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Building up a data engine for global urban mapping
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DOI：10.1016/j.rse.2024.114242.png)
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Abstract
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Global urban mapping is vital for understanding various environmental challenges and supporting Sustainable Development Goal 11. Although deep learning models present a potential unified solution, their effectiveness is intrinsically tied to the quality and diversity of the training data, which often present limitations in existing research. To overcome these limitations, this paper introduced a data engine tailored to generate high-quality and diverse training samples at the global scale. This semi-automatic procedure operated in two stages. The initial stage focused on the generation of globally-distributed accurate samples by harmonizing existing opensource datasets. The subsequent stage broadened the sample coverage to the global scale by leveraging published global data products and OpenStreetMap data, ensuring the sample's diversity. Using the dataset generated by the data engine, we trained a Global Urban Mapper (GUM), achieving superior global testing results, outperforming the second-best product (i.e., GISA-10) by 2.89% in Overall Accuracy (OA) and 5.92% in mean Intersection over Union (mIoU). The advancements can primarily be ascribed to the superior quality and heterogeneity of the data generated by the proposed data engine, providing a precise and diverse set of samples for the deep learning model to assimilate. The proposed data engine, built exclusively on open-source data, offers promising prospects for global mapping tasks beyond urban land cover. We will release GUM and the associated preprocessing code in https://github.com/LauraChow77/GlobalUrbanMapper, which will empower users to map specific areas of interest worldwide, thereby facilitating timely urban assessment and monitoring.
Keywords:
Urban land cover
Semantic segmentation
Data-centric AI
Global urban mapper (GUM)
Sustainable development goal 11
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