Advances in Earth observation and high-performance computing are revolutionizing how we monitor land cover and support sustainable development. This volume explores cutting-edge methods in land cover classification, highlighting deep learning applications such as semantic segmentation, object detection, and temporal analysis. Key contributions include the ABNet model for enhanced feature representation, accuracy assessments of 30-meter land cover products, CNN-based wildfire mapping, and the segmentation of China's coastal wetlands. These studies showcase AI's growing role in environmental monitoring and promote innovative and interdisciplinary solutions for managing landscape changes.
Advances in Earth observation and high-performance computing are revolutionizing how we monitor land cover and support sustainable development. This volume explores cutting-edge methods in land cover classification, highlighting deep learning applications such as semantic segmentation, object detection, and temporal analysis. Key contributions include the ABNet model for enhanced feature representation, accuracy assessments of 30-meter land cover products, CNN-based wildfire mapping, and the segmentation of China's coastal wetlands. These studies showcase AI's growing role in environmental monitoring and promote innovative and interdisciplinary solutions for managing landscape changes.
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