AI and Drone-Based Monitoring in Mining: Redefining Environmental Baselines, Plantation Strategy, and Post-Closure Accountability

Abstract. Mining and associated activities are pivotal in India's economic development, contributing over 2.5% to the national GDP and employing approximately 11 million people. However, Environmental degradation from mining has resulted in substantial ecosystem loss all over India. Between 1994 and 2022, India’s eastern coal belt witnessed a 7.3–17.6% loss in forest cover, a 5–10% reduction in water bodies, and a 3–5% drop in agricultural land. From 1991 to 2021, vegetation cover in mining zones declined from 40.17% to 31.20%, while mining land expanded to 9% of the regional footprint. As a result of this, mining PSUs have set a target of planting 60–75 million trees across 24,000–30,000 hectares by 2030. 

This paper explores current practices and possible interventions across three critical environmental dimensions of mining: (a) baseline environmental data collection during new mine allocations, (b) site selection strategies for ecological rehabilitation, and (c) mechanisms for Monitoring, Reporting, and Verification (MRV) during operations and post-closure phases. The analysis draws from MoEF and IBM guidelines, global standards such as the IEEE for EIA, and insights from green cover data and land-use change assessments using Aereo, a GIS and AI native solution, which automates data ingestion, orthomosaic, LULC, drainages analysis, canopy cover, afforestation area and change detections - replacing manual, error-prone methods with fast, scalable, and auditable insights with harvesting power of AI. The study concludes by advocating the institutional adoption of AI-integrated MRV frameworks, real-time plantation validation, and centralized data repositories within India’s mining regulations.