Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning

被引:37
作者
Li, Hao [1 ]
Zech, Johannes [1 ]
Ludwig, Christina [1 ]
Fendrich, Sascha [2 ]
Shapiro, Aurelie [3 ]
Schultz, Michael [1 ]
Zipf, Alexander [1 ,2 ]
机构
[1] Heidelberg Univ, Inst Geog, GISci Chair, D-69120 Heidelberg, Germany
[2] Heidelberg Univ, HeiGIT, Schloss Wolfsbrunnenweg 33, D-69118 Heidelberg, Germany
[3] United Nations FAO, Food & Agr Org, Viale Terme di Caracalla, I-00153 Rome, Italy
关键词
Volunteered geographical information; Inland surface water; SDG; 6; Copernicus; Deep learning; OpenStreetMap; Superpixel; HIGH-SPATIAL-RESOLUTION; TIME-SERIES; LAND-COVER; INDEX NDWI; CLASSIFICATION; EXTRACTION; ACCURACY; IMAGE; WETLANDS; QUALITY;
D O I
10.1016/j.jag.2021.102571
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning (ML). Highresolution maps of inland surface water bodies are important for water supply and natural disaster mitigation as well as for monitoring, managing, and preserving landscapes and ecosystems. In this paper, we propose an automatic surface water mapping workflow by training a deep residual neural network (ResNet) based on OpenStreetMap (OSM) data and Sentinel-2 multispectral data, where the Simple Non-Iterative Clustering (SNIC) superpixel algorithm was employed for generating object-based training samples. As a case study, we produced an open surface water layer for Germany using a national ResNet model at a 10 m spatial resolution, which was then harmonized with OSM data for final surface water products. Moreover, we evaluated the mapping accuracy of our open water products via conducting expert validation campaigns, and comparing to existing water products, namely the WasserBLIcK and Global Surface Water Layer (GSWL). Using 4,600 validation samples in Germany, the proposed model (ResNet+SNIC) achieved an overall accuracy of 86.32% and competitive detection rates over the WasserBLIcK (87.47%) and GSWL (98.61%). This study provides comprehensive insights into how to best explore the synergy of VGI and ML of EO data in a large-scale surface water mapping task.
引用
收藏
页数:16
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