Multi-Platform Remote Sensing for Geological Cartography and Disaster Assessment

Dear Colleagues,

The increasing availability of multi-platform remote sensing data—from satellite missions (e.g., Sentinel, Landsat, Gaofen, Planet), airborne platforms (LiDAR, UAV/drones), and ground-based sensors (GNSS, GPR, terrestrial laser scanning)—has opened unprecedented opportunities for geological cartography and disaster assessment. These multi-platform approaches enable multi-scale, high-resolution observations that are essential for understanding the processes of the Earth’s surface, mapping geological structures, and monitoring natural hazards with enhanced precision and temporal frequency.

Simultaneously, the integration of artificial intelligence (AI) and machine learning (ML) with remote sensing data is transforming how we detect, monitor, and predict geohazards, including landslides, glacier surges, glacial lake outburst floods (GLOFs), ice–rock avalanches, earthquakes, and volcanic deformation. AI-driven methods, particularly deep learning and small-sample learning techniques, offer powerful tools for automated feature extraction, change detection, deformation analysis, and early warning in data-scarce environments such as high mountain regions and polar areas.

We aim to collect cutting-edge research that leverages multi-platform remote sensing integrated with advanced computational methods such as the following:

Geological cartography: High-resolution mapping of geological structures, lithology, geomorphology, and active tectonics;

Disaster assessment: Monitoring, modeling, and risk assessment of geohazards and cryospheric hazards in a changing climate.

We particularly encourage contributions that address the challenges of data scarcity (e.g., in high mountain or remote regions) through innovative approaches such as small-sample learning, transfer learning, and physics-informed AI—topics that align with the latest advances in the field.

We invite original research articles, reviews, and case studies on topics including, but not limited to, the following:

Multi-Platform Remote Sensing Techniques: Satellite-based monitoring (optical, SAR, InSAR, thermal); UAV/drone-based photogrammetry and LiDAR; airborne and terrestrial laser scanning; GNSS and ground-based geodetic measurements; and multi-sensor data fusion and integration.

Geological Cartography: High-resolution geological and geomorphological mapping; active tectonics and fault characterization; structural geology and 3D geological modeling; lithological mapping and mineral exploration; and geomorphic change detection and landscape evolution.

Geohazard and Cryospheric Hazard Assessment: Landslides, rock avalanches, and debris flows; glacier surges, ice avalanches, and glacial lake outburst floods (GLOFs); permafrost degradation and slope failures; earthquake-induced deformation and seismic hazards; volcanic deformation and thermal anomalies; and multi-hazard cascading processes and chains.

AI and Machine Learning in Remote Sensing: Deep learning for feature extraction and change detection; small-sample learning, transfer learning, and few-shot learning; physics-informed neural networks and hybrid modeling; automated hazard detection and near-real-time monitoring; susceptibility mapping and predictive modeling; and uncertainty quantification in AI-based predictions.

Disaster Risk Reduction and Applications: Early warning systems and operational monitoring frameworks; risk assessment and vulnerability analysis; climate change impacts on geohazards and cryospheric hazards; case studies from high mountain regions (Himalaya, Tian Shan, Andes, Alps, etc.), polar areas, and tectonically active regions; and sustainable development and disaster resilience.

We look forward to receiving your contributions and building a compelling collection of research that advances the frontiers of multi-platform remote sensing for geological cartography and disaster assessment.

Prof. Dr. Yun Yang
Dr. Yuancheng Huang
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.

• multi-platform remote sensing
• geological cartography
• geohazards
• cryospheric hazards
• glacier surges
• landslides
• GLOFs
• active tectonics
• InSAR
• LiDAR
• UAV/drone
• optical and SAR data fusion
• artificial intelligence
• machine learning
• deep learning
• small-sample learning
• change detection
• deformation monitoring
• disaster risk assessment
• early warning
• climate change adaptation
• sustainable development