Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

• المؤلفون الرئيسيون: Pande C.B., Srivastava A., Moharir K.N., Radwan N., Mohd Sidek L., Alshehri F., Pal S.C., Tolche A.D., Zhran M.
• مؤلفون آخرون: 57193547008
• التنسيق: مقال
• منشور في: Springer 2025
• الموضوعات: India; Maharashtra; Classification (of information); Engines; Forestry; Image enhancement; Land use; Machine learning; Satellite imagery; Vegetation mapping; Change detection; Energy; Google earth engine; Google earths; Land cover maps; Land use and land cover; Land-use and land-cover classifications; Random forests; Remote-sensing; SDG; Anthropocene; cloud cover; data set; detection method; image classification; land cover; land use change; machine learning; remote sensing; satellite imagery; Sustainable Development Goal; Remote sensing

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness...