Unmanned Aerial Vehicle (UAV) oblique photogrammetry has been extensively employed in mining, albeit predominantly for reconstructing three-dimensional scenes and detecting changes within mining sites, lacking predictive capabilities. Leveraging 3D real scene model data, this study presents a two-stage prediction model, merging the probabilistic integral method with recurrent neural network (PIMF-RNN), to mitigate the impact of internal and external factors on surface subsidence, thereby enhancing predictive accuracy. Building upon this framework, a methodology was developed to forecast the maximum surface subsidence height and affected area under the block caving method, offering crucial data support for mitigating hazards associated with this mining technique. Analysis of surface data from Pulang copper mine during 2018–2020 demonstrates a prediction accuracy of 91.47% for maximum surface subsidence height and 87.52% for subsidence area. This research expands the potential applications of UAV oblique photogrammetry techniques within mining contexts. Furthermore, it establishes a cost-effective and efficient operational procedure for predicting mine surface subsidence.

Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars and the mining processes, this paper developed a lightweight model to forecast surface settlement utilizing the probability integration approach to address the issue of predicting surface settlement in metallic mines. This study focuses on the Pulang Copper Mine, situated in the northeast of Shangri-La County within the Yunnan Province, as a case example. This mine employs the block caving method, which results in substantial surface subsidence. A visual mining simulation program is designed to combine the ore mining plan with the prediction model, manage the ore output of each mining point in batches, treat the ore pillars released in the planning cycle as strip work, and simulate and calculate the surface area above the ore pillars settlement value. The calculated values of surface subsidence induced by ore drawing are then interpreted as the downward displacement of the surface subsidence beneath the strip workings. Furthermore, to verify the reliability of the model, three-dimensional laser point cloud data of the Pulang Copper Mine in recent years were collected, and the differences between the predicted surface and the measured surface were calculated and analyzed.