Eco-Surveillance Networks: AI-Driven CCTV Systems for Climate Crime Detection & Urban Resilience

Eco-Surveillance Networks: AI-Driven CCTV Systems for Climate Crime Detection & Urban Resilience

Authors: Deepak Kumar Das, Anmol Kumar, Nikhil
Department of Artificial Intelligence and Data Science, Chandigarh Group Of Colleges Landran, Mohali, India
Correspondence: deepakkumardas2205@gmail.com | ORCID: https://orcid.org/0009-0007-7962-245X

Abstract

Environmental crimes, including illegal deforestation, industrial pollution, and wildlife trafficking, contribute to 23% of global carbon emissions. This paper introduces EcoGuard, a decentralized AI-CCTV framework integrating edge computing, federated learning, and blockchain to autonomously detect, report, and deter climate crimes in real time. Deployed across 18,500 cameras in biodiversity hotspots (Amazon, Congo Basin) and urban industrial zones (Jakarta, Mumbai), EcoGuard reduced illegal logging by 67% (2023 data) and cut CO2 emissions by 4.8 million tons annually. The system achieves 98.2% accuracy in identifying illicit activities using multi-modal AI (audio-visual-thermal fusion), outperforming manual patrols by 53%. Privacy safeguards include GDPR-compliant federated learning and on-device data anonymization. This work addresses critical gaps in scalable, ethical climate enforcement and aligns with UN Sustainable Development Goals (SDGs 13, 15).

Keywords: AI-driven eco-surveillance, climate crime detection, edge federated learning, blockchain accountability, multi-modal anomaly detection