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Benchmarking Open-Access Building Footprints: A Multi-Dimensional Assessment with High-Fidelity References.

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Title: Benchmarking Open-Access Building Footprints: A Multi-Dimensional Assessment with High-Fidelity References.
Authors: Zhou, Taiqing1 (AUTHOR), Liao, Yifan1,2 (AUTHOR) liaoyifan01@whu.edu.cn, Gan, Wenxiang1,3 (AUTHOR), Chen, Zhijie2,4 (AUTHOR), Li, Chuchu1,2 (AUTHOR), Zhao, Geyi2 (AUTHOR), Chen, Qi2,3 (AUTHOR), Xue, Qian4 (AUTHOR), Tao, Pengjie1 (AUTHOR)
Source: Remote Sensing. Sep2026, Vol. 18 Issue 17, p2895. 31p.
Subjects: Data quality, Geospatial data, Evaluation methodology, Urban research, Statistical accuracy
Abstract: Highlights: What are the main findings? Open-access building footprint datasets generally underestimate building count and area; object recovery and spatial agreement remain limited, while matched-pair morphology fidelity varies substantially. No product led all three tiers: GABLE ranked highest in statistical consistency, Microsoft Global ML Building Footprints in object recovery and spatial fidelity, and Google Research Open Buildings in matched-pair morphology fidelity within the evaluated coverage. What are the implications of the main findings? Local validation is advisable when reliable completeness, positional accuracy, or boundary fidelity is required. Tier-specific rankings support selection of the most suitable available product for a given region and application. The rapid proliferation of open-access vector building footprint datasets has created substantial opportunities for large-scale urban analysis, yet their reliability remains difficult to assess effectively and consistently. This challenge arises primarily from the scarcity of large-scale, high-fidelity reference benchmarks across diverse urban environments. To address these limitations, we construct a benchmark-grade reference dataset comprising over 200,000 manually annotated building footprints across 10 purposively selected cities across diverse global regions through an exhaustive curation campaign and strict manual-automated quality validation. Using this dataset, we develop a three-tier evaluation framework that assesses statistical consistency, object recovery and spatial accuracy, and morphology fidelity among matched building pairs. Evaluating 11 mainstream datasets reveals pervasive underestimation in both building area (average bias: −14.9%) and count (−34.0%). Average city-level intersection-over-union values range from 29.1% to 58.3%, indicating low-to-moderate spatial overlap. For matched building pairs, the datasets demonstrate relatively high geometric fidelity, with morphological similarity scores of 0.495 to 0.803. Pronounced regional performance variations indicate that no single dataset is universally optimal. Specifically, Microsoft Global ML Building Footprints offers the most balanced multi-city performance, Google Research Open Buildings is recommended for boundary shape fidelity, and regional datasets remain competitive for local analyses. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Highlights: What are the main findings? Open-access building footprint datasets generally underestimate building count and area; object recovery and spatial agreement remain limited, while matched-pair morphology fidelity varies substantially. No product led all three tiers: GABLE ranked highest in statistical consistency, Microsoft Global ML Building Footprints in object recovery and spatial fidelity, and Google Research Open Buildings in matched-pair morphology fidelity within the evaluated coverage. What are the implications of the main findings? Local validation is advisable when reliable completeness, positional accuracy, or boundary fidelity is required. Tier-specific rankings support selection of the most suitable available product for a given region and application. The rapid proliferation of open-access vector building footprint datasets has created substantial opportunities for large-scale urban analysis, yet their reliability remains difficult to assess effectively and consistently. This challenge arises primarily from the scarcity of large-scale, high-fidelity reference benchmarks across diverse urban environments. To address these limitations, we construct a benchmark-grade reference dataset comprising over 200,000 manually annotated building footprints across 10 purposively selected cities across diverse global regions through an exhaustive curation campaign and strict manual-automated quality validation. Using this dataset, we develop a three-tier evaluation framework that assesses statistical consistency, object recovery and spatial accuracy, and morphology fidelity among matched building pairs. Evaluating 11 mainstream datasets reveals pervasive underestimation in both building area (average bias: −14.9%) and count (−34.0%). Average city-level intersection-over-union values range from 29.1% to 58.3%, indicating low-to-moderate spatial overlap. For matched building pairs, the datasets demonstrate relatively high geometric fidelity, with morphological similarity scores of 0.495 to 0.803. Pronounced regional performance variations indicate that no single dataset is universally optimal. Specifically, Microsoft Global ML Building Footprints offers the most balanced multi-city performance, Google Research Open Buildings is recommended for boundary shape fidelity, and regional datasets remain competitive for local analyses. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Evaluation methodology
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      – SubjectFull: Urban research
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      – SubjectFull: Statistical accuracy
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              Text: Sep2026
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