Area Usage

Area usage analysis encompasses the study of how spaces are utilized, aiming to understand and model human activity patterns across various contexts. Current research focuses on developing automated methods for area identification and classification using diverse techniques, including machine learning (e.g., deep learning architectures like SegFormer and U-Net, clustering algorithms like k-Means and Gustafson-Kessel), and novel embedding techniques to represent spatial data. These advancements have significant implications for diverse fields, such as urban planning, environmental monitoring (e.g., deforestation tracking), and resource management (e.g., optimizing agricultural yields), by providing data-driven insights into spatial dynamics.