2026-10-14T14:11:50Z https://www.scidb.cn/oai

10.57760/sciencedb.j00001.01676 2026-06-01T15:27:55Z

2026-06-01 Pakistan 2023 Multi-source Open Building Fusion Dataset doi:10.57760/sciencedb.j00001.01676 en Architectural spatial data is an important foundational data for disaster exposure assessment, built environment analysis, and emergency rescue. In response to the problems of inconsistent time benchmarks, significant differences in geometric expressions, insufficient attribute integrity, and obvious cross source conflicts in existing open-source building data, this paper constructs a multi-source open-source building fusion dataset for Pakistan in 2023. This data is based on Google Open Buildings, Microsoft Global ML Building Footprints, and OpenStreetMap building data, supplemented by the 2023 building existence probability grid, building height grid, and road network information. It is produced through the process of "preprocessing timeliness verification matching fusion road constraint adjustment quality control". Organize in the form of a unified building master data, recording target geometric contours, source markers, temporal status, matching relationships, adjustment parameters, and supplementary attributes in a single element. This dataset has the characteristics of building stock screening, spatial conflict resolution, and traceable expression of results under a unified time reference, which can provide reference for research on disaster assessment, exposure modeling, and built-up area analysis in Pakistan.  multi-source building data; building fusion; temporal validation; spatial conflict resolution; Pakistan; open spatial dataset chen nuo Zhou Xiaoguang Wang Lan Hou Dongyang Xi Jiajin Peng Shu Lin Shangwei PUBLIC https://creativecommons.org/licenses/by-sa/4.0/ dataset http://www.doi.org/10.11922/11-6035.csd.2026.0055.zh Science Data Bank