Global Building Area Estimation Products: How Accurate Are They?

2026-02-18 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, extended

Summary

A comprehensive evaluation assesses the accuracy of four major global building area estimation products: Global Human Settlement Layer (GHSL), Microsoft's TEMPO, The Global Building Atlas (GBA), and Overture. Utilizing ORBITaL-Net, a globally diverse dataset of 1.49 million manually labeled building footprints from 0.47m resolution imagery (2010-2020, median 2017), the study rigorously benchmarked products across multiple spatial resolutions (e.g., 100m, 77m grids) and performance metrics like MAE, WMAPE, and R^2. Results indicate GBA or TEMPO generally achieve the highest overall accuracy; GBA excels in MAE and WMAPE, while TEMPO leads in R^2 and exhibits the least bias. GHSL consistently shows the highest error rates, notably overestimating building area by +131% in Africa. All products demonstrate significantly reduced accuracy in Africa, Asia, and high-density urban areas. TEMPO proved most stable across geographic, population density, and income stratifications.

Key takeaway

For data scientists or urban planners selecting global building area datasets, understand that product accuracy is highly context-dependent. If your application requires precise area estimates with minimal large errors, TEMPO is generally more stable, especially across diverse regions and population densities. However, if minimizing average absolute error is critical, GBA may be preferable. Always account for significant accuracy degradation in Africa, Asia, and high-density urban areas, and verify temporal consistency with your analysis period.

Key insights

Independent evaluation reveals global building area product accuracy varies significantly by region and methodology.

Principles

• Ground truth quality dictates evaluation robustness.
• Reprojection introduces systematic error in raster data.
• Accuracy varies by geography, density, and income.

Method

Evaluate building area products using high-resolution, manually labeled ground truth (ORBITaL-Net) across multiple reference grids and stratified by geographic, population, and income factors.

In practice

• Prioritize GBA for MAE/WMAPE, TEMPO for R^2 and bias.
• Anticipate lower accuracy in Africa, Asia, and dense urban areas.
• Consider temporal alignment between product and ground truth.

Topics

• Geospatial Data
• Building Footprints
• Data Accuracy
• Remote Sensing
• Performance Benchmarking
• Urban Planning

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Data Scientist

Related on AIssential

• Global Building Area Estimation Products: How Accurate Are They? — Global building area products vary significantly in accuracy, especially across geographies and population densities.
• Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types — A framework quantifies seasonal and environmental impacts on Sentinel-2 building detection, guiding robust model selection.
• Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events — Satellite flood mapping accuracy, even with advanced models like Prithvi-EO-2.0, is critically governed by land cover, flood type, and reference data consistency.
• EXCLUSIVE: India's Varya Gives Every Creator Studio-Grade Video AI - At 48 Paisa Per Second — Varya provides culturally nuanced, affordable AI video generation by training on extensive Indian-specific datasets.
• Global Building Area Estimation Products: How Accurate Are They? — Global building area product accuracy varies significantly by region and density, with no single universally superior option.
• GPT Capability in Understanding Coordinates: How GPT-5.4 Transforms Spatial Precision — GPT-5.4 significantly improves spatial coordinate understanding, achieving near-perfect accuracy and consistency without extensive prompt engineering.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.