The localization of non-residential buildings over wide geographical areas is used as input within several contexts such as disaster management, regional and national planning, policy making and evaluation, among others. While the built-up environment has been continuously and globally mapped, given the efforts on producing synoptic land cover information; little attention has been given to the land use component of such built-up. This is due to, for example, difficulties in distinguishing built-up land use in non-commercial satellite imagery (e.g., Sentinel-2, with spatial resolution of up to 10 m), difficulties in collecting training data for supervised classification approaches, and the fact that variations in features of the built-up environment not always translate to a specific land use. This is even more critical when considering nadir viewing satellite or aerial imagery. However, map producers have been addressing this issue. For example, the Copernicus program (European Commission), through their pan-European CORINE Land Cover (CLC), and Urban Atlas restricted to several European metropolitan areas, have been making available land use information of the built-up cover, with 6-year intervals. The Global Human Settlement Layer (Copernicus program) has been providing built-up land use information by distinguishing residential from non-residential built-up since 2023 (GHSL_NRES). Currently these are also provided with a time interval of 5 years. National map producers often provide this information but usually with an interval between editions of several years. In this paper we combine readily available population counts and land cover maps to generate non-residential training labels that can be used to train a Sentinel-2 image segmentation model capable of distinguishing non-residential built-up from the remaining built-up. Leveraging two publicly available datasets, population counts (WorldPop) and built-up land cover (ESA WorldCover), allowed to produce training data from which an image segmentation model was able to learn relevant features to distinguish non-residential areas from other built-up in Sentinel-2 images. The results within a study area of 4 Sentinel-2 tiles shown that it improves the detection of non-residential built-up areas when comparing with CLC and GHSL_NRES (F1-score of 32 %, 25 % and 29 %, respectively), which are the products providing pan-European information regarding the built-up land use. These results indicate that the combination of publicly available geospatial datasets may be used to produce higher quality geospatial information.

中文翻译：

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结合现成的人口和土地覆盖地图，生成非住宅建筑标签以训练 Sentinel-2 影像分割模型


在广泛的地理区域内非住宅建筑的定位被用作多种背景下的输入，例如灾害管理、区域和国家规划、政策制定和评估等。虽然建筑环境已经不断在全球范围内绘制地图，但考虑到在生成天气土地覆盖信息方面的努力;很少关注此类建筑的土地使用部分。例如，这是由于难以在非商业卫星影像中区分建筑用地（例如，Sentinel-2，空间分辨率高达 10 m），难以为监督分类方法收集训练数据，以及建筑环境特征的变化并不总是转化为特定的土地利用。在考虑像底点查看卫星或航空影像时，这一点更为关键。但是，地图制作者一直在解决此问题。例如，哥白尼计划（欧盟委员会）通过其泛欧洲 CORINE 土地覆盖 （CLC） 和仅限于几个欧洲大都市地区的城市地图集，每隔 6 年提供一次建成覆盖物的土地使用信息。自 2023 年以来，全球人类住区图层（哥白尼计划）一直通过区分住宅和非住宅建筑来提供建筑土地利用信息 （GHSL_NRES）。目前，这些也提供 5 年的时间间隔。国家地图制作者通常会提供此信息，但版本之间通常间隔数年。 在本文中，我们将现成的人口计数和土地覆盖地图相结合，生成非住宅训练标签，可用于训练能够区分非住宅建筑和剩余建筑的 Sentinel-2 图像分割模型。利用两个公开可用的数据集，即人口计数 （WorldPop） 和建筑用地覆被 （ESA WorldCover），可以生成训练数据，图像分割模型能够从中学习相关特征，以区分非住宅区与 Sentinel-2 中的其他建筑区图像。4 个 Sentinel-2 瓷砖研究区域内的结果表明，与 CLC 和 GHSL_NRES（F1 分数分别为 32 %、25 % 和 29 %）相比，它提高了对非住宅建筑区的检测，它们是提供有关建筑用地的泛欧洲信息的产品。这些结果表明，公开可用的地理空间数据集的组合可用于生成更高质量的地理空间信息。