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SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101921 Titre : SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Wenting Luo, Auteur ; Anqi Lin, Auteur ; Fanghua Hao, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Lanfa Liu, Auteur ; Yan Li, Auteur Année de publication : 2023 Projets : 1-Pas de projet / Article en page(s) : n° 101921 Note générale : Bibliographie This work was supported by the National Natural Science Foundation of China [42201468, 42071358], Postdoctoral Innovation Talents Support Program of China [BX20220128], China Postdoctoral Science Foundation [2022M721283] and Fundamental Research Funds for the Central Universities [CCNU22QN018]. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multicritère [Termes IGN] apprentissage automatique [Termes IGN] boosting adapté [Termes IGN] cartographie urbaine [Termes IGN] Chine [Termes IGN] détection du bâti [Termes IGN] données localisées des bénévoles [Termes IGN] image à très haute résolution [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] point d'intérêt [Termes IGN] représentation spatiale [Termes IGN] zone urbaine Résumé : (auteur) Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks. Numéro de notice : A2023-125 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101921 Date de publication en ligne : 06/12/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102504 [article]

Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions / Yan Li in Scientific reports, vol 7 (2017)  

Public [article]  inScientific reports > vol 7 (2017) Titre : Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions Type de document : Article/Communication Auteurs : Yan Li, Auteur ; Damien Sulla-Menashe, Auteur ; Safa Motesharrei, Auteur ; et al., Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique [Termes IGN] Chine [Termes IGN] cohérence des données [Termes IGN] estimation statistique [Termes IGN] image Landsat [Termes IGN] image Terra-MODIS [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The Chinese National Forest Inventory (NFI) has reported increased forest coverage in China since 2000, however, the new satellite-based dataset Global Forest Change (GFC) finds decreased forest coverage. In this study, four satellite datasets are used to investigate this discrepancy in forest cover change estimates in China between 2000 and 2013: forest cover change estimated from MODIS Normalized Burn Ratio (NBR), existing MODIS Land Cover (LC) and Vegetation Continuous Fields (VCF) products, and the Landsat-based GFC. Among these satellite datasets, forest loss shows much better agreement in terms of total change area and spatial pattern than do forest gain. The net changes in forest cover as a proportion of China’s land area varied widely from increases of 1.56% in NBR, 1.93% in VCF, and 3.40% in LC to a decline of −0.40% in GFC. The magnitude of net forest increase derived from MODIS datasets (1.56–3.40%) is lower than that reported in NFI (3.41%). Algorithm parameters, different spatial resolutions, and inconsistent forest definitions could be important sources of the discrepancies. Although several MODIS datasets support an overall forest increase in China, the direction and magnitude of net forest change is still unknown due to the large uncertainties in satellite-derived estimates. Numéro de notice : A2017-781 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1038/s41598-017-07732-5 En ligne : http://dx.doi.org/10.1038/s41598-017-07732-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89005 [article]