Integrating Deep Learning (DL) and Satellite Imagery in Landslide Mapping
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 133
Special Issue Editors
Interests: landslides; geomorphology; remote sensing
Special Issue Information
Dear Colleagues,
Integrating deep learning (DL) and satellite imagery in landslide mapping has emerged as a powerful approach to improve the detection, mapping and monitoring of landslides across different spatial and temporal scales.
This Special Issue aims to bring together state-of-the-art research that explores innovative methodologies, data integration strategies and practical applications of deep learning for landslide mapping and analysis. By fostering interdisciplinary contributions from geomorphology, remote sensing and data science, this Special Issue seeks to advance methodological robustness and promote reproducible, scalable and operational solutions for landslide risk reduction and disaster management.
Contributions may address topics such as automated landslide inventory generation, post-event mapping, susceptibility and hazard assessment, multi-sensor data fusion, the transferability of DL models across regions and challenges related to training data quality, interpretability and uncertainty.
Dr. Helen Cristina Dias
Prof. Dr. Carlos Henrique Grohmann
Guest Editors
Manuscript Submission Information
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Keywords
- mass movement
- landslide mapping
- landslide detection
- satellite remote sensing
- convolutional neural networks (CNNs)
- hazard assessment
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