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Algorithms
Special Issues
Machine Learning Algorithms for Sensor Data and Image Understanding
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Machine Learning Algorithms for Sensor Data and Image Understanding

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4106

Special Issue Editors

Dr. Pintelas Panagiotis

E-Mail Website
Guest Editor
Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
Interests: software engineering; AI in education; intelligent systems; decision support systems; machine learning; data mining; knowledge discovery
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis E. Livieris

E-Mail Website1 Website2
Guest Editor
Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
Interests: artificial intelligence; machine learning; neural networks; deep learning; optimization algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many contemporary applications of AI, ML and DM are based on datasets collected by a variety of local or remote sensor devices. These application domains cover a huge area of research, from geospatial applications and earth observation to medical prognosis and treatment, from smart devices to smart homes and cities, and so on. In many occasions, the collected datasets require purification, augmentation and several other types of preprocessing before they are suitable for feeding a ML algorithm. Furthermore, the predicted output of the ML algorithm has to be intelligible and useful for humans; therefore, the ML models have to provide human-comprehensible explanations. Finally, since large amounts of the sensor collected datasets are or can be transformed into 2D or 3D images, image processing methods along with methods from AI, ML and DM reinforced with interpretable and explainable capabilities form a new state-of-the-art research domain which covers an enormous area of practical applications.

Dr. Pintelas Panagiotis
Dr. Ioannis E. Livieris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ML-based algorithms for sensor data
  • explainable machine learning
  • ML applications in the IoT and smart sensors
  • geo-spatial AI/ML applications
  • 2D and 3D image processing
  • remote sensing applications
  • earth observation systems based on ML techniques
  • sensor based geoscience systems and applications
  • feature extraction from sensor data
  • interpretability and explainability issues vs AI/DM methods on sensory data
  • global and local explanation approaches
  • sensor data and image understanding
  • autoencoder models for sensor data processing
  • ML methods for sensing systems
  • AI and DM for smart systems and smart cities
  • surrogate models
  • AI/DM using sensing systems for medical, biomedical and other engineering domains
  • ethical issues in the application of ai/dm methods on sensory data

Published Papers (2 papers)

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22 pages, 26451 KiB  
Article
Map** the Distribution of High-Value Broadleaf Tree Crowns through Unmanned Aerial Vehicle Image Analysis Using Deep Learning
by Nyo Me Htun, Toshiaki Owari, Satoshi Tsuyuki and Takuya Hiroshima
Algorithms 2024, 17(2), 84; https://doi.org/10.3390/a17020084 - 17 Feb 2024
Cited by 1 | Viewed by 1911
Abstract
High-value timber species with economic and ecological importance are usually distributed at very low densities, such that accurate knowledge of the location of these trees within a forest is critical for forest management practices. Recent technological developments integrating unmanned aerial vehicle (UAV) imagery [...] Read more.
High-value timber species with economic and ecological importance are usually distributed at very low densities, such that accurate knowledge of the location of these trees within a forest is critical for forest management practices. Recent technological developments integrating unmanned aerial vehicle (UAV) imagery and deep learning provide an efficient method for map** forest attributes. In this study, we explored the applicability of high-resolution UAV imagery and a deep learning algorithm to predict the distribution of high-value deciduous broadleaf tree crowns of Japanese oak (Quercus crispula) in an uneven-aged mixed forest in Hokkaido, northern Japan. UAV images were collected in September and October 2022 before and after the color change of the leaves of Japanese oak to identify the optimal timing of UAV image collection. RGB information extracted from the UAV images was analyzed using a ResU-Net model (U-Net model with a Residual Network 101 (ResNet101), pre-trained on large ImageNet datasets, as backbone). Our results, confirmed using validation data, showed that reliable F1 scores (>0.80) could be obtained with both UAV datasets. According to the overlay analyses of the segmentation results and all the annotated ground truth data, the best performance was that of the model with the October UAV dataset (F1 score of 0.95). Our case study highlights a potential methodology to offer a transferable approach to the management of high-value timber species in other regions. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Sensor Data and Image Understanding)
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17 pages, 11933 KiB  
Article
Manifold Explorer: Satellite Image Labelling and Clustering Tool with Using Deep Convolutional Autoencoders
by Tulsi Patel, Mark W. Jones and Thomas Redfern
Algorithms 2023, 16(10), 469; https://doi.org/10.3390/a16100469 - 4 Oct 2023
Viewed by 1517
Abstract
We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders to create an efficient feature representation for image tiles [...] Read more.
We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders to create an efficient feature representation for image tiles derived from remote sensing satellite imagery. The proposed methodology consists of two main stages. Firstly, an autoencoder network is utilised to reduce the high-dimensional image tile data into a compact and expressive latentfeature representation. Subsequently, features are further reduced to a two-dimensional embedding space using the manifold learning algorithm Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbour Embedding (t-SNE). This step enables the visualization of the image tile clusters in a 2D plot, providing an intuitive and interactive representation that can be used to aid rapid and geographically distributed image labelling. To facilitate the labelling process, our approach allows users to interact with the 2D visualization and label clusters based on their domain knowledge. In cases where certain classes are not effectively separated, users can re-apply dimension reduction to interactively refine subsets of clusters and achieve better class separation, enabling a comprehensively labelled dataset. We evaluate the proposed approach on real-world remote sensing satellite image datasets and demonstrate its effectiveness in achieving accurate and efficient image tile clustering and labelling. Users actively participate in the labelling process through our interactive approach, leading to enhanced relevance of the labelled data, by allowing domain experts to contribute their expertise and enrich the dataset for improved downstream analysis and applications. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Sensor Data and Image Understanding)
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