Deep Learning in Environmental Remote Sensing: Enhancing Ecosystem Monitoring
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 82
Special Issue Editors
Interests: GIScience; remote sensing; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: geographical information science; spatial analysis and modeling; remote sensing; climate change; land cover land use change
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; GIScience; environmental science; data science; geography
Special Issues, Collections and Topics in MDPI journals
Interests: semantic and knowledge graph; data interoperability and provenance; exploratory data analytics and visualization; geoinformatics
Special Issues, Collections and Topics in MDPI journals
Interests: spatial modeling; big data; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals
Interests: GIS; geospatial analysis; climate change; hydrology; land use and land cover; health geography; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning is rapidly reshaping the field of environmental remote sensing, opening new frontiers for the monitoring and management of ecosystems. This Special Issue, “Deep Learning in Environmental Remote Sensing: Enhancing Ecosystem Monitoring,” centers on the latest research, methodologies, and real-world applications where deep learning technologies drive more effective and timely ecosystem observation.
We welcome contributions that demonstrate how advanced deep learning models—such as Large Language Models (LLMs), diffusion models, multimodal AI, geoembedding, and emerging neural network architectures—are being used to monitor, analyze, and predict ecosystem changes. Key areas of interest include habitat mapping, biodiversity assessment, vegetation health monitoring, detection of land and water changes, and early warning systems for environmental disturbances.
This issue encourages submissions that address practical challenges in ecosystem monitoring, including, but not limited to, dealing with heterogeneous and multi-source remote sensing data, improving spatial and temporal resolution, and enhancing the interpretability and reliability of deep learning outputs for environmental decision-making. Studies that integrate deep learning with ecological models or traditional remote sensing techniques, as well as those presenting case studies of ecosystem applications at local, regional, or global scales, are especially welcomed.
By assembling cutting-edge research and practical solutions, this Special Issue aims to advance the capabilities of deep learning in environmental remote sensing, empowering scientists, practitioners, and policymakers to better understand, protect, and sustainably manage our ecosystems.
Dr. Zhe Wang
Dr. Chao Fan
Dr. Sanaz Salati
Dr. Marshall (Xiaogang) Ma
Dr. Xiang Que
Dr. Hui Wang
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.
Keywords
- remote sensing
- ecosystem monitoring
- GeoAI
- deep learning
- machine learning
- multimodal, LLM, SAM, geoembedding and generative AI
- image processing and pattern recognition
- artificial intelligence
- GIS
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
Related Special Issue
- Deep Learning Innovations in Remote Sensing in Remote Sensing (8 articles)
