该地理空间数据集绘制了2002年至2022年间被破坏性野火摧毁或暴露于其中的所有建筑的点位。数据涵盖美国本土至少造成10栋建筑损毁的362起野火事件。利用Microsoft（Bing Maps Team, 2018）发布的公共建筑轮廓数据集，我们将建筑叠加至高分辨率火灾前后航空影像中，以分类判定其是否烧毁。研究纳入了野火过火边界内的所有建筑，以及边界外2.4公里缓冲区内的建筑（代表荒野-城市交界区定义）。我们绘制了2014年至2022年期间228场野火的建筑损失情况，并整合了2002年至2013年（Alexandre et al., 2016; Kramer et al., 2018）及2014年至2018年部分大型野火（Caggiano et al., 2020）的现有建筑损毁数据。对2002年至2013年的数据进行筛选，以符合每场野火至少10栋建筑损毁的标准，并通过将损毁建筑数量与事故报告总数进行比对来进行质量检查。此外，利用Microsoft建筑点位数据，在将点位与历史航空影像比对以剔除火灾发生时不存在的开发项目后，添加了野火边界外2.4公里缓冲区域内的建筑。本次数据发布包含一个压缩Shapefile文件，内含所有数字化建筑的点位，字段包括野火ID、起火日期、火灾后建筑状况（烧毁或未烧毁）以及暴露类型（建筑相对于火场边界的位置）。同时提供包含野火边界的Shapefile文件，以及汇总每场野火暴露和损毁建筑数量的CSV表格。该综合数据集提供了对野火建筑损失及暴露情况的...

This geospatial dataset maps point locations of all buildings that were destroyed by or exposed to destructive wildfires from 2002-2022. The data include all wildfire events in the conterminous United States where at least 10 buildings were destroyed (n=362). Using a public building footprint dataset from Microsoft (Bing Maps Team, 2018), we overlaid buildings with high-resolution pre- and post-fire aerial imagery to classify them as burned or unburned. We included all buildings within wildfire perimeters, as well as within a 2.4-km buffer outside the perimeter (representing the definition of the wildland-urban interface). We mapped building loss for 228 fires from 2014-2022 and incorporated existing building destruction data for 2002-2013 (Alexandre et al., 2016; Kramer et al., 2018) and for a few large fires from 2014-2018 (Caggiano et al., 2020). The 2002-2013 data were filtered to match our criterion of 10 or more destroyed buildings per fire and quality-checked by comparing destroyed building counts with incident report totals. We also added buildings within the 2.4-km buffer area outside fire perimeters using Microsoft building points, after comparing points to historical aerial imagery to remove developments that were not present at the time of fire. This data release includes a zipped shapefile containing point locations of all digitized buildings, with fields providing wildfire IDs, ignition dates, post-fire building condition (burned or unburned), and exposure type (building location relative to the fire perimeter). We also include a shapefile containing wildfire perimeters and a CSV table summarizing numbers of exposed and destroyed buildings for each fire. The combined dataset provides the first spatially precise assessment of wildfire building losses and exposure across the entire conterminous United States that allows for analysis of destruction trends over two decades. Alexandre, P. M., Stewart, S. I., Keuler, N. S., Clayton, M. K., Mockrin, M. H., Bar-Massada, A., Syphard, A. D., & Radeloff, V. C. (2016). Factors related to building loss due to wildfires in the conterminous United States. Ecological Applications, 26(7), 2323–2338. https://doi.org/10.1002/eap.1376 Bing Maps Team. (2018). Microsoft Building Footprints [Geospatial data]. Microsoft Planetary Computer. https://planetarycomputer.microsoft.com/dataset/ms-buildings Caggiano, M. D., Hawbaker, T. J., Gannon, B. M., & Hoffman, C. M. (2020). Building loss in WUI disasters: evaluating the core components of the wildland–urban interface definition. Fire, 3(4), 73. https://doi.org/10.3390/fire3040073 Kramer, H. A., Mockrin, M. H., Alexandre, P. M., Stewart, S. I., & Radeloff, V. C. (2018). Where wildfires destroy buildings in the US relative to the wildland–urban interface and national fire outreach programs. International Journal of Wildland Fire, 27(5), 329-341. https://doi.org/10.1071/WF17135