Land Use

Land use research focuses on understanding how land is allocated and utilized, aiming to improve urban planning, resource management, and environmental monitoring. Current research heavily employs machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), graph neural networks (GNNs), and transformers, along with advanced techniques like transfer learning and semi-supervised learning, to analyze diverse data sources including satellite imagery, sensor data, and social media posts. These advancements enable more accurate and efficient land use mapping, change detection, and prediction, informing policy decisions related to climate change mitigation, disaster response, and sustainable urban development.