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Urban expansion in the megacity since 1970s: a case study in Mumbai / Sisi Yu in Geocarto international, vol 36 n° 6 ([01/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 6 [01/04/2021] . - pp 603 - 621 Titre : Urban expansion in the megacity since 1970s: a case study in Mumbai Type de document : Article/Communication Auteurs : Sisi Yu, Auteur ; ZengXiang Zhang, Auteur ; Fang Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 603 - 621 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatio-temporelle [Termes IGN] Bombay [Termes IGN] cartographie urbaine [Termes IGN] croissance urbaine [Termes IGN] données spatiotemporelles [Termes IGN] dynamique spatiale [Termes IGN] image Landsat [Termes IGN] mégalopole [Termes IGN] paysage urbain Résumé : (Auteur) Understanding the process of urban expansion in megacities is considerably important. In this study, megacity Mumbai was selected as the study area. Based on the urban maps retrieved from Landsat images in 1973–2018, we mapped and quantified the detailed urban expansion process of Mumbai by adopting the expansion area and speed indices, centroid shift model, urban expansion type method, hot-zone identification method and landscape metrics. The results indicated that: (1) urban land remarkably expanded, and its centroid moved from the southwest to the northeast direction, mainly adopting the edge-expansion form. (2) Distinctly spatiotemporal heterogeneities existed in eight directions, faster in the north, northeast and east directions, whereas slower in the five other directions. (3) The number of hot-zones increased from two to three and moved outward in space from urban centroid. (4) The urban landscape of Mumbai showed the ‘diffusion, aggregation, re-diffusion’ pattern and presented differences in eight directions. Numéro de notice : A2021-290 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622600 Date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1622600 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97336 [article]

The influence of urban form on the spatiotemporal variations in land surface temperature in an arid coastal city / Irshad Mir Parvez in Geocarto international, vol 36 n° 6 ([01/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 6 [01/04/2021] . - pp 640 - 659 Titre : The influence of urban form on the spatiotemporal variations in land surface temperature in an arid coastal city Type de document : Article/Communication Auteurs : Irshad Mir Parvez, Auteur ; Yusuf A. Aina, Auteur ; Abdul‐Lateef Balogun, Auteur Année de publication : 2021 Article en page(s) : pp 640 - 659 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de données [Termes IGN] analyse de variance [Termes IGN] Arabie Saoudite [Termes IGN] données spatiotemporelles [Termes IGN] ilot thermique urbain [Termes IGN] image Landsat [Termes IGN] littoral [Termes IGN] morphologie urbaine [Termes IGN] occupation du sol [Termes IGN] température au sol [Termes IGN] ville durable [Termes IGN] zone aride Résumé : (Auteur) This article explores using satellite images to monitor spatiotemporal variations in temperature related to urban form. Land surface temperatures (LST) were estimated from Landsat images (1986–2016) and the land cover and urban form LST were extracted by using samples representing different urban forms/cover types. A transect of 20 km was taken across the city to derive the LST across the different land cover types. Urban heat island index and statistical analysis were carried out to understand the influence of urban form and cover on changes in surface temperature. The results are compared with temperature regimes of an industrial city (Yanbu) to depict differences in the two cities. The analysis of variance (ANOVA) shows variations, at 0.01 level of significance, in the LST values of the city centre, high-rise, low-density, vegetation, desert and industrial land-use types. The outcome of the study is valuable for decision-makers in achieving sustainable urban development. Numéro de notice : A2021-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622598 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1622598 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97337 [article]

Anti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([01/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 6 [01/04/2021] . - pp 676 - 697 Titre : Anti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification Type de document : Article/Communication Auteurs : Laxmi Narayana Eeti, Auteur ; Krishna Mohan Buddhiraju, Auteur Année de publication : 2021 Article en page(s) : pp 676 - 697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données multisources [Termes IGN] image hyperspectrale [Termes IGN] jeu de données [Termes IGN] précision de la classification Résumé : (Auteur) Achieving high classification accuracy is vital in reliable information extraction from images. Single classifiers and existing ensemble methods suffer from data dimensionality, insufficient ground truth information and lack in defining optimal feature selection. This article presents a novel idea for constructing component classifiers that boost random subspace ensemble method in improving its classification performance. It is achieved through sub-optimal training of component classifiers through interference in training process during validation error evaluation. The new approach allows to enforce different class errors among component classifiers, besides improving individual class accuracy. This article demonstrates effectiveness of the anti-cross validation approach using three classical hyperspectral Image (HSI) datasets with significant improvement in classification accuracies from 3 to 10% with the proposed approach. Numéro de notice : A2021-292 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1618926 Date de publication en ligne : 03/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1618926 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97338 [article]