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scientifique

Simulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])  

Public [article]  inGeocarto international > vol 37 n° 4 [15/02/2022] . - pp 1084 - 1099 Titre : Simulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India Type de document : Article/Communication Auteurs : Vaibhav Kumar, Auteur ; Arnab Jana, Auteur ; Krithi Ramamritham, Auteur Année de publication : 2022 Article en page(s) : pp 1084 - 1099 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage automatique [Termes IGN] bâtiment [Termes IGN] Bombay [Termes IGN] incendie [Termes IGN] modèle de régression [Termes IGN] planification urbaine [Termes IGN] prévention des risques [Termes IGN] régression linéaire [Termes IGN] zone urbaine Résumé : (auteur) The article addresses the void in developing analytical methods concerning to design urban configurations that could reduce fire risks, and, thus, could help in achieving sustainable goals. A novel algorithm is developed to generate alternative Urban Built Form (UBF) models that could be less susceptible to fire compared to the existing built-form. Fire susceptibility of a generated UBF is predicted using a developed linear regression model. The algorithm considers existing regulations to derive rules and develop scenarios that might be effective in building fire-resilient cities. The outcomes of the simulations showed a significant decrease in the fire susceptibility of the southern region of Mumbai city. Moreover, for a certain simulated scenario the predicted UBF could accommodate twice the current population while being less susceptible than the existing UBF. The proposed techniques and methods can act as a decision-making tool in taking pre-emptive planning measures to develop fire resilient cities. Numéro de notice : A2022-395 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1756463 Date de publication en ligne : 28/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1756463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100689 [article]

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]

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study / Hossein Shafizadeh-Moghadam in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 606 - 623 Titre : Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Julian Hagenauer, Auteur ; Manuchehr Farajzadeh, Auteur ; Marco Helbich, Auteur Année de publication : 2015 Article en page(s) : pp 606 - 623 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Bombay [Termes IGN] croissance urbaine [Termes IGN] fonction de base radiale [Termes IGN] milieu urbain [Termes IGN] modèle de simulation [Termes IGN] Perceptron multicouche [Termes IGN] performance [Termes IGN] test de performance [Termes IGN] urbanisation Résumé : (Auteur) The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling. Numéro de notice : A2015-589 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.993989 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.993989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77875 [article]

Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery / Ujjwal Maulik in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 77 (March 2013) . - pp 66 - 78 Titre : Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery Type de document : Article/Communication Auteurs : Ujjwal Maulik, Auteur ; Debasis Chakraborty, Auteur Année de publication : 2013 Article en page(s) : pp 66 - 78 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Bombay [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification pixellaire [Termes IGN] classification semi-dirigée [Termes IGN] image infrarouge couleur [Termes IGN] image SPOT [Termes IGN] Inde [Termes IGN] ville Résumé : (Auteur) Land cover classification using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover area of different categories. In this regard, support vector machines (SVMs) have recently received increasing attention. However, small number of training samples remains a bottleneck to design suitable supervised classifiers. On the other hand, adequate number of unlabeled data is available in remote sensing images which can be employed as additional source of information about margins. To fully leverage all of the precious unlabeled data, integration of filtering in a transductive SVM is proposed. Using two labeled image datasets of small size and two large unlabeled image datasets, the effectiveness of the proposed method is explored. Experimental results show that the proposed technique achieves average overall accuracies of around 4.5–7.8%, 0.8–2.6% and 0.9–2.2% more than the standard inductive SVM (ISVM), progressive transductive SVM (PTSVM) and low density separation (LDS) classifiers, respectively on larger domains in case of labeled datasets. Using image datasets, visual interpretation from the classified images as well as the segmentation quality reveal that the proposed method can efficiently filter informative data from the unlabeled samples. Numéro de notice : A2013-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.12.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.12.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32254 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Satellite image classification using genetically guided fuzzy clustering with spatial information / S. Bandyopadhyay in International Journal of Remote Sensing IJRS, vol 26 n° 3 (February 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 3 (February 2005) . - pp 579 - 593 Titre : Satellite image classification using genetically guided fuzzy clustering with spatial information Type de document : Article/Communication Auteurs : S. Bandyopadhyay, Auteur Année de publication : 2005 Article en page(s) : pp 579 - 593 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse de groupement [Termes IGN] Bombay [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] image satellite [Termes IGN] pixel [Termes IGN] segmentation d'image [Termes IGN] utilisation du sol Résumé : (Auteur) Land-cover classification of satellite images is an important task in analysis of remote sensing imagery. Segmentation is one of the widely used techniques in this regard. One of the important approaches for segmentation of an image is by clustering the pixels in the spectral domain, where pixels that share some common spectral property are put in the same group, or cluster. However, such spectral clustering completely ignores the spatial information contained in the pixels, which is often an important consideration for good segmentation of images. Moreover, the clustering algorithms often provide locally optimal solutions. In this paper, we propose to perform. image segmentation by a genetically guided unsupervised fuzzy clustering technique where some spatial information of the pixels is incorporated. Two ways of incorporating spatial information are suggested. The characteristic of this technique is that it is able to determine automatically the appropriate number of clusters without making any assumptions regarding the dataset. while attempting to provide globally near optimal solutions. In order to evolve the appropriate number of clusters, the chromosome encoding scheme is enhanced to incorporate the don't care symbol (#). Real-coded genetic algorithm with appropriatly defined operators is used. A cluster validity index is used as a measure of the value of the chromosomes. Results, both quantitative and qualitative are demonstrated for several images, including a satellite image of a part of the city of Mumbai. Numéro de notice : A2005-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331316432 En ligne : https://doi.org/10.1080/01431160512331316432 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27180 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05031	RAB	Revue	Centre de documentation	En réserve L003	Disponible