We introduce FOTBCD, a large-scale building change detection dataset derived from authoritative French orthophotos and topographic building data provided by IGN France. Unlike existing benchmarks that are geographically constrained to single cities or limited regions, FOTBCD spans 28 departments across mainland France, with 25 used for training and three geographically disjoint departments held out for evaluation. The dataset covers diverse urban, suburban, and rural environments at 0.2m/pixel resolution. We publicly release FOTBCD-Binary, a dataset comprising approximately 28,000 before/after image pairs with pixel-wise binary building change masks, each associated with patch-level spatial metadata. The dataset is designed for large-scale benchmarking and evaluation under geographic domain shift, with validation and test samples drawn from held-out departments and manually verified to ensure label quality. In addition, we publicly release FOTBCD-Instances, a publicly available instance-level annotated subset comprising several thousand image pairs, which illustrates the complete annotation schema used in the full instance-level version of FOTBCD. Using a fixed reference baseline, we benchmark FOTBCD-Binary against LEVIR-CD+ and WHU-CD, providing strong empirical evidence that geographic diversity at the dataset level is associated with improved cross-domain generalization in building change detection.

翻译：我们提出了FOTBCD，这是一个基于法国国家地理与林业信息研究所（IGN France）提供的权威正射影像与地形建筑物数据构建的大规模建筑物变化检测数据集。与现有局限于单一城市或有限区域的基准数据集不同，FOTBCD覆盖法国本土28个省份，其中25个用于训练，另外3个地理上互不关联的省份留作评估。该数据集以0.2米/像素的分辨率涵盖了多样化的城市、郊区和乡村环境。我们公开发布了FOTBCD-Binary，该数据集包含约28,000组前后时相图像对及其像素级二值建筑物变化掩膜，每组数据均附带图块级别的空间元数据。本数据集专为地理域偏移下的大规模基准测试与评估而设计，其验证集和测试集样本均来自预留省份，并经过人工核查以确保标注质量。此外，我们还公开发布了FOTBCD-Instances，这是一个包含数千组图像对的公开实例级标注子集，展示了完整版FOTBCD实例级数据所使用的标注规范。通过固定参考基线，我们将FOTBCD-Binary与LEVIR-CD+和WHU-CD进行基准对比，提供了有力的实证证据，表明数据集层面的地理多样性有助于提升建筑物变化检测的跨域泛化能力。