Refined Urban Informal Settlements’ Mapping at Agglomeration Scale With the Guidance of Background Knowledge From Easy-Accessed Crowdsourced Geospatial Data

Urban Informal Settlements (UIS) denote densely populated locales characterized by inadequate urban infrastructure standards, often exhibiting an amalgamation of rural and urban attributes, primarily situated within the confines of major cities or metropolitan regions. UIS mapping is a typical task that aims to identify pixels corresponding to urban informal settlements in remote sensing images. The extremely similar visual characteristics, sample uncertainty, and highly manual costs bring large-scale UIS mapping noteworthy challenges. In this paper, we propose a sample uncertainty-aware semi-supervised learning method guided by background-knowledge from crowdsourced geospatial data (namely SemiUIS) for UIS mapping and produces a 1-meter resolution UIS map in the Urban Agglomeration in the Middle Reaches of the Yangtze River, China (UAMRYR). Our proposed SemiUIS method integrates semisupervised learning with the guidance of background knowledge from crowdsourcing data to reduce the uncertainty of the generated pseudo-semantic segmentation samples to improve the quality of the constructed data set by jointly optimizing the background (Not UIS areas) and the foreground (UIS areas). In contrast, traditional semi-supervised learning methods only focus on explicitly optimizing the foreground (UIS areas) in the pseudo-sample generation process. Experiments were conducted in the 31 prefecture-level cities of UAMRYR. Visual interpretation of labeled remote sensing samples, street view images and two novel knowledge validation indicators, Global Knowledge Precision (GKP) and Local Knowledge Precision (LKP) are proposed to verify the UIS mapping results. The proposed method reached an overall accuracy (OA) of 90.58 %, mean intersection over union (mIoU) of 75.88 %, Accuracy (Acc) of 78.77 %, Intersection over Union (IoU) of 62.97 %, GKP of 90.33 %, and LKP of 85.01 %, respectively with only 36 (5%) training samples. This work will be available at https://github.com/RunyuFan/UisYangtze.