Centre de documentation
scientifique

Dépouillements

Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6551 - 6578 Titre : Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model Type de document : Article/Communication Auteurs : Santanu Dinda, Auteur ; Nilanjana Das Chatterjee, Auteur ; Subrata Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 6551 - 6578 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] chaîne de Markov [Termes IGN] changement d'occupation du sol [Termes IGN] croissance urbaine [Termes IGN] densité du bâti [Termes IGN] espace vert [Termes IGN] Inde [Termes IGN] logique floue [Termes IGN] modèle de simulation [Termes IGN] processus de hiérarchisation analytique [Termes IGN] réseau neuronal artificiel [Termes IGN] vulnérabilité [Termes IGN] zone urbaine dense Résumé : (auteur) Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response’ framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process and Fuzzy Comprehensive Evaluation method have been used. An integrated multilayer perceptron based artificial neural network and Markov chain (MLP-ANN-MC) model has been employed to predict the UGS vulnerable area in Kolkata. Results indicated that growth rates of built-up area, land-use dynamic degree, change intensity index, and proximity factors are the major responsible for UGS vulnerability. Applying the MLP-ANN-MC model, future vulnerable zones were identified for management and conservation of UGS. The methodology developed and demonstrated in this study expands LULC change analysis and provide a new dimension for UGS vulnerability assessment. Numéro de notice : A2022-726 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1952315 Date de publication en ligne : 16/07/2021 En ligne : https://doi.org/10.1080/10106049.2021.1952315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101672 [article]

Raster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6494 - 6518 Titre : Raster-based method for building selection in the multi-scale representation of two-dimensional maps Type de document : Article/Communication Auteurs : Yilang Shen, Auteur ; Tinghua Ai, Auteur ; Rong Zhao, Auteur Année de publication : 2022 Article en page(s) : pp 6494 - 6518 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] bâtiment [Termes IGN] densité du bâti [Termes IGN] distribution spatiale [Termes IGN] données matricielles [Termes IGN] représentation cartographique 2D [Termes IGN] représentation multiple [Termes IGN] segmentation [Termes IGN] superpixel [Termes IGN] triangulation de Delaunay [Vedettes matières IGN] Généralisation Résumé : (auteur) In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method for raster building selection is proposed. In this method, to preserve the distribution range, a new homogeneous linear spectral clustering (HLSC) superpixel segmentation method is developed for the relatively homogeneous spatial division of building groups. Then, to preserve the relative distribution density, multi-level spatial division is performed according to the local number of buildings. Finally, to preserve the local geometric, attributive and geographical characteristics, four selection strategies, namely, the minimum centroid distance, minimum boundary distance, maximum area and considering geographical element strategies, are designed to generate selection results. To evaluate the proposed method, dispersed buildings in a suburban area are utilized to perform selection tasks. The experimental results indicate that the proposed method can effectively select dispersed irregular buildings at different levels of detail while maintaining the original distribution range and relative distribution density. In addition, the use of multiple selection strategies considering various geometric, attributive and geographical characteristics provides multiple options for cartography. Numéro de notice : A2022-727 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1943007 Date de publication en ligne : 29/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1943007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101673 [article]

Analysis of the spatial range of service and accessibility of hospitals designated for coronavirus disease 2019 in Yunnan Province, China / Liangting Zheng in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6519 - 6537 Titre : Analysis of the spatial range of service and accessibility of hospitals designated for coronavirus disease 2019 in Yunnan Province, China Type de document : Article/Communication Auteurs : Liangting Zheng, Auteur ; Jia Li, Auteur ; Wenying Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6519 - 6537 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accessibilité [Termes IGN] diagramme de Voronoï [Termes IGN] données médicales [Termes IGN] données routières [Termes IGN] épidémie [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] interpolation par pondération de zones [Termes IGN] maladie virale [Termes IGN] médecine humaine [Termes IGN] secours d'urgence [Termes IGN] Yunnan (Chine) Résumé : (auteur) COVID-19 poses a major threat to global health care systems, and the recent surge in mortality rates confirms the importance of timely access to care. The capacity of medical service providers is reflected both in the spatial accessibility of medical institutions and in the spatial scope of their services. Therefore, this study aims to investigate the spatial scope of services and spatial accessibility of COVID-19-designated hospitals in Yunnan Province, China. Data are collected from multiple sources and included COVID-19 case data, road data, and data from designated hospitals for COVID-19 in Yunnan Province. The optimal spatial service range for designated hospitals is delineated using a weighted Voronoi diagram that takes into account the number of medical staff and the number of beds in the hospital. Traffic accessibility coefficients are introduced to analyze the spatial accessibility of COVID-19-designated hospitals, and the spatial accessibility of each designated hospital is visualized using the inverse distance weighting interpolation algorithm. The results show the following: (1) COVID-19 cases in Yunnan Province are concentrated in the central and northern regions. The largest single cells in the weighted Voronoi diagram are mainly Pu'er (59168 km2), Honghe (35569 km2), and Baoshan (46795 km2), and the time cost of attainting medical treatment is greater for residents in marginal areas. (2) Within the service space of designated hospitals, 90.24% of patients could obtain medical assistance within 2 h. Those in 52 (36.36%) counties within a municipal jurisdiction could obtain medical services within 2 h, and 76.47% of counties have above-average spatial accessibility. (3) Medical resources in Yunnan Province should be shifted toward the high-risk east-central region and the less spatially accessible in southern and western regions. Numéro de notice : A2022-728 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1943008 Date de publication en ligne : 09/07/2021 En ligne : https://doi.org/10.1080/10106049.2021.1943008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101674 [article]

Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6643 - 6670 Titre : Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information Type de document : Article/Communication Auteurs : Ozlem Akar, Auteur ; Esra Tunc Gormus, Auteur Année de publication : 2022 Article en page(s) : pp 6643 - 6670 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] filtre de Gabor [Termes IGN] image hyperspectrale [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] texture d'image [Termes IGN] transformation en ondelettes [Termes IGN] Turquie Résumé : (auteur) Land use and Land cover (LULC) mapping is one of the most important application areas of remote sensing which requires both spectral and spatial resolutions in order to decrease the spectral ambiguity of different land cover types. Airborne hyperspectral images are among those data which perfectly suits to that kind of applications because of their high number of spectral bands and the ability to see small details on the field. As this technology has newly developed, most of the image processing methods are for the medium resolution sensors and they are not capable of dealing with high resolution images. Therefore, in this study a new framework is proposed to improve the classification accuracy of land use/cover mapping applications and to achieve a greater reliability in the process of mapping land use map using high resolution hyperspectral image data. In order to achieve it, spatial information is incorporated together with spectral information by exploiting feature extraction methods like Grey Level Co-occurrence Matrix (GLCM), Gabor and Morphological Attribute Profile (MAP) on dimensionally reduced image with highest accuracy. Then, machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM) are used to investigate the contribution of texture information in the classification of high resolution hyperspectral images. In addition to that, further analysis is conducted with object based RF classification to investigate the contribution of contextual information. Finally, overall accuracy, producer’s/user’s accuracy, the quantity and allocation based disagreements and location and quantity based kappa agreements are calculated together with McNemar tests for the accuracy assessment. According to our results, proposed framework which incorporates Gabor texture information and exploits Discrete Wavelet Transform based dimensionality reduction method increase the overall classification accuracy up to 9%. Amongst individual classes, Gabor features boosted classification accuracies of all the classes (soil, road, vegetation, building and shadow) to 7%, 6%, 6%, 8%, 9%, and 24% respectively with producer’s accuracy. Besides, 17% and 10% increase obtained in user’s accuracy with MAP (area) feature in classifying road and shadow classes respectively. Moreover, when the object based classification is conducted, it is seen that the OA of pixel based classification is increased further by 1.07%. An increase between 2% and 4% is achieved with producer’s accuracy in soil, vegetation and building classes and an increase between 1% and 3% is achieved by user’s accuracy in soil, road, vegetation and shadow classes. In the end, accurate LULC map is produced with object based RF classification of gabor features added airborne hyperspectral image which is dimensionally reduced with DWT method. Numéro de notice : A2022-729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1944453 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.1080/10106049.2021.1944453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101675 [article]