论文标题
二元建筑表面层的比例敏感，空间上明确的精度评估的框架
A framework for scale-sensitive, spatially explicit accuracy assessment of binary built-up surface layers
论文作者
论文摘要
为了更好地了解人类定居点的动态，对地理空间构建表面数据集中不确定性的透彻了解至关重要。尽管已经提出了用于分类网格数据局部精度评估的框架，以说明分类准确性的空间非平稳性，但此类方法尚未应用于（二进制）构建的土地数据。这些数据与其他数据（例如土地覆盖数据）有所不同，因为在农村城市连续体之间建立的表面密度有很大变化，从而导致了类不平衡的转换，从而导致基于小的基础样本大小的少量混淆矩阵。在本文中，我们旨在通过测试共同的一致性措施的适用性和合理性来填补这一空白，以衡量积累表面数据的局部准确性。我们研究了局部准确性对评估支持以及分析单位的敏感性，并分析了跨越农村城市轨迹的局部准确性与建筑区域的密度 /结构相关特性之间的关系。我们的实验基于多个时空的全球人类沉降层（GHSL）和马萨诸塞州（美国）的参考数据库。我们发现，在常用一致性措施中，适合性的差异很大，对评估支持的敏感性有所不同。然后，我们应用框架来评估从1975年到2014年的局部GHSL数据准确性。除了提高沿农村城市梯度的准确性，我们发现准确性通常会随着时间的推移而提高，这主要是由于我们研究区域的城市周期致密化过程所驱动的。此外，我们发现，由于GHSL Epoch 1975年的GHSL误差较高，因此从GHSL得出的局部致密度量倾向于高估发生在1975年至2014年之间的城市周期性致密过程。
To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.
