ABSTRACT

The identification of buildings has become a major research focus of settlement mapping with Earth Observation data. Building area or building footprint data is particularly required in research related to population, such as disaster risk management or urban health. This study examined the suitability of machine learning regression-based unmixing for quantifying the pixel-wise share of building area with decametre resolution Copernicus Sentinel-1 and Sentinel-2 imagery. Compared to using a single-step approach directly estimating building area, leading to an over-estimation of building area compared to non-building impervious surface area due to feature similarity, the introduction of a hierarchical approach considerably improved mapping results. While the original mapping resolution was 10 m, we found that building area was most accurately mapped starting at a spatial resolution of 100 m – a resolution well suitable for many urban applications. The proposed approach is widely transferable in space as it used spatially robust spectral-temporal metrics from time series imagery and as its requirements for training data are very limited.

摘要

建筑物的识别已成为利用地球观测数据进行聚落测绘的主要研究热点。建筑面积或建筑足迹数据在与人口相关的研究中尤其需要，例如灾害风险管理或城市健康。本研究检查了基于机器学习回归的解混合是否适用于以分米分辨率的哥白尼 Sentinel-1 和 Sentinel-2 图像量化建筑面积的像素份额。与使用单步法直接估计建筑面积相比，由于特征相似性导致建筑面积与非建筑不透水表面积相比高估，分层方法的引入显着改善了映射结果。虽然原始映射分辨率为 10 m，但 我们发现，从 100 m 的空间分辨率开始最准确地绘制建筑面积图——该分辨率非常适合许多城市应用。所提出的方法可以在空间中广泛转移，因为它使用了来自时间序列图像的空间鲁棒的光谱时间度量，并且它对训练数据的要求非常有限。