Monitoring of granite quarries using deep learning and UAV photogrammetry in Bengaluru, India

Abstract

Granite quarrying, a cornerstone of the construction industry in South India, yields significant economic benefits but poses substantial environmental and social challenges, including land degradation, dust pollution, alternation of the water regime, and harsh working conditions. Rapid urban expansion has escalated granite demand in many countries, intensifying quarrying activities. This trend is particularly pronounced in Bengaluru, India, where rural-urban transformation causes concerns about environmental sustainability and social-ecological consequences of urban resource mining. This study proposes an innovative multi-modal framework to monitor granite quarrying in Bengaluru by combining deep learning with a 2024 dry-season multi-date Sentinel-2 composite for quarry segmentation and UAV SfM-MVS photogrammetry for volumetrics. We benchmark five CNN architectures—U-Net, PSPNet, DeepLabV3 +, FCN, and EMANet. In-area development results peaked with DeepLabV3+ (F1 ≈ 94.6%, IoU ≈ 89.7%), while an external, geographically independent audit established PSPNet as the most robust model (F1 = 93.4% [95% CI 90.8–95.9], IoU = 87.6%) with significantly fewer errors than alternatives (McNemar tests, FDR-adjusted p < 0.001). Applying the best model across the region yielded 252 candidates; 227 quarries were confirmed via field checks and sub-meter imagery, spanning 740 hectares. UAV photogrammetry at the Prasannacharipalya site (0.046 m grid; LoD95 masking), yielded a combined lowering volume of 9 280 051 m3 (acceptance area 97.2%; 95% CI ± 17 864 m3 0.19%). The satellite-to-UAV pipeline enabled automated, scalable quarry footprint mapping with site-level volumetric quantification, offering actionable evidence for environmental management and oversight of quarrying in the quickly-urbanizing study region.