Machine Learning and GeoAI for Remote Sensing Environmental Monitoring (2nd Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 488
Special Issue Editors
Interests: spatio-temporal data mining; remote sensing; computer vision; vegetation monitoring; dynamic temporal trend analysis; multi-modal data fusion
Interests: machine learning; remote sensing; semantic segmentation; scene parsing; small-sample learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of machine learning and GeoAI has transformed remote sensing applications, enabling automated, high-resolution environmental monitoring and spatial modeling. As ecosystems face increasing challenges from climate change, deforestation, air pollution, land-use change, and urban expansion, AI-driven methods offer innovative solutions for large-scale, multi-temporal analysis of environmental processes. Remote sensing data, including multispectral, hyperspectral, LiDAR, SAR, and atmospheric observations, combined with deep learning and spatial modeling techniques, provide unprecedented insights into landscape dynamics, cloud formation patterns, air quality variations, transportation networks, and ecosystem health.
Recent developments in deep learning, graph neural networks, and spatial statistics have enhanced our ability to model complex spatial-temporal interactions in environmental systems. AI-driven remote sensing techniques are now being employed to analyze traffic-related air pollution cloud structures, track aerosol distributions, and assess environmental risks associated with urban mobility and infrastructure development. These methods provide valuable information for climate modeling, smart city planning, and public health monitoring. Cloud computing platforms like Google Earth Engine facilitate scalable data processing, allowing researchers to efficiently analyze multi-source geospatial data. Integrating AI with remote sensing is crucial for air quality forecasting, vegetation mapping, land cover classification, transportation impact assessment, environmental risk modeling, and predictive analysis of ecosystem changes.
This Special Issue seeks contributions to AI-driven remote sensing applications for environmental monitoring and spatial modeling. We welcome studies focusing on deep learning, spatio-temporal analysis, multi-source data fusion, transportation-related environmental modeling, and the development of scalable AI frameworks for geospatial data interpretation.
Dr. Haomin Yu
Dr. Zhiyu Jiang
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
- spatio-temporal analysis
- multi-source data fusion
- environmental monitoring
- predictive analytics
- air quality monitoring
- cloud pattern recognition
- atmospheric remote sensing
- transportation impact assessment
- traffic-related air pollution
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