Survey on Machine Learning Algorithms for Classification and Prediction of Land Use Changes Using GIS - Bharanikumar, N; Dhanalakshmi, P (International Journal of Computer Sciences and Engineering, 2025)
Large area land-cover monitoring scenarios, involving large volumes of data, are becoming more prevalent in remote sensing applications. Thus, there is a pressing need for increased automation in the change mapping process the land transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks. The objective of this research presents the survey report based on compare the performance of three machine learning algorithms (MLAs) and prediction of land use changes in GIS. The change map generated using ARTMAP has similar accuracies to a human-interpreted map produced by the U.S. Forest Service in the southern study area (John Rogan et al 2007). ARTMAP appears to be robust and accurate for automated, large area change monitoring as it performed equally well across the diverse study areas with minimal human intervention in the classification process. GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. the individual contribution of each predictor variable was examined and shown to vary across spatial scales.At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales