Deep Learning in Environmental Remote Sensing: Challenges, Innovations, and Achievements

Dear Colleagues,

The environmental and socioeconomic impacts of climate change have been become increasingly apparent, affecting different aspects of human life. To deal with such a global problem, many countries and multinational companies have committed to several environmental pledges, most notably the one to achieve net-zero carbon by 2040. However, it is not easy to monitor and manage progress without the aid of modern technologies. In particular, advances in high-altitude imaging have facilitated the capture of large-scale image datasets, thus benefiting the development of data-driven environmental remote sensing research. In addition to the abundant data now available, the growing interest in deep unfolding techniques is also essential to further developing this field. Efficient and interpretable deep learning approaches provide new insights into the monitoring and management of environmental footprint, signifying their importance in environmental remote sensing research.

This Special Issue aims to specify and summarizes current challenges, innovations, and achievements in environmental remote sensing research. This type of research agenda is essential to facilitate efficient and interpretable deep learning in future studies. Additionally, it fits well with the journal scope of image processing and computer vision.

Three important aspects will be covered: challenges, innovations, and achievements in deep learning in environmental remote sensing. Submissions to the Special Issue can thus be categorized as follows:

• Surveys and short communications that deal with a small aspect, such as a specific challenge or idea, without details about its implementation or verification.
• Original research articles that present novel solutions to existing challenges and provide details about their implementation and verification.
• Systematic review articles that collate studies in the literature and provide a comprehensive description of achievements. They should also specify current difficulties and discuss future research directions.

Dr. Dat Ngo
Prof. Dr. Bongsoon Kang
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 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.

• computer vision
• image processing
• machine learning
• deep learning
• environmental monitoring
• ecological footprint analysis
• carbon emission monitoring
• land use management
• disaster detection and localization
• deep unfolding
• interpretable deep learning
• vision transformer