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This study conducts a comprehensive evaluation of the accuracy of four major global building footprint datasets—GHSL, TEMPO, GBA, and Overture—using ORBITaL-Net as a ground truth reference. By employing multiple spatial resolutions and conventional performance metrics, the authors reveal that GBA and TEMPO generally outperform the others, although accuracy varies significantly based on geographic location, population density, and income levels. The findings highlight critical discrepancies in accuracy, particularly in Africa and Asia, and in high-density urban areas, underscoring the need for improved data quality in these regions.
GBA and TEMPO emerge as the most accurate global building footprint datasets, but their performance is highly variable across different regions and urban densities.
Geo-spatial rasters of building footprint area are useful for a variety of tasks, such as monitoring urbanization, improving energy efficiency, and tracking greenhouse gas emissions. There are now multiple global building raster datasets, however there lacks an independent, comprehensive, and fair assessment of their accuracy. In this work, we evaluate the accuracy of four major global building products: Global Human Settlement Layer (GHSL), Microsoft's TEMPO (TEMPO), The Global Building Atlas (GBA), and Overture. As ground truth for assessing their accuracy, we use ORBITaL-Net, a globally diverse dataset of manually labeled building footprints. To ensure fairness, we evaluate products on grids of multiple spatial resolutions, and several conventional performance metrics. Our results indicate that either GBA or TEMPO generally achieves the highest overall accuracy, depending upon the particular evaluation criteria. We also stratify the accuracy of each product by several factors: geographic location, population density, and income groups. The results reveal that product accuracy can sometimes vary significantly with respect to these factors. Notably, all products are significantly less accurate in Africa and Asia. Most products also suffer significant accuracy reduction in high-density urban areas.