This chapter explores the pivotal role of Geospatial Artificial Intelligence (GeoAI) in advancing human geography research, particularly in the domain of Land Use and Land Cover (LULC) studies. As the Earth’s environment evolves amid a growing human population, accurate LULC mapping becomes essential for understanding human–environment interactions. The chapter highlights how advancements in remote sensing technologies and the proliferation of Earth observation data have empowered geographers to monitor and analyze LULC changes with unprecedented detail. By integrating GeoAI—especially deep learning models like convolutional neural networks and transformer-based architectures—researchers can enhance the precision of LULC maps through semantic segmentation, facilitating deeper insights into urbanization, deforestation, agricultural expansion, and land abandonment. Despite these technological advancements, challenges such as the need for extensive labeled datasets and class imbalances remain. The chapter delves into the socioeconomic impacts of human-induced LULC changes on climate, hydrology, and biodiversity, emphasizing their significance in human geography. It advocates for future research to focus on developing explainable GeoAI models that incorporate socio-economic and environmental variables, aiming to enhance sustainable land management, climate resilience, and inform policy-making processes relevant to human geography.