Atlas AI: Watching the planet evolve through AI-Powered Satellite Intelligence

Abstract: Environmental changes such as deforestation, urban expansion, and reduction in water bodies have increased in recent years. Monitoring these changes using traditional field surgeys is often time-consuming and difficult for large areas. Because of this, satellite remote sensing combined with artificial intelligence has become an effective approach for environmental monitoring. This paper reviews recent studies that use satellite pfatforms such as Sentinel-1, Sentinef-2, and Landsat along with models including CAT, U-Net, Vision Transformers (ViT), and LSTM for change detectton The reviewed studies suggest that combining multispectral and SAR data improves monitoring accuracy in different land- cover conditions. it is also obsewed that transformer and hybrid deed learning models oflen Perform better than traditional approaches. Oreraff, the combination of AI and satellite imagery provides a useful way to monitor environmental changes and support better planning and consewation decisions.

Keywords: Environmental Monitoring, Remote Sensing, Satellite Imagery, Artificial Intelligence, Deep Learning, Deforestation Detection, Urban Expansion, Water Body Shrinkage, Sentinel-1, Sentinel-2, Landsat, Convolutional Neural Network (CNN), U-Net 3D, Vision Transformer (ViT), Long Short-Term Memory (LSTM), Transformer-Based Models, Change Detection, Sustainable Development, Urban Planning, Land Use Land Cover (LULC).