Land Use Changes and Sustainability: A Review of Geospatial, AI and Socioeconomic Approaches

Abstract

Integration of geospatial technology and Artificial Intelligence (AI) algorithms, coupled with socio-economic assessments can optimize sustainable land use management decision-making. The evaluation examines how remote sensing collaborates with geographic information systems (GIS) and predictive AI models support land use analysis. The inclusion of socio-economic data improves the analysis of human-environment connections, which results in stronger decision-making in development in land management. Current applications encounter several significant challenges, such as limited interpretability of AI models, inconsistent data availability, and weak system integration across geographic, social and computational domains. The article examines integrated methodologies with their constraints as well as the requirements for standardized protocols, together with interdisciplinary teamwork and ethical management of AI systems. During land cover observation, high-resolution remote sensing works together with machine learning approaches to detect changes, yet the assessment methods within deep learning remain unclear to users. Socio-economic data integration faces challenges because different information collection methods generate inconsistent results, and various data resolutions create difficulties for integration. Research priorities must include real-time data collection as well as hybrid AI model development alongside stakeholder-driven land use planning systems. The study provides vital information to support adapting sustainable land management by stressing various aspects of geospatial technologies to bring solutions under various socio-economic environments between important sectors as well as explaining the capabilities of AI systems.