Deep Learning-Based Multitemporal Spatial Analytics for Assessing Reclamation Compliance of Coal Mining Permits in Kalimantan with Satellite Images

Monitoring reclamation compliance is important to ensure mining activities follow environmental regulations and reduce land degradation. Yet, few studies directly assess compliance by linking multitemporal satellite data with mining permits. This study presents a multitemporal spatial analytics approach to evaluate reclamation compliance in coal mining permit areas in South Kalimantan, Indonesia. Using satellite imagery from 2016 to 2021, a U-Net-based deep learning classification model classified five land surface types (topsoil, subsoil, vegetation, coal bodies and water bodies) with 0.94 accuracy and a Kappa statistic of 0.91. However, this relatively high accuracy was influenced by the dominance of vegetation compared to more challenging classes such as topsoil and subsoil, which remain subject to misclassification. Analysis of temporal transitions revealed patterns of surface disturbance and delayed reclamation, particularly shown by increased subsoil and reduced vegetation. These changes were integrated with coal mining permit boundaries to derived compliance ratios (CR) ranging from 0.32 to 1.44 across nine permit holders, most of which showed moderate to excellent compliance levels. This indicates that reclamation efforts have been generally being implemented, with several permit holders exceeding expectations, while a few others still need to improve. Reclamation Activity Index (RAI) was developed to classify annual performance and showed strong alignment with the U-Net-based deep learning classification model for surface change trends. The proposed approach provides a scalable and practical tool to support evidence-based monitoring and enforcement of mining reclamation policies.