation of a harmonized data model for noise simulations in the European Union (EU). Different noise assessment methods are used by different EU member states (MS) for estimating noise at local, regional, and national scales. These methods, along with [...] Read more.

This paper presents our implementation of a harmonized data model for noise simulations in the European Union (EU). Different noise assessment methods are used by different EU member states (MS) for estimating noise at local, regional, and national scales. These methods, along with the input data extracted from the national registers and databases, as well as other open and/or commercially available data, differ in several aspects and it is difficult to obtain comparable results across the EU. To address this issue, a common framework for noise assessment methods (CNOSSOS-EU) was developed by the European Commission’s (EC) Joint Research Centre (JRC). However, apart from the software implementations for CNOSSOS, very little has been done for the practical guidelines outlining the specifications for the required input data, metadata, and the schema design to test the real-world situations with CNOSSOS. We describe our approach for modeling input and output data for noise simulations and also generate a real world dataset of an area in the Netherlands based on our data model for simulating urban noise using CNOSSOS. Full article

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19 pages, 3101 KiB

Open AccessEditor’s ChoiceArticle

A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data

by Xucai Zhang, Yeran Sun, Anyao Zheng and Yu Wang

ISPRS Int. J. Geo-Inf. 2020, 9(2), 124; https://doi.org/10.3390/ijgi9020124 - 21 Feb 2020

Cited by 30 | Viewed by 3979

Abstract

The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shop**, eating, etc. The [...] Read more.

The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shop**, eating, etc. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing technology. To address this issue, we used a new social sensing approach to classify land use according to human mobility and activity patterns. Previous studies used other social sensing approaches to predict land use types at the parcel or the area level, whilst fine-grained point-of-interest (POI)-level land use data are likely to more useful in urban planning. To abridge this research gap, we proposed a new social sensing approach dedicated to classifying land use at a finer scale (i.e., POI-level or building level) according to human mobility and activity patterns reflected by location-based social network (LBSN) data. Specifically, we firstly investigated spatial and temporal patterns of human mobility and activity behavior using check-in data from a popular Chinese LBSN named Sina Weibo and subsequently applied those patterns to predicting the category of POI to refine urban land use classification in Guangzhou, China. In this study, we applied three classification methods (i.e., naive Bayes, support vector machines, and random forest) to recognize category of a certain POI by spatial and temporal features of human mobility and activity behavior as well as POIs’ locational characteristics. Random forest outperformed the other two methods and obtained an overall accuracy of 72.21%. Apart from that, we compared the results of the different rules in filtering check-in samples. The comparison results show that a reasonable rule to select samples is essential for predicting the category of POI. Moreover, the approach proposed in this study can be potentially applied to identifying functions of buildings according to visitors’ mobility and activity behavior and buildings’ locational characteristics. Full article

(This article belongs to the Special Issue Convergence of GIS and Social Media)

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14 pages, 5832 KiB

Open AccessEditor’s ChoiceArticle

Complexity Level of People Gathering Presentation on an Animated Map—Objective Effectiveness Versus Expert Opinion

by Beata Medyńska-Gulij, Łukasz Wielebski, Łukasz Halik and Maciej Smaczyński

ISPRS Int. J. Geo-Inf. 2020, 9(2), 117; https://doi.org/10.3390/ijgi9020117 - 20 Feb 2020

Cited by 17 | Viewed by 2858

Abstract

The aim of the following study was to present three alternative methods of visualization on animated maps illustrating the movement of people gathered at an open-air event recorded on photographs taken by a drone. The effectiveness of an orthorectified low-level aerial image (a [...] Read more.

The aim of the following study was to present three alternative methods of visualization on animated maps illustrating the movement of people gathered at an open-air event recorded on photographs taken by a drone. The effectiveness of an orthorectified low-level aerial image (a so-called orthophoto), a dot distribution map, and a buffer map was tested in an experiment featuring experts, and key significance was attached to the juxtaposition of objective responses with subjective opinions. The results of the study enabled its authors to draw conclusions regarding the importance of visualizing topographic references (stable objects) and people (mobile objects) and the usefulness of the particular elements of animated maps for their analysis and interpretation. Full article

(This article belongs to the Special Issue Multimedia Cartography)

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17 pages, 5682 KiB

Open AccessEditor’s ChoiceArticle

Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images

by Tessio Novack, Leonard Vorbeck, Heinrich Lorei and Alexander Zipf

ISPRS Int. J. Geo-Inf. 2020, 9(2), 98; https://doi.org/10.3390/ijgi9020098 - 4 Feb 2020

Cited by 17 | Viewed by 6104

Abstract

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues [...] Read more.

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In this paper, we present an approach towards detecting building facades with graffiti artwork based on the automatic interpretation of images from Google Street View (GSV). It starts with the identification of geo-tagged photos of graffiti artwork posted on the photo sharing media Flickr. GSV images are then extracted from the surroundings of these photos and interpreted by a customized, i.e., transfer learned, convolutional neural network. The compass heading of the GSV images classified as containing graffiti artwork and the possible positions of their acquisition are considered for scoring building facades according to their potential of containing the artwork observable in the GSV images. More than 36,000 GSV images and 5000 facades from buildings represented in OpenStreetMap were processed and evaluated. Precision and recall rates were computed for different facade score thresholds. False-positive errors are caused mostly by advertisements and scribblings on the building facades as well as by movable objects containing graffiti artwork and obstructing the facades. However, considering higher scores as threshold for detecting facades containing graffiti leads to the perfect precision rate. Our approach can be applied for identifying previously unmapped graffiti artwork and for assisting map contributors interested in the topic. Furthermore, researchers interested on the spatial correlations between graffiti artwork and socio-economic factors can profit from our open-access code and results. Full article

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21 pages, 7553 KiB

Open AccessEditor’s ChoiceArticle

Application of AHP to Road Selection

by Yuan Han, Zhonghui Wang, **aomin Lu and Bowei Hu

ISPRS Int. J. Geo-Inf. 2020, 9(2), 86; https://doi.org/10.3390/ijgi9020086 - 1 Feb 2020

Cited by 39 | Viewed by 5244

Abstract

The analytic hierarchy process (AHP), a decision-making method, allows the relative prioritization and assessment of alternatives under multiple criteria contexts. This method is also well suited for road selection. The method for road selection based on AHP involves four steps: (i) Points of [...] Read more.

The analytic hierarchy process (AHP), a decision-making method, allows the relative prioritization and assessment of alternatives under multiple criteria contexts. This method is also well suited for road selection. The method for road selection based on AHP involves four steps: (i) Points of Interest (POIs), the point-like representations of the facilities and habitations in maps, are used to describe and build the contextual characteristic indicator of roads; (ii) form an AHP model of roads with topological, geometrical, and contextual characteristic indicators to calculate their importance; (iii) select roads based on their importance and the adaptive thresholds of their constituent density partitions; and (iv) maintain the global connectivity of the selected network. The generalized result at a scale of 1:200,000 by AHP-based methods better preserved the structure of the original road network compared with other methods. Our method also gives preference to roads with relatively significant contextual characteristics without interfering with the structure of the road network. Furthermore, the result of our method largely agrees with that of the manual method. Full article

(This article belongs to the Special Issue Map Generalization)

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18 pages, 2424 KiB

Open AccessEditor’s ChoiceArticle

Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data

by David S. Lamb, Joni Downs and Steven Reader

ISPRS Int. J. Geo-Inf. 2020, 9(2), 85; https://doi.org/10.3390/ijgi9020085 - 1 Feb 2020

Cited by 13 | Viewed by 4362

Abstract

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail [...] Read more.

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify grou**s in movement data. The approach, i.e., space–time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animal’s home range. Full article

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30 pages, 5804 KiB

Open AccessEditor’s ChoiceReview

Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future

by Serena Coetzee, Ivana Ivánová, Helena Mitasova and Maria Antonia Brovelli

ISPRS Int. J. Geo-Inf. 2020, 9(2), 90; https://doi.org/10.3390/ijgi9020090 - 1 Feb 2020

Cited by 82 | Viewed by 13648

Abstract

All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of [...] Read more.

All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of open source geospatial software, focusing on the Open Source Geospatial Foundation (OSGeo) software ecosystem and its communities, as well as three kinds of open geospatial data (collaboratively contributed, authoritative and scientific). The current state confirms that openness has changed the way in which geospatial data are collected, processed, analyzed, and visualized. A perspective on future developments, informed by responses from professionals in key organizations in the global geospatial community, suggests that open source geospatial software and open geospatial data are likely to have an even more profound impact in the future. Full article

(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)

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12 pages, 1695 KiB

Open AccessEditor’s ChoiceArticle

Linguistic Landscapes on Street-Level Images

by Seong-Yun Hong

ISPRS Int. J. Geo-Inf. 2020, 9(1), 57; https://doi.org/10.3390/ijgi9010057 - 20 Jan 2020

Cited by 14 | Viewed by 7539

Abstract

Linguistic landscape research focuses on relationships between written languages in public spaces and the sociodemographic structure of a city. While a great deal of work has been done on the evaluation of linguistic landscapes in different cities, most of the studies are based [...] Read more.

Linguistic landscape research focuses on relationships between written languages in public spaces and the sociodemographic structure of a city. While a great deal of work has been done on the evaluation of linguistic landscapes in different cities, most of the studies are based on ad-hoc interpretation of data collected from fieldwork. The purpose of this paper is to develop a new methodological framework that combines computer vision and machine learning techniques for assessing the diversity of languages from street-level images. As demonstrated with an analysis of a small Chinese community in Seoul, South Korea, the proposed approach can reveal the spatiotemporal pattern of linguistic variations effectively and provide insights into the demographic composition as well as social changes in the neighborhood. Although the method presented in this work is at a conceptual stage, it has the potential to open new opportunities to conduct linguistic landscape research at a large scale and in a reproducible manner. It is also capable of yielding a more objective description of a linguistic landscape than arbitrary classification and interpretation of on-site observations. The proposed approach can be a new direction for the study of linguistic landscapes that builds upon urban analytics methodology, and it will help both geographers and sociolinguists explore and understand our society. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)

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28 pages, 9450 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Spaces in Spatial Science and Urban Applications—State of the Art Review

by Sisi Zlatanova, **** Yan, Yi**g Wang, Abdoulaye Diakité, Umit Isikdag, George Sithole and Jack Barton

ISPRS Int. J. Geo-Inf. 2020, 9(1), 58; https://doi.org/10.3390/ijgi9010058 - 20 Jan 2020

Cited by 36 | Viewed by 10373

Abstract

In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and [...] Read more.

In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and these variations have to be harmonised. For example, space may have diverse definitions and classification, the same environment may be abstracted/modelled by contradicting notions of space, which can lead to inconsistencies and misunderstandings. In this paper, we seek to investigate and document the state-of-the-art in the research of “space” regarding its definition, classification, modelling and utilization (2D/3D) in spatial sciences and urban applications. We focus on positioning, navigation, building micro-climate and thermal comfort, landscape, urban planning and design, urban heat island, interior design and planning, transportation and intelligent space. We review 147 research papers, technical reports and on-line resources. We compare the presented space concepts with respect to five criteria—classification, boundary, modelling components, use of standards and granularity. The review inventory is intended for both scientists and professionals in the spatial industry, such as companies, national map** agencies and governments, and aim to provide a reference to better understand and employ the “space” while working across disciplines. Full article

(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)

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21 pages, 5516 KiB

Open AccessEditor’s ChoiceArticle

Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model

by Daijun Zhang, **aoqi Zhang, Yanqiao Zheng, **nyue Ye, Shengwen Li and Qiwen Dai

ISPRS Int. J. Geo-Inf. 2020, 9(1), 56; https://doi.org/10.3390/ijgi9010056 - 19 Jan 2020

Cited by 6 | Viewed by 3090

Abstract

This study analyzed the spillovers among intra-urban housing submarkets in Bei**g, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is [...] Read more.

This study analyzed the spillovers among intra-urban housing submarkets in Bei**g, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series models. Instead, we formulated the spillover effect as a Markov chain procedure. The constrained clustering technique was applied to identify the submarkets as the hidden states of Markov chain and estimate the transition matrix. Using a day-by-day transaction dataset of second-hand apartments in Bei**g during 2011–2017, we detected 16 submarkets/regions and the spillover effect among these regions. The highest transition probability appeared in the overlapped region of urban core and Tongzhou district. This observation reflects the impact of urban planning proposal initiated since early 2012. In addition to the policy consequences, we analyzed a variety of spillover “types” through regression analysis. The latter showed that the “ripple” form of spillover is not dominant at the intra-urban level. Other types, such as the spillover due to the existence of price depressed regions, play major roles. This observation reveals the complexity of intra-urban spillover dynamics and its distinct driving-force compared to the inter-urban spillover. Full article

(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)

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22 pages, 30142 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Augmented Reality-Based Building Diagnostics Using Third Person Perspective

by Fei Liu, Torsten Jonsson and Stefan Seipel

ISPRS Int. J. Geo-Inf. 2020, 9(1), 53; https://doi.org/10.3390/ijgi9010053 - 16 Jan 2020

Cited by 11 | Viewed by 3775

Abstract

Comprehensive user evaluations of outdoor augmented reality (AR) applications in the architecture, engineering, construction and facilities management (AEC/FM) industry are rarely reported in the literature. This paper presents an AR prototype system for infrared thermographic façade inspection and its evaluation. The system employs [...] Read more.

Comprehensive user evaluations of outdoor augmented reality (AR) applications in the architecture, engineering, construction and facilities management (AEC/FM) industry are rarely reported in the literature. This paper presents an AR prototype system for infrared thermographic façade inspection and its evaluation. The system employs markerless tracking based on image registration using natural features and a third person perspective (TPP) augmented view displayed on a hand-held smart device. We focus on evaluating the system in user experiments with the task of designating positions of heat spots on an actual façade as if acquired through thermographic inspection. User and system performance were both assessed with respect to target designation errors. The main findings of this study show that positioning accuracy using this system is adequate for objects of the size of one decimeter. After ruling out the system inherent errors, which mainly stem from our application-specific image registration procedure, we find that errors due to a human’s limited visual-motoric and cognitive performance, which have a more general implication for using TPP AR for target designation, are only a few centimeters. Full article

(This article belongs to the Special Issue Advances in Augmented Reality and Virtual Reality for Smart Cities)

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22 pages, 10420 KiB

Open AccessEditor’s ChoiceArticle

A Spatial Analytics Framework to Investigate Electric Power-Failure Events and Their Causes

by Vivian Sultan and Brian Hilton

ISPRS Int. J. Geo-Inf. 2020, 9(1), 54; https://doi.org/10.3390/ijgi9010054 - 16 Jan 2020

Cited by 14 | Viewed by 5655

Abstract

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of [...] Read more.

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of the causes of recent power outages. This paper explores geographic information systems (GIS) and a spatially enhanced predictive power-outage model to address: How may spatial analytics enhance our understanding of power outages? To answer this research question, we developed a spatial analysis framework that utilities can use to investigate power-failure events and their causes. Analysis revealed areas of statistically significant power outages due to multiple causes. This study’s GIS model can help to advance smart-grid reliability by, for example, elucidating power-failure root causes, defining a data-responsive blackout solution, or implementing a continuous monitoring and management solution. We unveil a novel use of spatial analytics to enhance power-outage understanding. Future work may involve connecting to virtually any type of streaming-data feed and transforming GIS applications into frontline decision applications, showing power-outage incidents as they occur. GIS can be a major resource for electronic-inspection systems to lower the duration of customer outages, improve crew response time, as well as reduce labor and overtime costs. Full article

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23 pages, 10245 KiB

Open AccessEditor’s ChoiceArticle

An Efficient Staged Evacuation Planning Algorithm Applied to Multi-Exit Buildings

by Litao Han, Huan Guo, Haisi Zhang, Qiaoli Kong, Aiguo Zhang and Cheng Gong

ISPRS Int. J. Geo-Inf. 2020, 9(1), 46; https://doi.org/10.3390/ijgi9010046 - 15 Jan 2020

Cited by 17 | Viewed by 4350

Abstract

When the occupant density of buildings is large enough, evacuees are prone to congestion during emergency evacuation, which leads to the extension of the overall escape time. Especially for multi-exit buildings, it’s a challenging problem to afford an effective evacuation plan. In this [...] Read more.

When the occupant density of buildings is large enough, evacuees are prone to congestion during emergency evacuation, which leads to the extension of the overall escape time. Especially for multi-exit buildings, it’s a challenging problem to afford an effective evacuation plan. In this paper, a novel evacuation planning algorithm applied to multi-exit buildings is proposed, which is based on an indoor route network model. Firstly, evacuees are grouped by their location proximity, then all groups are approximately equally classified into several evacuation zones, each of which has only one safe exit. After that, all evacuation groups in the same zone are sorted by their shortest path length, then the time window of each evacuation group occupying the safe exit is calculated in turn. In the case of congestion at the safe exit, the departure time of each evacuation group is delayed in its arrival order. The objectives of the proposed algorithm include minimizing the total evacuation time of all evacuees, the travel time of each evacuee, avoiding traffic congestion, balancing traffic loads among different exits, and achieving high computational efficiency. Case studies are conducted to examine the performance of our algorithm. The influences of group number, group size, evacuation speed on the total evacuation time are discussed on a single-exit network, and that of partitioning methods and evacuation density on the performance and applicability in different congestion levels are also discussed on a multi-exit network. Results demonstrate that our algorithm has a higher efficiency and performs better for evacuations with a large occupant density. Full article

(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)

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18 pages, 15585 KiB

Open AccessEditor’s ChoiceArticle

Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

by Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng and Bo Huang

ISPRS Int. J. Geo-Inf. 2020, 9(1), 40; https://doi.org/10.3390/ijgi9010040 - 14 Jan 2020

Cited by 19 | Viewed by 4697

Abstract

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by [...] Read more.

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed. Full article

(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)

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21 pages, 2896 KiB

Open AccessEditor’s ChoiceArticle

The Role of Spatial Context Information in the Generalization of Geographic Information: Using Reducts to Indicate Relevant Attributes

by Anna Fiedukowicz

ISPRS Int. J. Geo-Inf. 2020, 9(1), 37; https://doi.org/10.3390/ijgi9010037 - 10 Jan 2020

Cited by 4 | Viewed by 3468

Abstract

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. [...] Read more.

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. However, the question remains which information is crucial to the decisions regarding the generalization (in this paper: selection) of objects. The article presents and compares the usability of three methods based on rough set theories (rough set theory, dominance-based rough set theory, fuzzy rough set theory) that facilitate the designation of the attributes relevant to a decision. The methods are using different types (levels of measurements) of attributes. The author determines reducts and their cores (common elements) that show the relevance of attributes stemming from the spatial context. The fuzzy rough set theory method proved the least useful, whereas the rough set theory and dominance-based rough set theory methods seem to be recommendable (depending on the governing level of measurement). Full article

(This article belongs to the Special Issue Map Generalization)

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16 pages, 4771 KiB

Open AccessEditor’s ChoiceArticle

Identification of Salt Deposits on Seismic Images Using Deep Learning Method for Semantic Segmentation

by Aleksandar Milosavljević

ISPRS Int. J. Geo-Inf. 2020, 9(1), 24; https://doi.org/10.3390/ijgi9010024 - 1 Jan 2020

Cited by 19 | Viewed by 6989

Abstract

Several areas of Earth that are rich in oil and natural gas also have huge deposits of salt below the surface. Because of this connection, knowing precise locations of large salt deposits is extremely important to companies involved in oil and gas exploration. [...] Read more.

Several areas of Earth that are rich in oil and natural gas also have huge deposits of salt below the surface. Because of this connection, knowing precise locations of large salt deposits is extremely important to companies involved in oil and gas exploration. To locate salt bodies, professional seismic imaging is needed. These images are analyzed by human experts which leads to very subjective and highly variable renderings. To motivate automation and increase the accuracy of this process, TGS-NOPEC Geophysical Company (TGS) has sponsored a Kaggle competition that was held in the second half of 2018. The competition was very popular, gathering 3221 individuals and teams. Data for the competition included a training set of 4000 seismic image patches and corresponding segmentation masks. The test set contained 18,000 seismic image patches used for evaluation (all images are 101 × 101 pixels). Depth information of the sample location was also provided for every seismic image patch. The method presented in this paper is based on the author’s participation and it relies on training a deep convolutional neural network (CNN) for semantic segmentation. The architecture of the proposed network is inspired by the U-Net model in combination with ResNet and DenseNet architectures. To better comprehend the properties of the proposed architecture, a series of experiments were conducted applying standardized approaches using the same training framework. The results showed that the proposed architecture is comparable and, in most cases, better than these segmentation models. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)

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23 pages, 3952 KiB

Open AccessEditor’s ChoiceReview

UAV-Based Structural Damage Map**: A Review

by Norman Kerle, Francesco Nex, Markus Gerke, Diogo Duarte and Anand Vetrivel

ISPRS Int. J. Geo-Inf. 2020, 9(1), 14; https://doi.org/10.3390/ijgi9010014 - 26 Dec 2019

Cited by 125 | Viewed by 11641

Abstract

Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned [...] Read more.

Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned aerial vehicles (UAVs) in recent years has opened up many new opportunities for damage map**, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. This study provides a comprehensive review of how UAV-based damage map** has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed map** strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing map** autonomy, damage map** in interior, GPS-denied environments, the utility of UAVs for infrastructure map** and maintenance, as well as the emergence of UAVs with robotic abilities. Full article

(This article belongs to the Special Issue GI for Disaster Management)

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18 pages, 4964 KiB

Open AccessEditor’s ChoiceArticle

WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

by Mohamed R. Ibrahim, James Haworth and Tao Cheng

ISPRS Int. J. Geo-Inf. 2019, 8(12), 549; https://doi.org/10.3390/ijgi8120549 - 30 Nov 2019

Cited by 45 | Viewed by 6287

Abstract

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance [...] Read more.

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used in practice. What has been achieved to-date are rather sectorial models that address a limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as dawn/dusk, day and night for time detection, glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used but are not limited to autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)

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16 pages, 6122 KiB

Open AccessEditor’s ChoiceArticle

DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran

by Sadra Karimzadeh, Bakhtiar Feizizadeh and Masashi Matsuoka

ISPRS Int. J. Geo-Inf. 2019, 8(12), 537; https://doi.org/10.3390/ijgi8120537 - 27 Nov 2019

Cited by 16 | Viewed by 5715

Abstract

Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, [...] Read more.

Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, we mainly focused on the use of an ASTER 1c digital elevation model (DEM) to produce Vs30 maps throughout Iran using a GIS-based regression analysis of Vs30 measurements at 514 seismic stations. These maps were found to be comparable with those that were previously created from SRTM 30c data. The Vs30 results from ASTER 1c estimated the higher velocities better than those from SRTM 30c. In addition, a combination of ASTER 1c and SRTM 30c amplification maps can be useful for the detection of geological and geomorphological units. We also classified the terrain surface of six seismotectonic regions in Iran into 16 classes, considering three important criteria (slope, convexity and texture) to extract more information about the location and morphological characteristics of the stations. The results show that 98% of the stations are situated in six classes, 30% of which are in class 12, 27% in class 6, 17% in class 9, 16% in class 3, 4% in class 3and the rest of the stations are located in other classes. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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18 pages, 9805 KiB

Open AccessEditor’s ChoiceArticle

A Method of Watershed Delineation for Flat Terrain Using Sentinel-2A Imagery and DEM: A Case Study of the Taihu Basin

by Leilei Li, **tao Yang and ** Wu

ISPRS Int. J. Geo-Inf. 2019, 8(12), 528; https://doi.org/10.3390/ijgi8120528 - 26 Nov 2019

Cited by 20 | Viewed by 7769

Abstract

Accurate watershed delineation is a precondition for runoff and water quality simulation. Traditional digital elevation model (DEM) may not generate realistic drainage networks due to large depressions and subtle elevation differences in local-scale plains. In this study, we propose a new method for [...] Read more.

Accurate watershed delineation is a precondition for runoff and water quality simulation. Traditional digital elevation model (DEM) may not generate realistic drainage networks due to large depressions and subtle elevation differences in local-scale plains. In this study, we propose a new method for solving the problem of watershed delineation, using the Taihu Basin as a case study. Rivers, lakes, and reservoirs were obtained from Sentinel-2A images with the Canny algorithm on Google Earth Engine (GEE), rather than from DEM, to compose the drainage network. Catchments were delineated by modifying the flow direction of rivers, lakes, reservoirs, and overland flow, instead of using DEM values. A watershed was divided into the following three types: Lake, reservoir, and overland catchment. A total of 2291 river segments, seven lakes, eight reservoirs, and 2306 subwatersheds were retained in this study. Compared with results from HydroSHEDS and Arc Hydro, the proposed method retains crisscross structures in the topology and prevented erroneous streamlines in large lakes. High-resolution Sentinel-2A images available on the GEE have relatively greater merits than DEMs for precisely representing drainage networks and catchments, especially in the plains area. Because of the higher accuracy, this method can be used as a new solution for watershed division in the plains area. Full article

(This article belongs to the Special Issue Geo-Spatial Analysis in Hydrology)

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21 pages, 12053 KiB

Open AccessEditor’s ChoiceArticle

Dynamic 3D Simulation of Flood Risk Based on the Integration of Spatio-Temporal GIS and Hydrodynamic Models

by Yongxing Wu, Fei Peng, Yang Peng, **aoyang Kong, Heming Liang and Qi Li

ISPRS Int. J. Geo-Inf. 2019, 8(11), 520; https://doi.org/10.3390/ijgi8110520 - 18 Nov 2019

Cited by 14 | Viewed by 6283

Abstract

Dynamic visual simulation of flood risk is crucial for scientific and intelligent emergency management of flood disasters, in which data quality, availability, visualization, and interoperability are important. Here, a seamless integration of a spatio-temporal Geographic Information System (GIS) with one-dimensional (1D) and two-dimensional [...] Read more.

Dynamic visual simulation of flood risk is crucial for scientific and intelligent emergency management of flood disasters, in which data quality, availability, visualization, and interoperability are important. Here, a seamless integration of a spatio-temporal Geographic Information System (GIS) with one-dimensional (1D) and two-dimensional (2D) hydrodynamic models is achieved for data flow, calculation processes, operation flow, and system functions. Oblique photography-based three-dimensional (3D) modeling technology is used to quickly build a 3D model of the study area (including the hydraulic engineering facilities). A multisource spatio-temporal data platform for dynamically simulating flood risk was built based on the digital earth platform. Using the spatio-temporal computation framework, a dynamic visual simulation and decision support system for flood risk management was developed for the **ashan Reservoir. The integration method proposed here was verified using flood simulation calculations, dynamic visual simulations, and downstream river channel and dam-break flood simulations. The results show that the proposed methods greatly improve the efficiency of flood risk simulation and decision support. The methods and system put forward in this study can be applied to flood risk simulations and practical management. Full article

(This article belongs to the Special Issue Geo-Spatial Analysis in Hydrology)

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21 pages, 4344 KiB

Open AccessEditor’s ChoiceArticle

The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts

by Kangjae Lee and Mei-Po Kwan

ISPRS Int. J. Geo-Inf. 2019, 8(11), 514; https://doi.org/10.3390/ijgi8110514 - 13 Nov 2019

Cited by 15 | Viewed by 4080

Abstract

To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on [...] Read more.

To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals’ dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (CRHTI) Survey, this study addresses the methodological issue of how the associations between environmental contexts and active travel modes (ATMs) as a subset of physical activity vary with GPS-based buffer size. The results indicate that buffer size influences such associations and the significance levels of the seven environmental factors selected as predictors. Further, the findings on the effects of buffer size on such associations and the significance levels are clearly different between the ATMs of walking and biking. Such evidence of the existence of buffer-size effects for multiple environmental factors not only confirms the importance of the uncertain geographic context problem (UGCoP) but provides a resounding cautionary note to all future research on human mobility involving individuals’ GPS trajectories, including studies on physical activity and travel behaviors, especially on the reliable estimation of individual exposures to environmental factors and their health outcomes. Full article

(This article belongs to the Special Issue Geospatial Methods in Social and Behavioral Sciences)

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22 pages, 3954 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Tourism Hotspot Behaviour Based on Geolocated Travel Blog Data: The Case of Qyer

by Michael Kaufmann, Patrick Siegfried, Lukas Huck and Jürg Stettler

ISPRS Int. J. Geo-Inf. 2019, 8(11), 493; https://doi.org/10.3390/ijgi8110493 - 1 Nov 2019

Cited by 14 | Viewed by 5999

Abstract

We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, it is possible to identify and target groups that are possibly interested in alternative destinations [...] Read more.

We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, it is possible to identify and target groups that are possibly interested in alternative destinations to relieve overtourism. A pilot application in a case study of Chinese travel in Switzerland by analysing Qyer travel blog data demonstrates the potential of our method. Accordingly, we contribute four conclusions supported by empirical data. First, our method can enable discovery of plausible geographical distributions of tourist hotspots, which validates the plausibility of the data and its collection. Second, our method discovered statistically significant stochastic dependencies that meaningfully differentiate the observed user base, which demonstrates its value for segmentation. Furthermore, the case study contributes two practical insights for tourism management. Third, Chinese independent travellers, which are the main target group of Qyer, are mainly interested in the discovered travel hotspots, similar to tourists on packaged tours, but also show interest in alternative places. Fourth, the proposed user segmentation revealed two clusters based on users’ social media activity level. For tourism research, users within the second cluster are of interest, which are defined by two segmentation attributes: they blogged about more than just one location, and they have followers. These tourists are significantly more likely to be interested in alternative destinations out of the hotspot axis. Knowing this can help define a target group for marketing activities to promote alternative destinations. Full article

(This article belongs to the Special Issue Convergence of GIS and Social Media)

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18 pages, 3007 KiB

Open AccessEditor’s ChoiceArticle

Strengths of Exaggerated Tsunami-Originated Placenames: Disaster Subculture in Sanriku Coast, Japan

by Yuzuru Isoda, Akio Muranaka, Go Tanibata, Kazumasa Hanaoka, Junzo Ohmura and Akihiro Tsukamoto

ISPRS Int. J. Geo-Inf. 2019, 8(10), 429; https://doi.org/10.3390/ijgi8100429 - 24 Sep 2019

Cited by 4 | Viewed by 3675

Abstract

Disaster-originated placename is a kind of disaster subculture that is used for a practical purpose of identifying a location while reminding the past disaster experience. They are expected to transmit the risks and knowledge of high-risk low-frequency natural hazards, surviving over time and [...] Read more.

Disaster-originated placename is a kind of disaster subculture that is used for a practical purpose of identifying a location while reminding the past disaster experience. They are expected to transmit the risks and knowledge of high-risk low-frequency natural hazards, surviving over time and generations. This paper compares the perceptions to tsunami-originated placenames in local communities having realistic and exaggerated origins in Sanriku Coast, Japan. The reality of tsunami-originated placenames is first assessed by comparing the tsunami run-ups indicated in the origins and that of the tsunami in the Great East Japan Earthquake 2011 using GIS and digital elevation model. Considerable proportions of placenames had exaggerated origins, but the group interviews to local communities revealed that origins indicating unrealistic tsunami run-ups were more believed than that of the more realistic ones. We discuss that accurate hazard information will be discredited if it contradicts to the people’s everyday life and the desire for safety, and even imprecise and ambiguous information can survive if it is embedded to a system of local knowledge that consistently explains the various facts in a local area that requires explanation. Full article

(This article belongs to the Special Issue Historical GIS and Digital Humanities)

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19 pages, 24765 KiB

Open AccessEditor’s ChoiceArticle

Transparent Collision Visualization of Point Clouds Acquired by Laser Scanning

by Weite Li, Kenya Shigeta, Kyoko Hasegawa, Liang Li, Keiji Yano, Motoaki Adachi and Satoshi Tanaka

ISPRS Int. J. Geo-Inf. 2019, 8(9), 425; https://doi.org/10.3390/ijgi8090425 - 19 Sep 2019

Cited by 2 | Viewed by 3104

Abstract

In this paper, we propose a method to visualize large-scale colliding point clouds by highlighting their collision areas, and apply the method to visualization of collision simulation. Our method uses our recent work that achieved precise three-dimensional see-through imaging, i.e., transparent visualization, of [...] Read more.

In this paper, we propose a method to visualize large-scale colliding point clouds by highlighting their collision areas, and apply the method to visualization of collision simulation. Our method uses our recent work that achieved precise three-dimensional see-through imaging, i.e., transparent visualization, of large-scale point clouds that were acquired via laser scanning of three-dimensional objects. We apply the proposed collision visualization method to two applications: (1) The revival of the festival float procession of the Gion Festival, Kyoto city, Japan. The city government plans to revive the original procession route, which is narrow and not used at present. For the revival, it is important to know whether the festival floats would collide with houses, billboards, electric wires, or other objects along the original route. (2) Plant simulations based on laser-scanned datasets of existing and new facilities. The advantageous features of our method are the following: (1) A transparent visualization with a correct depth feel that is helpful to robustly determine the collision areas; (2) the ability to visualize high collision risk areas and real collision areas; and (3) the ability to highlight target visualized areas by increasing the corresponding point densities. Full article

(This article belongs to the Special Issue Historical GIS and Digital Humanities)

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25 pages, 3103 KiB

Open AccessEditor’s ChoiceArticle

Social Media Use in American Counties: Geography and Determinants

by James Pick, Avijit Sarkar and Jessica Rosales

ISPRS Int. J. Geo-Inf. 2019, 8(9), 424; https://doi.org/10.3390/ijgi8090424 - 19 Sep 2019

Cited by 13 | Viewed by 7017

Abstract

This paper analyzes the spatial distribution and socioeconomic determinants of social media utilization in 3109 counties of the United States. A theory of determinants was modified from the spatially aware technology utilization model (SATUM). Socioeconomic factors including demography, economy, education, innovation, and social [...] Read more.

This paper analyzes the spatial distribution and socioeconomic determinants of social media utilization in 3109 counties of the United States. A theory of determinants was modified from the spatially aware technology utilization model (SATUM). Socioeconomic factors including demography, economy, education, innovation, and social capital were posited to influence social media utilization dependent variables. Spatial analysis was conducted including exploratory analysis of geographic distribution and confirmatory screening for spatial randomness. The determinants were identified through ordinary least squares (OLS) regression analysis. Findings for the nation indicate that the major determinants are demographic factors, service occupations, ethnicities, and urban location. Furthermore, analysis was conducted for the U.S. metropolitan, micropolitan, and rural subsamples. We found that Twitter users were more heavily concentrated in southern California and had a strong presence in the Mississippi region, while Facebook users were highly concentrated in Colorado, Utah, and adjacent Rocky Mountain States. Social media usage was lowest in the Great Plains, lower Midwest, and South with the exceptions of Florida and major southern cities such as Atlanta. Measurements of the overall extent of spatial agglomeration were very high. The paper concludes by discussing the policy implications of the study at the county as well as national levels. Full article

(This article belongs to the Special Issue Convergence of GIS and Social Media)

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22 pages, 2973 KiB

Open AccessEditor’s ChoiceArticle

Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective

by Wei Cui, Dongyou Zhang, **n He, Meng Yao, Ziwei Wang, Yuanjie Hao, Jie Li, Weijie Wu, Wenqi Cui and Jiejun Huang

ISPRS Int. J. Geo-Inf. 2019, 8(9), 417; https://doi.org/10.3390/ijgi8090417 - 17 Sep 2019

Cited by 8 | Viewed by 2966

Abstract

Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will [...] Read more.

Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)

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