EcoSense AI: An AI-Based Environmental Change Detection in Coorg Using Satellite Data and U-Net Deep Learning

Environmental changes such as vegetation loss, deforestation, and land-use changes can gradually affect the ecological condition of a region. Monitoring these changes over large areas through manual inspection or basic satellite-image comparison can be time-consuming and may provide limited information about the spatial extent of the changes. This paper presents EcoSense AI, an AI-based environmental monitoring system developed to detect and analyze environmental changes in the Coorg region of Karnataka using satellite imagery and environmental data. The proposed system combines Sentinel-2 satellite observations with Normalized Difference Vegetation Index (NDVI) analysis to assess vegetation conditions across different time periods. A U-Net based deep learning model is incorporated for pixel-level analysis of satellite imagery, while environmental parameters such as temperature, rainfall, and soil moisture are used to provide additional context for ecosystem assessment. Temporal comparison of satellite-derived vegetation information is used to identify areas showing degradation or improvement. The system integrates these analytical components into a web-based platform for geospatial visualization and environmental assessment. Model performance is evaluated using standard metrics including accuracy, precision, recall, F1-score, Intersection over Union (IoU), and Dice coefficient. The proposed approach provides an automated framework for localized environmental monitoring and can assist in identifying areas that require further ecological investigation.