Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

中文翻译：

---

使用信念函数基于OpenStreetMap和Sentinel-2图像的公共城市绿地地图

城市公共绿地对​​于城市生活质量至关重要。但是，大多数城市尚无法提供有关城市绿地的全面开放数据集。作为开放的和全球可用的数据集，Sentinel-2卫星图像和OpenStreetMap（OSM）数据在城市绿色空间制图中的潜力很高，但由于它们各自的不确定性而受到限制。Sentinel-2图像无法将公共绿地与私人绿地区分开，其10 m的空间分辨率无法捕获细粒度的城市结构，而在OSM中，绿地并非始终如一地映射，并且处处具有相同的完整性水平。为了解决这些限制，我们建议在明确考虑其不确定性的情况下融合这些数据集。使用Dempster-Shafer理论将Sentinel-2衍生的归一化差异植被指数与OSM数据融合，以增强对小植被区域的检测。公共和私人绿地之间的区别是使用贝叶斯分层模型和OSM数据实现的。该分析是根据OSM数据得出的土地使用地块进行的，并在德国德累斯顿市进行了测试。最终城市公共绿地地图的整体准确性为95％，主要受公共可及性模型的不确定性影响。