Special Issue "Machine Learning Using High-Resolution Remote Sensing Datasets"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Katharine M. Johnson
Website
Guest Editor
North Carolina Institute for Climate Studies, North Carolina State University, USA
Interests: human-environment land use dynamics; geographic information science; lidar.
Dr. Yuhan Rao
Website
Guest Editor
North Carolina Institute for Climate Studies, North Carolina State University, USA
Interests: climate change; machine learning; environmental remote sensing.
Dr. Jaime Zabalza
Website
Guest Editor
University of Strathclyde, Glasgow, UK
Interests: signal and image processing; hyperspectral imaging; remote sensing; data mining; machine learning; artificial intelligence.
Special Issues and Collections in MDPI journals
Prof. Dr. Giuseppe Modica
Website SciProfiles
Guest Editor
Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Località Feo di Vito, I-89122 Reggio Calabria, Italy
Interests: land cover and land use change dynamics; satellite and UAV remote sensing; landscape analysis and interpretation; remote sensing of vegetation; geographic object-based image analysis; machine learning.
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data have become higher resolution and more accessible in recent years. The rapid developments in machine learning technologies (including deep learning) have led to innovative applications with remote sensing data, such as extracting information for environmental studies, fusing remote sensing data from different platforms for agriculture monitoring, and identifying features across landscapes (e.g., land cover/land use, historic cultural features of past land use, the vertical structure of forest). Although machine learning has shown great potential for remote sensing applications, the lack of high-quality training data, the explainability, and the reproducibility have limited the wide adoption of machine learning in remote sensing communities. Fortunately, recent efforts in building benchmark training data for remote sensing applications and explainable machine learning technologies are changing the landscape of machine learning applications in remote sensing communities. These recent developments in data and technologies have allowed for trustworthy applications of remote sensing data for solving pressing environmental issues. In this Special Issue, we aim to showcase innovative research using machine learning and remote sensing data in climate and environmental studies as well as human–landscape dynamics and the Anthropocene. Topics of interest include, but are not limited to:

  • climate informatics;
  • land use/land cover classification;
  • creating benchmark machine learning (ML) training data;
  • historic environmental data;
  • explainability of ML models for environmental studies;
  • automated feature extraction and/or classification.

Dr. Katharine M. Johnson
Dr. Yuhan Rao
Dr. Jaime Zabalza
Prof. Dr. Giuseppe Modica
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 papers will be 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 2200 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

  • machine learning
  • climate
  • environment
  • land use/land cover classification
  • human–environment land use dynamics
  • automated feature extraction

Published Papers

This special issue is now open for submission.
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