Built Area Ratio (BAR) for 211 Cities in France at 100m Resolution

This dataset supports a systematic comparative analysis of global building footprint datasets for urban applications. The central research hypothesis is that despite increasing availability of open-access building footprint data, significant variation exists in spatial coverage and accuracy across products, particularly when analyzed at fine spatial resolutions relevant to environmental modeling and urban planning. Specifically, the study evaluates how three widely used global building datasets, JRC Digital Building Stock Model (DBSM), Microsoft Bing Global Building Footprints, and EUBUCCO compare against France’s authoritative BD TOPO dataset in terms of built-up area representation. To operationalize this comparison, Built Area Ratio (BAR)—defined as the total building footprint area divided by the total land area of each grid cell—was calculated for each dataset within a uniform 100 × 100 m grid covering 211 Cities mainland France. This resolution was chosen as it balances spatial granularity with computational efficiency and is commonly used in impervious surface estimation and urban density modeling. The dataset includes derived BAR values for each Grid cell within these cities.本数据集可支撑面向城市应用场景的全球建筑基底数据集（building footprint dataset）系统性对比分析。本研究的核心假设为：尽管开放获取的建筑基底数据日益丰富，但各数据集在空间覆盖范围与精度上仍存在显著差异，尤其在与环境建模、城市规划相关的精细空间分辨率下开展分析时，该差异尤为突出。 具体而言，本研究评估了三款主流全球建筑数据集——JRC数字建筑存量模型（DBSM）、微软必应全球建筑基底数据集、EUBUCCO——与法国权威BD TOPO数据集在建成区面积表征效果上的差异。 为开展该对比研究，本研究在覆盖法国本土211座城市的统一100×100米网格内，针对各数据集计算了建筑面积比（Built Area Ratio, BAR），其定义为单个网格单元内总建筑基底面积与总土地面积的比值。该分辨率的选取兼顾了空间精细度与计算效率，亦是不透水面估算、城市密度建模领域的常用分辨率。 本数据集包含上述城市内每个网格单元的衍生建筑面积比数值。