Abstract: [Purposes]To evaluate the efficacy of AI-augmented high-resolution satellite remote sensing in enhancing the efficiency and accuracy of natural resource regulation enforcement in Guiyang.[Methods]A human-machine collaborative technical framework was established by integrating commercial high-temporal-resolution imagery with deep learning and GIS technologies.The system operationalized a “change identification-clue filtration-enforcement verification” pipeline,wherein AI automatically extracted potential change indicators followed by manual verification and legality assessment to detect four violation categories: construction,paved surfaces,water bodies,and earthwork operations.AI-driven change detection demonstrated 1155-fold efficiency gain over manual interpretation.Detection accuracy descended in the following order: water bodies>buildings>paved surfaces>earthworks.Violations were identified within 20 days post-occurrence.The empirical data facilitated over 200 enforcement cases,resulting in the reclamation of 1555 acres of cultivated land,431 acres of permanent prime farmland,and 328 acres of ecological red zones.[Findings]AI-derived change indicators showed 100% spatial concordance with national supervisory data.[Conclusions]AI-enhanced remote sensing significantly improves natural resource enforcement efficacy,with human-machine collaboration representing the optimal paradigm for monitoring complex mountainous terrains.

Key words: high-resolution remote sensing, AI, natural resource regulation enforcement, change detection, human-machine collaboration