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A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area Type de document : Article/Communication Auteurs : Myung-Jin Jun, Auteur Année de publication : 2021 Article en page(s) : pp 2149 - 2167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] arbre de décision
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] Séoul
[Termes IGN] zone urbaineRésumé : (auteur) This study compares the performance of gradient boosting decision tree (GBDT), artificial neural networks (ANNs), and random forests (RF) methods in LUC modeling in the Seoul metropolitan area. The results of this study showed that GBDT and RF have higher predictive power than ANN, indicating that tree-based ensemble methods are an effective technique for LUC prediction. Along with the outstanding predictive performance, the DT-based ensemble models provide insights for understanding which factors drive LUCs in complex urban dynamics with the relative importance and nonlinear marginal effects of predictor variables. The GBDT results indicate that distance to the existing residential site has the highest contribution to urban land use conversion (30.4% of the relative importance), while other significant predictor variables were proximity to industrial and public sites (combined 32.3% of relative importance). New residential development is likely to be adjacent to existing residential sites, but nonresidential development occurs at a distance (about 600 m) from such sites. The distance to the central business district (CBD) had increasing marginal effects on residential land use conversion, while no significant pattern was found for nonresidential land use conversion, indicating that Seoul has experienced more population suburbanization than employment decentralization. Numéro de notice : A2021-756 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887490 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887490 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98771
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2149 - 2167[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Identifying surface urban heat island drivers and their spatial heterogeneity in China’s 281 cities: An empirical study based on multiscale geographically weighted regression / Lu Niu in Remote sensing, vol 13 n° 21 (November-1 2021)
[article]
Titre : Identifying surface urban heat island drivers and their spatial heterogeneity in China’s 281 cities: An empirical study based on multiscale geographically weighted regression Type de document : Article/Communication Auteurs : Lu Niu, Auteur ; Zhengfeng Zhang, Auteur ; Peng Zhong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4428 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse géovisuelle
[Termes IGN] analyse multiéchelle
[Termes IGN] analyse spatio-temporelle
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] échelle géographique
[Termes IGN] hétérogénéité spatiale
[Termes IGN] ilot thermique urbain
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] nuit
[Termes IGN] régression géographiquement pondérée
[Termes IGN] variation diurne
[Termes IGN] variation saisonnière
[Termes IGN] zone urbaineRésumé : (auteur) The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers. Numéro de notice : A2021-821 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13214428 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.3390/rs13214428 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98931
in Remote sensing > vol 13 n° 21 (November-1 2021) . - n° 4428[article]The geography of social media data in urban areas: Representativeness and complementarity / Alvaro Bernabeu-Bautista in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)
[article]
Titre : The geography of social media data in urban areas: Representativeness and complementarity Type de document : Article/Communication Auteurs : Alvaro Bernabeu-Bautista, Auteur ; Leticia Serrano-Estrada, Auteur ; V. Raul Perez-Sanchez, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 747 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse socio-économique
[Termes IGN] analyse spatiale
[Termes IGN] données démographiques
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] données socio-économiques
[Termes IGN] géolocalisation
[Termes IGN] réseau social géodépendant
[Termes IGN] Valence (Espagne)
[Termes IGN] zone urbaineRésumé : (auteur) This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns of geolocated data from various social networks (i.e., Google Places, Foursquare, Twitter, Airbnb and Idealista) and statistical information such as land value, average gross income, and population distribution by age range. The main findings show that there is no direct relationship between land value or age of registered population and the amount of social network data generated in a given area. However, a noteworthy coincidence was observed between Google Places data-clustering patterns, which represent the offer of economic activities, and the spatial concentration of the other LBSNs analyzed, suggesting that data from these sources are mostly generated in areas with a high density of economic activities. Numéro de notice : A2021-828 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10110747 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.3390/ijgi10110747 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98965
in ISPRS International journal of geo-information > vol 10 n° 11 (November 2021) . - n° 747[article]Urban land-use analysis using proximate sensing imagery: a survey / Zhinan Qiao in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : Urban land-use analysis using proximate sensing imagery: a survey Type de document : Article/Communication Auteurs : Zhinan Qiao, Auteur ; Xiaohui Yuan, Auteur Année de publication : 2021 Article en page(s) : pp 2129 - 2148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] image Streetview
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with the Global Positioning System (GPS) greatly proliferates proximate sensing images taken near or on the ground at a close distance to urban targets. Studies leveraging proximate sensing images have demonstrated great potential to address the need for local data in the urban land-use analysis. This paper reviews and summarizes the state-of-the-art methods and publicly available data sets from proximate sensing to support land-use analysis. We identify several research problems in the perspective of examples to support the training of models and means of integrating diverse data sets. Our discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing images in urban land-use studies. Numéro de notice : A2021-759 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1919682 Date de publication en ligne : 03/05/2021 En ligne : https://doi.org/10.1080/13658816.2021.1919682 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98788
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2129 - 2148[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Bi- and three-dimensional urban change detection using sentinel-1 SAR temporal series / Meiqin Che in Geoinformatica, vol 25 n° 4 (October 2021)
[article]
Titre : Bi- and three-dimensional urban change detection using sentinel-1 SAR temporal series Type de document : Article/Communication Auteurs : Meiqin Che, Auteur ; Paolo Gamba, Auteur Année de publication : 2021 Article en page(s) : pp 759 - 773 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] banlieue
[Termes IGN] centre-ville
[Termes IGN] Chine
[Termes IGN] détection de changement
[Termes IGN] image Sentinel-SAR
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (auteur) Urban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also because of catastrophic and sudden events, such as earthquakes, landslides, or floods. This paper aims at designing a procedure able to cope with both types of changes by combining interferometric coherence and backscatter amplitude, and provide a semantically meaningful analysis of the changes detected in both city inner cores and suburban areas. Specifically, this paper focuses on detecting multi-dimensional changes in urban areas using a stack of repeat-pass SAR data sets from Sentinel-1A/B satellites. The proposed procedure jointly exploits amplitude and coherence time series to perform this task. SAR amplitude is used to extract changes about the urban extents, i.e. in 2D, while interferometric coherence is sensitive to the presence of buildings and to their size, i. e. to 3D changes. The proposed algorithm is tested using a time-series of two years of Sentinel-1 data, from May 2016 to October 2018, and in two different Chinese cities, Changsha and Hangzhou, with the aim to understand both the temporal evolution of the urban extents, and the changes within what is constantly classified as “urban” throughout the considered time period. Numéro de notice : A2021-966 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-020-00398-8 Date de publication en ligne : 22/02/2020 En ligne : https://doi.org/10.1007/s10707-020-00398-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100389
in Geoinformatica > vol 25 n° 4 (October 2021) . - pp 759 - 773[article]Spatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkUrban geospatial information acquisition mobile mapping system based on close-range photogrammetry and IGS site calibration / Ming Guo in Geo-spatial Information Science, vol 24 n° 4 (October 2021)PermalinkGIS-based logic scoring of preference method for urban densification suitability analysis / Shuoge Shen in Computers, Environment and Urban Systems, vol 89 (September 2021)PermalinkImproving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkFlood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)PermalinkRoad-network-based fast geolocalization / Yongfei Li in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkSpatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkRoadside tree extraction and diameter estimation with MMS lidar by using point-cloud image / Genki Takahashi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkFast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)PermalinkA framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([15/06/2021])PermalinkSemantic signatures for large-scale visual localization / Li Weng in Multimedia tools and applications, vol 80 n° 15 (June 2021)PermalinkCrowdsourcing of popular toponyms: How to collect and preserve toponyms in spoken use / Daniel Vrbik in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkQuality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)PermalinkDecision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkDetecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkA trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks / Bozhao Li in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)PermalinkUrban heat island formation in greater Cairo: Spatio-temporal analysis of daytime and nighttime land surface temperatures along the urban–rural gradient / Darshana Athukorala in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkCharacterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkLearning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)Permalink