A COMPREHENSIVE REVIEW OF SOFTWARE FOR PREDICTING LAND USE AND LAND COVER

Abstract

Land Use and Land Cover prediction is a critical component in environmental management, urban planning, and sustainable development. With advancements in computational capabilities, numerous software applications have emerged to model and forecast LULC changes based on spatial data and environmental variables. This paper provides a comprehensive review of existing software platforms designed for LULC prediction, analysing their methodologies, features, input requirements, and performance. We explore tools that employ machine learning, cellular automata, agent-based models, and hybrid approaches, highlighting their suitability for different geographical scales and use cases. Additionally, we address key challenges such as data availability, accuracy, ease of use, and integration with GIS systems. The review also identifies trends in the field, including the growing utilization of open-source software and cloud-based solutions for enhancing predictive accuracy. These findings offer valuable insights for researchers, urban planners, and policymakers, assisting them in selecting appropriate tools for LULC analysis and forecasting.