AI Technology for Enhanced Analysis of High-Resolution Earth Observation Imagery in Climate Change

Dear Colleagues,

The increasing availability of high-resolution Earth observation (EO) imagery has significantly advanced our ability to monitor and understand climate change. Datasets of such images provide essential insights into environmental dynamics, such as land cover changes, extreme weather patterns, and ecosystem shifts. However, the sheer volume and complexity of EO data present challenges for traditional analytical methods. Artificial Intelligence (AI), particularly deep learning and machine learning, has emerged as a powerful tool for extracting meaningful information from EO imagery, enabling automated, scalable, and precise climate monitoring. Integrating AI into EO analysis enhances the accuracy and efficiency of climate assessments, facilitating improved decision-making in environmental management and policy.

This Special Issue aims to explore cutting-edge AI techniques for the processing of high-resolution EO imagery to address climate change challenges. It aligns with the journal’s focus on geospatial data analysis, remote sensing applications, and environmental informatics. By bringing together AI advancements and EO data science, this issue seeks to highlight novel methodologies, interdisciplinary applications, and real-world implementations that enhance climate change analysis and mitigation strategies.

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

• Image Super-Resolution and Data Fusion: enhancing EO imagery quality and combining multi-source data for improved analysis.
• Image Classification and LULC Classification: AI-driven approaches for high-accuracy land use and land cover mapping.
• Weakly/Semi-Supervised Learning in Remote Sensing: reducing data annotation dependency while improving model performance.
• Object Extraction for Natural Hazard Monitoring: AI-powered detection of features relevant to floods, wildfires, landslides, and other climate-related disasters.
• Image Matching and Change Detection: AI-enhanced techniques for aligning, comparing, and analyzing multi-temporal EO images.
• SAR Image Processing: deep learning applications in interpreting synthetic aperture radar data in climate and environmental studies.

Dr. Hongjie He
Dr. Liangzhi Li
Dr. Linlin Xu
Dr. Wei Liu
Prof. Dr. Yuhong He
Guest Editors

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