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Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)  

Public [article]  inRemote sensing of environment > vol 278 (September 2022) . - n° 113088 Titre : Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression Type de document : Article/Communication Auteurs : Haoyu Wang, Auteur ; Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113088 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] apprentissage profond [Termes IGN] carte d'occupation du sol [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] croissance urbaine [Termes IGN] image Terra-MODIS [Termes IGN] modèle de régression [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (auteur) Global urbanization changes land cover patterns and affects the living environment of humans. However, urbanization and its evolution process, i.e., conversions among diverse land covers, are hard to measure, as existing land cover maps usually have low temporal resolutions; conversely, long-term and temporally dense land cover maps, such as vegetation-impervious-soil decomposition maps base on MODIS, ignore the important land cover of cropland in urban evolution process (UEP). To resolve the issue, this study suggests a novel model named time-extended non-crop vegetation-impervious-cropland (Time V-I-C) to represent and quantify different stages of UEP; then, a normalized multi-objective T-ConvLSTM (NMT) method is proposed to unmix cropland, non-crop vegetation, and impervious based on the intra-annual remotely-sensed time series, and obtain their fractions in each pixel for generating UEP maps. Consequently, UEP maps from 2001 to 2018 are generated for two Chinese urban agglomerations, i.e., Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. The mapping results have high accuracies with a small standard error of regression (SER) of 13.1%, small root mean square error (RMSE) of 12.6%, and small mean absolute error (MAE) of 8.4%, and the maps reveal the different UEP in the two urban agglomerations. Therefore, this study provides a new idea for expressing UEP and contributes to a wide range of urbanization studies and sustainable city development. Numéro de notice : A2022-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.rse.2022.113088 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113088 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101049 [article]