Advancing Land Use and Land Cover Classification Through Deep Learning in Satellite Imagery

Land Use and Land Cover Classification through Deep Learning in Satellite Imagery” describes a method for accurately mapping geographical features based on their pixel-level characteristics in satellite images. This method can distinguish between various land uses (such as urban areas, forests, and agriculture) and land covers (such as vegetation, water bodies, and bare soil) on Earth’s surface. This study uses deep learning techniques to increase the accuracy and efficiency of land use and cover classification in remote sensing and satellite data. Using core models such as CNNs, VGG, and MobileNet, an overall accuracy of 93% was obtained. The dataset used consists of 27,000 hyperspectral, multispectral, and RGB pictures that cover a wide range of spectral and geographical data. Using such a large dataset, the suggested technique helps efficiently overcome the challenges presented by complex remote sensing data.