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Unsupervised deep joint segmentation of multitemporal high-resolution images / Sudipan Saha in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
[article]
Titre : Unsupervised deep joint segmentation of multitemporal high-resolution images Type de document : Article/Communication Auteurs : Sudipan Saha, Auteur ; Lichao Mou, Auteur ; Chunping Qiu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8780 - 8792 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de données
[Termes IGN] image à haute résolution
[Termes IGN] image à très haute résolution
[Termes IGN] image multitemporelle
[Termes IGN] itération
[Termes IGN] segmentation sémantiqueRésumé : (auteur) High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth’s surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento. Numéro de notice : A2020-744 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2990640 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2990640 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96375
in IEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8780 - 8792[article]Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)
[article]
Titre : Street-Frontage-Net: urban image classification using deep convolutional neural networks Type de document : Article/Communication Auteurs : Stephen Law, Auteur ; Chanuki Illushka Seresinhe, Auteur ; Yao Shen, Auteur Année de publication : 2020 Article en page(s) : pp 681- 707 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace public
[Termes IGN] évaluation foncière
[Termes IGN] extraction de données
[Termes IGN] façade
[Termes IGN] habitat urbain
[Termes IGN] image Streetview
[Termes IGN] immobilier (secteur)
[Termes IGN] information géographique
[Termes IGN] Londres
[Termes IGN] matrice de confusion
[Termes IGN] Paris (75)
[Termes IGN] paysage urbain
[Termes IGN] urbanisme
[Termes IGN] vision par ordinateurRésumé : (auteur) Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small-scale studies have indicated that the more active the frontage, the livelier and safer a street feels. However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design. Numéro de notice : A2020-110 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1555832 Date de publication en ligne : 26/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1555832 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94712
in International journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 681- 707[article]Extracting impervious surfaces from full polarimetric SAR images in different urban areas / Sara Attarchi in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)
[article]
Titre : Extracting impervious surfaces from full polarimetric SAR images in different urban areas Type de document : Article/Communication Auteurs : Sara Attarchi, Auteur Année de publication : 2020 Article en page(s) : pp 4644 - 4663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de données
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] polarimétrie radar
[Termes IGN] précision de la classification
[Termes IGN] radar à antenne synthétique
[Termes IGN] surface imperméable
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] texture d'image
[Termes IGN] zone urbaineRésumé : (auteur) Accurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited. Numéro de notice : A2020-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723178 Date de publication en ligne : 24/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723178 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95539
in International Journal of Remote Sensing IJRS > vol 41 n° 12 (20 - 30 March 2020) . - pp 4644 - 4663[article]A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)
[article]
Titre : A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data Type de document : Article/Communication Auteurs : Sheng Hu, Auteur ; Zhanjun He, Auteur ; Liang Wu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] données massives
[Termes IGN] espace urbain
[Termes IGN] extraction de données
[Termes IGN] gestion urbaine
[Termes IGN] image à haute résolution
[Termes IGN] point d'intérêt
[Termes IGN] regroupement de données
[Termes IGN] télédétection spatiale
[Termes IGN] traitement du langage naturel
[Termes IGN] Wuhan (Chine)
[Termes IGN] zone urbaineRésumé : (auteur) Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed. Numéro de notice : A2020-191 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2019.101442 Date de publication en ligne : 15/11/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101442 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94853
in Computers, Environment and Urban Systems > vol 80 (March 2020)[article]An indoor navigation model and its network extraction / Filippo Mortari in Applied geomatics, Vol 11 n° 4 (December 2019)
[article]
Titre : An indoor navigation model and its network extraction Type de document : Article/Communication Auteurs : Filippo Mortari, Auteur ; Eliseo Clementini, Auteur ; Sisi Zlatanova, Auteur ; Liu Liu, Auteur Année de publication : 2019 Article en page(s) : pp 413–427 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] axe médian
[Termes IGN] CityGML
[Termes IGN] diagramme de Voronoï
[Termes IGN] espace topologique
[Termes IGN] extraction automatique
[Termes IGN] extraction de données
[Termes IGN] modèle géométrique du bâti
[Termes IGN] modèle numérique du bâti
[Termes IGN] modélisation 3D
[Termes IGN] positionnement en intérieur
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] représentation spatio-sémantiqueRésumé : (auteur) We propose a navigation model for indoor environments that combines a 3D geometric modeling of buildings with connection properties of spaces and semantic elements such as openings and installations. The model is an extension of the IndoorGML standard navigation module with a twofold benefit: the extension facilitated the data import from the international standard CityGML and introduced the semantics of various fixtures in indoor space of buildings making the navigation model more suitable for human needs. Several experiments have been conducted by extracting networks from CityGML data and performing a comparison with other network construction techniques. The second contribution of the paper is an algorithm for the automatic extraction of the navigation network. Such an algorithm is a hybrid solution between medial axis approaches and visibility graph approaches. Normally, medial axes approaches are a good representation of human navigation in narrow corridors, especially to avoid obstacles, but introduce distortions in open space. On the other hand, visibility approaches work better in open spaces. In our extraction technique, the resulting network takes advantages of both approaches and better mimics human beings’ navigation in indoor environments. Numéro de notice : A2019-534 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00273-8 Date de publication en ligne : 17/06/2019 En ligne : https://doi.org/10.1007/s12518-019-00273-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94129
in Applied geomatics > Vol 11 n° 4 (December 2019) . - pp 413–427[article]PPD: Pyramid Patch Descriptor via convolutional neural network / Jie Wan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)PermalinkA generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)PermalinkEngraved footprints from the past. Retrieving cartographic geohistorical data from the Cassini Carte de France, 1750-1789 / Bertrand Duménieu (2019)PermalinkWebscraping, bigdata et analyse spatiale de données immobilières : réponse à un projet ESPON au sein de l'UMS RIATE / Marc Lieury (2019)PermalinkLes usages des systèmes d’informations géographiques en matière de gestion de mobilité en milieu urbain : la mise en accessibilité aux personnes à mobilité réduite des arrêts de bus en Seine-Seine-Denis / Thi-Lieu Gremont-Dong (2018)PermalinkKnowledge extraction from crowdsourced data for the enrichment of road networks / Gregor Jossé in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkUne chaîne de données pour évaluer la qualité des données OSM. Partie 1 : Extraction des données et description / Damien Garaud in Géomatique expert, n° 117 (juillet - août 2017)PermalinkInformation extraction and visualization from twitter considering spatial structure / Hideyuki Fujita in Cartographica, vol 52 n° 2 (Summer 2017)PermalinkProcess BIM : Une chaîne de traitements pour le tel que construit / Tania Landes in Géomètre, n° 2146 (avril 2017)PermalinkVGDI – advancing the concept: Volunteered geo-dynamic information and its benefits for population dynamics modeling / Christoph Aubrecht in Transactions in GIS, vol 21 n° 2 (April 2017)Permalink