Deep Learning Framework for Semantic Change Detection in Urban Green Spaces Along with Overall Urban Areas

Urban green spaces, crucial for ecological balance, face global degradation from natural disasters and rapid urbanization. Manual deforestation monitoring is laborious, prompting a shift to remote sensing and bitemporal satellite imagery. Traditional change detection (CD) methods have limitations, but deep learning, especially in semantic CD, shows promise. This study addresses challenges in semantic CD techniques, advocating for comprehensive training on datasets covering both semantic change masks and binary change masks. We propose a novel semantic CD network for urban changes while additionally providing urban greenery increased and decreased regions, integrating deep bitemporal features with an encoder-decoder structure, Atrous spatial pyramid pooling, and a spatial attention module with parallel dilated convolutions. Quantitative assessment, especially with pre-trained VGG16 as a backbone and parallel convolutional layers, demonstrates the proposed method's superiority, showcasing substantial improvements in urban greenery CD alongside overall urban changes. The proposed method holds potential for monitoring climate change, rapid urbanization, and the impact of natural disasters on urban environments, particularly urban greenery.