The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed the field of geography by enhancing spatial data analysis, environmental monitoring, and decision-making processes. Geographic research increasingly relies on large geospatial datasets derived from satellite imagery, geographic information systems (GIS), remote sensing, and global positioning systems (GPS). AI and ML algorithms provide efficient tools for analyzing complex spatial patterns, predicting environmental changes, and automating mapping processes (Goodchild & Li, 2021). This paper examines recent trends in the application of AI and ML within geographical studies, focusing on geospatial data analysis, remote sensing image classification, urban planning, climate change assessment, disaster management, and agricultural monitoring. The study adopts a qualitative review methodology based on secondary data collected from research articles, reports, and academic publications. Findings indicate that AI-driven techniques such as deep learning, neural networks, and spatial predictive modeling have significantly improved the accuracy and efficiency of geographic analysis (Reichstein et al., 2019). The integration of AI with GIS and remote sensing has given rise to the concept of Geospatial Artificial Intelligence (GeoAI), which enables the extraction of meaningful spatial information from large datasets (Janowicz et al., 2020). Despite its benefits, challenges such as data quality, computational requirements, and limited technical expertise remain significant barriers. The paper concludes that AI and ML will continue to play a transformative role in geographic research and sustainable development planning.
Anand Purushottam Pandit (Fri,) studied this question.