荒野-城市交界区（WUI）地图用于识别具有野火风险的区域，但由于缺乏建筑数据，这些地图往往已过时。卷积神经网络（CNN）可以从遥感数据中提取建筑位置，但其在 WUI 区域的准确性尚不明确。此外，CNN 计算密集且技术复杂，使得终端用户（如使用或创建 WUI 地图的人员）难以应用。本研究针对加州的 Camp、Tubbs 和 Woolsey 三场野火，识别了火灾前后的建筑并估算了建筑损毁情况。我们使用了 Esri 提供的 CNN 模型，从高分辨率影像中检测建筑。该数据集代表了当前可用于潜在 WUI 制图的最先进水平。

Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex making it challenging for end-users, such as those who use or create WUI maps, to apply. We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs, and Woolsey. We used a CNN model from Esri to detect buildings from high-resolution imagery. This dataset represents the state-of-the-art of what is readily available for potential WUI mapping.