Abstract. Artificial impervious surface area (ISA) documents the human footprint. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change studies and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA datasets at a 10 m resolution are still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and crowdsourced OpenStreetMap (OSM) data, 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. We then produced a 10 m resolution global ISA dataset (GISA-10m) from over 2.7 million Sentinel optical and radar images on the Google Earth Engine platform. Based on test samples that are independent of the training set, GISA-10m achieves an overall accuracy of greater than 86 %. In addition, the GISA-10m dataset was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was confirmed. The global road area was further investigated, courtesy of this 10 m dataset. It was found that China and the US have the
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