Deep Learning for Very-High Resolution Land-Cover Map** (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 37
Special Issue Editors
Interests: land cover map**; urban remote sensing; machine learning; deep learning; geoinformation; very high resolution; object-based image analysis; big data; automation; change detection; uncertainty; human geography
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are launching the second Special Issue of Remote Sensing to be released under the title ‘Deep Learning for Very-High Resolution Land-Cover Map**’.
In the field of remote sensing, land-cover map** has always been a popular subject, which is attributable to the fact that land-cover data are essential for a multitude of applications. Due to their detailed resolution, very high-resolution imagery is a privileged tool for studying the Earth’s surface, and a large number of publications on land-cover map** from such data have been published. For a long time, pixel-oriented and object-oriented approaches, using either rule-based classifications or conventional machine learning algorithms, have been well established and used by a majority of the community to produce land-cover maps from very high-resolution Earth observation data. However, since 2015, deep learning techniques from computer vision have made their way into the remote sensing field and have since been successfully applied for various applications. While deep learning approaches can largely outperform conventional machine learning approaches for some tasks, several studies have shown that, instead, conventional machine learning and object-oriented classification approaches still perform better in some contexts.
For this Special Issue, we welcome state-of-the-art research or review papers that are focused on land-cover map** from very high-resolution Earth observation data, using deep learning methods and addressing topics, including (but not limited to) the following: architecture comparison; data fusion; comparison of conventional machine learning and deep learning approaches; deep learning model comprehension; explainable deep learning; weak supervision and semi-supervision; scene classification; semantic segmentation; instance segmentation; change detection; existing land-cover map enrichment.
Dr. Lei Ma
Dr. Claudio Persello
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
- deep learning
- explainable deep learning
- land-cover map**
- geo-information extraction
- very high-resolution images
- data augmentation
- data fusion
- semantic segmentation
- instance segmentation
- change detection
- weak and semi-supervision
