全球建筑面积估算产品：它们的准确性如何？
Global Building Area Estimation Products: How Accurate Are They?
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中文总结 AI 辅助
研究对全球四种主要建筑产品（GHSL、TEMPO、GBA和Overture）准确性进行评估，以ORBITaL-Net为真值，在多分辨率网格及传统指标上评测，结果显示GBA或TEMPO总体准确率最高，产品准确性因地理位置、人口密度和收入群体等因素有显著差异。
中文摘要 AI 辅助
地理空间的建筑占地面积栅格对于诸如监测城市化、提高能源效率和追踪温室气体排放等各种任务很有用。现在有多个全球建筑栅格数据集，但缺乏对其准确性的独立、全面和公平评估。在这项工作中，我们评估了四种主要全球建筑产品的准确性：全球人类住区层（GHSL）、微软的TEMPO（TEMPO）、全球建筑地图集（GBA）和序曲（Overture）。作为评估其准确性的地面真值，我们使用ORBITaL-Net，这是一个全球多样化的手动标注建筑占地面积数据集。为确保公平性，我们在多个空间分辨率的网格上以及几个传统性能指标上评估产品。我们的结果表明，根据特定评估标准，GBA或TEMPO通常总体准确率最高。我们还按地理位置、人口密度和收入群体等几个因素对每个产品的准确性进行分层。结果表明，产品准确性有时会因这些因素而有显著差异。值得注意的是，所有产品在非洲和亚洲的准确性明显较低。大多数产品在高密度城市地区的准确性也大幅下降。
英文摘要
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.
发表机构
- University of Missouri（密苏里大学）
- Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。
