Establishing Land Use Maps using a Deep Learning Model and UAV imagery: Testing for Rice Land in the Mekong Delta

Abstract Land use (LU) maps are considered useful for assisting land management. These thematic maps are highly persistently dynamic due to socioeconomic progress throughout. Generally, the traditional approaches of field GPS and surveys are costly and time-consuming. Hence, this research suggests a mapping technique for the Mekong Delta regions that applies the indirect measurement approach from UAV photos in a new deep learning model of segment anything model (SAM) adopted for geospatial data for the rice land. The Phantom 4 pro drone is recommended for use in establishing the LC maps of agricultural land as it has a high-resolution camera and accuracy system, less affected by wind. The SAMGeo was robust and assisted in producing three LU maps of three study sites in Soc Trang, An Giang, and Long An provinces. This study highlights the primary obstacles and advances in utilizing UAV imagery to enhance effective land management and apply, enhance effective land management, and apply deep learning models in this field.