Autonomous Satellite AI for Real-Time Environment Crime Detection

Mr.Kumar K1, Ujwal G Naik2, Somesh K N3, Tarun R4, Shivamani N5 Assistant Professor, Dept of CSE, KSIT, Karnataka, India1
Student, Dept of CSE, KSIT, Karnataka, India2-5

ABSTRACT:Illegal environmental activities such as deforestation, unauthorized sand mining, and urban lake encroachment continue to pose severe threats to ecological sustainability and urban resilience. traditional monitoring methods—manual patrols, delayed reporting, and fragmented data collection—are reactive, resource-intensive, and often fail to provide timely intervention. this paper introduces an autonomous, ai-driven framework that leverages multi-temporal satellite imagery and deep learning-based change detection to enable proactive environmental crime monitoring.The system architecture integrates sentinel-2 datasets, spectral indices such as ndvi (normalized difference vegetation index) and ndwi (normalized difference water index), and a u-net segmentation pipeline to isolate and classify ecological anomalies. a lightweight backend, developed using python and flask, processes satellite tiles and pushes geotagged alerts with comparative before-and-after imagery to a mobile application designed for enforcement authorities. this multimodal alert pipeline ensures that actionable intelligence is delivered in real time, bridging the gap between orbital data and civic action.Experiments conducted on benchmark datasets (sentinel-2, copernicus hub) and a custom bengaluru dataset demonstrate high accuracy in detecting vegetation loss, water body encroachment, and illegal land-use changes. the framework achieved an overall anomaly detection accuracy of 96.1%, with average alert delivery latency under 5 seconds, validating its suitability for continuous monitoring. beyond local civic enforcement, the system’s scalability positions it as a transparent auditing tool for corporate esg (environmental, social, and governance) compliance, disaster management, and smart city infrastructure.By shifting environmental protection from a reactive to a preemptive paradigm, this autonomous satellite ai system represents a transformative step toward intelligent ecological governance, offering a practical, reliable, and future-ready solution to combat environmental crimes worldwide.Keywords--satellite imagery, environmental crime detection, change detection, deep learning, u-net segmentation, ndvi, ndwi, real-time monitoring, cloud-edge deployment, esg compliance, smart cities, civic enforcement.