FOTBCD：基於法國正射影像與地形資料的大規模建築物變更檢測基準數據集

FOTBCD: A Large-Scale Building Change Detection Benchmark from French Orthophotos and Topographic Data

January 30, 2026

作者: Abdelrrahman Moubane

cs.AI

摘要

我们推出FOTBCD——一个基于法国国家地理与林业信息研究所（IGN France）权威正射影像与地形建筑数据构建的大规模建筑变化检测数据集。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，三个地理隔离的省份留作评估。该数据集以0.2米/像素的分辨率涵盖城市、郊区及乡村等多种环境。我们公开发布FOTBCD-Binary数据集，包含约2.8万组前后时相图像对及像素级二元建筑变化标注，每组数据均附带图块级空间元数据。该数据集专为地理域偏移下的大规模基准测试而设计，其验证集与测试集样本均来自预留省份，并经过人工核验以确保标注质量。此外，我们同步公开FOTBCD-Instances实例级标注子集，包含数千组图像对，完整展示了FOTBCD全实例级版本的标注体系。通过固定参考基线，我们将FOTBCD-Binary与LEVIR-CD+、WHU-CD进行基准测试，有力证实了数据集层面的地理多样性能够提升建筑变化检测的跨域泛化能力。

English

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.