A Multi-Modal AI Framework for Real- Time Illegal Sand Mining Detection and Prevention using SAR Satellite Imagery and IoT-Enabled Seismic Sensors in India

Abstract

The mining activities pose threats of the environment, destruction of biodiversity, and environmental imbalances, which are illegal. Although the entire community is involved in the illegal mining, the conventional methods of detecting and implementation of environmental policies is predominantly manual and ineffective. The paper thus suggests that an artificial intelligence solution should be utilized to extract and forecast illegal mining activities through the execution of the collective processing of satellite imagery and environmental sensors. Computer vision algorithms that are based on artificial intelligence process the satellite images, which results in automatic identification of land use, mining patterns, and illegal mining areas. The strategy is also supported by the fact that real time environmental sensors are used to gather data on air quality, soil composition, and ground vibration data, thereby offering temporal data of the mining activities. The suggested framework will be based on the spatiotemporal machine learning method of the analysis of both past and live data to enable the identification of hotspots of the illegal mining at risk and the time frame to be predicted. By implementing the multi-modal data fusion method by integrating spatial and temporal attribute, the proposed method enhances the accuracy of the detection by removing the false positives. The model introduces the Explainable AI (XAI) approach to the explanation of the results with the feature attribution methods in order to address the explanation of the AI decision-making process in the legal context of its adequacy. It is evident that the proposed approach is better in detecting the illegal mining activity than the conventional monitoring practices because of its accuracy, early warning and predictability of the method. The study confirms the efficiency of the approach proposed in helping the departments concerned in the active monitoring, intervention, and implementation of mining laws in the various concerned departments worldwide.