{"title":"Building up a data engine for global urban mapping","authors":"Yuhan Zhou , Qihao Weng","doi":"10.1016/j.rse.2024.114242","DOIUrl":null,"url":null,"abstract":"<div><p>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 open-source 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 <span>https://github.com/LauraChow77/GlobalUrbanMapper</span><svg><path></path></svg>, which will empower users to map specific areas of interest worldwide, thereby facilitating timely urban assessment and monitoring.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724002608/pdfft?md5=dcc3beb49c0bb8e6364869c6b4c08920&pid=1-s2.0-S0034425724002608-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724002608","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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 open-source 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.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.