Estimating the building heights and occupancy of underground space resources based on high-resolution remote sensing images

In recent years, limited surface space and restricted urban expansion have become increasingly prominent. The development of underground spaces has emerged as an effective solution, offering a new dimension for urban growth and sustainability. However, due to the non-renewable and costly nature of underground space (UGS), it should be properly planned before development to avoid waste of resources and economic loss. In this study, the potential of UGS development based on the influence of surface buildings was evaluated by using the high-resolution satellite images of Gaofen-2 (GF-2) in Qingdao, Shandong Province, China. Firstly, the DeepLabv3+ network was lightened to enhance the training efficiency and building extraction accuracy. The building shadows were then extracted by an improved shadow measurement model, and used to estimate the height of extracted buildings based on the imaging mechanism and information of the remote sensing image. Finally, the building height (BH) was converted into the depth of influence on the UGS, and the development potential of the UGS was then estimated. The experimental results show that the UGS within 0-10 m in Qingdao city has been extensively occupied. However, the geological layers between 10-30 m and 30-50 m contain large continuous areas that have not been utilized, indicating the greatest potential for resource development within the city.