基于深度学习的美国高分辨率建成区数据的空间显式精度评估
Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States
AI总结：
本研究评估了GHS-BUILT-S2概率与参考建成区比例的关系，确定0.5为最优二值化阈值，并通过空间显式评估证实基于深度学习的建成区数据在城乡均具有高精度。
摘要：
通过机器学习方法从遥感数据中得出的地理空间数据集，通常基于抽象性质的概率输出，这些输出难以转化为可解释的度量。例如，全球人类居住区图层GHS-BUILT-S2产品报告了2018年在全球10米×10米网格中建成区存在的概率。然而，从业者通常需要可解释的度量，例如指示建成区存在或不存在的二元表面，或亚像素建成区表面比例的估计。在此，我们评估了GHS-BUILT-S2中建成区概率与美国多个地区从高度可靠的参考数据库得出的参考建成区表面比例之间的关系。此外，我们使用一致性最大化方法确定了一个二值化阈值，该阈值从这些建成区概率中创建二元建成区土地数据。这些二元表面作为空间显式、尺度敏感的精度评估的输入，该评估包括使用一种新颖的视觉分析工具，我们称之为焦点精确率-召回率特征图。我们的分析表明，应用于GHS-BUILT-S2的0.5阈值最大化了与从参考建成区面积比例得出的二值化建成区土地数据的一致性。我们发现，在得出的建成区中，精度水平较高（即县级F-1分数平均接近0.8），并且在我们研究区域的城乡梯度上，精度始终较高。这些结果揭示了基于Sentinel-2数据和深度学习的居住区模型在精度上的显著改进，与早期基于Landsat的全球人类居住区图层版本相比，在乡村和城市地区均如此。
英文摘要：
Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m x 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, in both rural and urban areas, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.
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