AI-Powered FRA Atlas: An AI- and WebGIS-Based Decision Support Framework for Forest Rights Act Implementation Monitoring

The Forest Rights Act (FRA), 2006 recognizes the land and resource rights of forest-dwelling communities in India, but its implementation is hindered by paper-based records, fragmented databases, delayed claim verification and weak monitoring. This paper presents an AI-powered FRA Atlas integrated with a WebGIS-based Decision Support System (DSS) that combines digitized FRA records, Sentinel-2 satellite imagery, machine-learning land-cover classification, a retrieval-augmented policy assistant, a statutory scheme-eligibility rule engine and a tamper-evident audit trail in one platform. A Random Forest classifier operating on five Sentinel-2 bands and three spectral indices assigns pixels to seven land-cover classes. On a 4,200-sample holdout set it reaches 87.24% overall accuracy, Cohen’s κ of 0.8511 and a macro F1-score of 0.8726, and 5-fold stratified cross-validation gives 87.35% ± 0.24%. The hybrid retrieval module achieves 85.71% top-1 accuracy and a mean reciprocal rank of 0.9286 on seven policy queries. The classifier is trained on calibrated synthetic spectral samples rather than field-labelled FRA land, so the reported figures measure separability under those distributions and are not field accuracy; validation on real labelled imagery remains the main open item.