Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities

Abstract Reliable urban land-use maps are essential for urban analysis because the spatial distribution of land use reflects the complex environment of cities under the combined effects of nature and socio-economics. In recent years, very high resolution (VHR) remote sensing imagery interpretation has resolved the “semantic gap” between the low-level data and the high-level semantic scenes, and has been used to map urban land use. Nevertheless, the existing frameworks cannot easily be applied to practical urban analysis, which can be attributed to three main reasons: 1) the indistinguishable socio-economic attributes of the same ground object layouts; 2) the weak transferability of the supervised frameworks and the time-consuming training sample annotation; and 3) the category system inconsistency between the data source and the urban land-use application. In this paper, to achieve an “application gap” breakthrough for urban land-use mapping, a data-driven point, line, and polygon semantic object mapping (PLPSOM) framework is proposed, which makes full use of open-source VHR images and multi-source geospatial data. In the PLPSOM framework, point, line, and polygon semantic objects are represented by the points of interest (POIs), OpenStreetMap (OSM) data, and VHR images corresponding to the scenes in the land-use mapping units, respectively. OSM line semantic objects are utilized to supply the boundaries of the land-use mapping units for the POIs and VHR images, forming urban land parcels (street blocks). To reduce the cost of the data annotation, the training dataset is constructed using multiple open-source data sources. An enhanced deep adaptation network (EDAN) is then proposed to acquire the categories of the VHR scene images in the case of partial transfer learning. Finally, in order to meet the actual needs, a rule-based category mapping (RCM) model is applied to integrate the categories of the POIs and VHR images into the urban land-use category system, allowing us to acquire the land-use maps of the cities. The effectiveness of the proposed method was tested in four cities of China, including six specific areas: Beijing and Wuhan city centers; the Hanyang District of Wuhan; the Hannan District of Wuhan; Macao; and the Wan Chai area of Hong Kong, achieving a high classification accuracy. The “urban image” analysis confirmed the practicality of the obtained urban land-use maps.