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Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)
[article]
Titre : Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network Type de document : Article/Communication Auteurs : Alex David Singleton, Auteur ; Dani Arribas-Bel, Auteur ; John Murray, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101802 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Grande-Bretagne
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] morphologie urbaine
[Termes IGN] pondération
[Termes IGN] processeur graphiqueRésumé : (auteur) The increased availability of high-resolution multispectral imagery captured by remote sensing platforms provides new opportunities for the characterisation and differentiation of urban context. The discovery of generalized latent representations from such data are however under researched within the social sciences. As such, this paper exploits advances in machine learning to implement a new method of capturing measures of urban context from multispectral satellite imagery at a very small area level through the application of a convolutional autoencoder (CAE). The utility of outputs from the CAE is enhanced through the application of spatial weighting, and the smoothed outputs are then summarised using cluster analysis to generate a typology comprising seven groups describing salient patterns of differentiated urban context. The limits of the technique are discussed with reference to the resolution of the satellite data utilised within the study and the interaction between the geography of the input data and the learned structure. The method is implemented within the context of Great Britain, however, is applicable to any location where similar high resolution multispectral imagery are available. Numéro de notice : A2022-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101802 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101802 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100606
in Computers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101802[article]Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)
[article]
Titre : Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Xinyue Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1317 - 1342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] distorsion d'image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] hauteur du bâti
[Termes IGN] image Streetview
[Termes IGN] lever tachéométrique
[Termes IGN] modèle numérique de surface
[Termes IGN] porteRésumé : (auteur) Street view imagery such as Google Street View is widely used in people’s daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors’ elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas. Numéro de notice : A2022-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1981334 Date de publication en ligne : 06/10/2021 En ligne : https://doi.org/10.1080/13658816.2021.1981334 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100970
in International journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1317 - 1342[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022071 SL Revue Centre de documentation Revues en salle Disponible GANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)
[article]
Titre : GANmapper: geographical data translation Type de document : Article/Communication Auteurs : Abraham Noah Wu, Auteur ; Filip Biljecki, Auteur Année de publication : 2022 Article en page(s) : pp 1394 - 1422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment
[Termes IGN] distance de Fréchet
[Termes IGN] empreinte
[Termes IGN] morphologie urbaine
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau routier
[Termes IGN] système d'information géographique
[Termes IGN] texture d'imageRésumé : (auteur) We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data. Numéro de notice : A2022-493 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2041643 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2041643 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100975
in International journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1394 - 1422[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022071 SL Revue Centre de documentation Revues en salle Disponible Graph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)
[article]
Titre : Graph-based block-level urban change detection using Sentinel-2 time series Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Wei Li, Auteur ; Ran Tao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112993 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] bâtiment
[Termes IGN] Chine
[Termes IGN] détection de changement
[Termes IGN] espace vert
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (auteur) Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high temporal resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multivariate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers. Numéro de notice : A2022-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112993 Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112993 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100699
in Remote sensing of environment > vol 274 (June 2022) . - n° 112993[article]Automatic training data generation in deep learning-aided semantic segmentation of heritage buildings / Arnadi Murtiyoso in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Automatic training data generation in deep learning-aided semantic segmentation of heritage buildings Type de document : Article/Communication Auteurs : Arnadi Murtiyoso, Auteur ; Francesca Matrone, Auteur ; M.C. Martini, Auteur ; Andrea Lingua, Auteur ; Pierre Grussenmeyer, Auteur ; Roberto Pierdicca, Auteur Année de publication : 2022 Article en page(s) : pp 317 - 324 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] monument historique
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and more widespread. In this context, the 3D semantic segmentation of heritage point clouds presents an interesting and promising approach for modelling automation, in light of the heterogeneous nature of historical building styles and features. However, this heterogeneity also presents an obstacle in terms of generating the training data for use in deep learning, hitherto performed largely manually. The current generally low availability of labelled data also presents a motivation to aid the process of training data generation. In this paper, we propose the use of approaches based on geometric rules to automate to a certain degree this task. One object class will be discussed in this paper, namely the pillars class. Results show that the approach managed to extract pillars with satisfactory quality (98.5% of correctly detected pillars with the proposed algorithm). Tests were also performed to use the outputs in a deep learning segmentation setting, with a favourable outcome in terms of reducing the overall labelling time (−66.5%). Certain particularities were nevertheless observed, which also influence the result of the deep learning segmentation. Numéro de notice : A2022-430 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-317-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100736
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 317 - 324[article]ART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage / M. Moreno in Journal of Cultural Heritage, vol 55 (May - June 2022)PermalinkCity3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)PermalinkNavigation network derivation for QR code-based indoor pedestrian path planning / Jinjin Yan in Transactions in GIS, vol 26 n° 3 (May 2022)PermalinkDetermination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkA GAN-based approach toward architectural line drawing colorization prototyping / Qian (Chayn) Sun in The Visual Computer, vol 38 n° 4 (April 2022)PermalinkHuman movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? / Meiliu Wu in Annals of GIS, vol 28 n° 2 (April 2022)PermalinkAutomated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkThe re-invention of the Goori cultural landscape: Telling the country: Mapping two pockets / Paul Memmott in Cartographica, Vol 57 n° 1 (Spring 2022)PermalinkSimulating 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])PermalinkDetection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)PermalinkAn approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web / Abdulkadir Memduhoglu in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkAutomatic structuring of photographic collections for spatio-temporal monitoring of restoration sites: problem statement and challenges / Laura Willot (2022)PermalinkLa cartographie au service de la diffusion des connaissances de l’Inventaire du Patrimoine culturel de la Région Bretagne / Elise Frank (2022)PermalinkCultivating historical heritage area vitality using urban morphology approach based on big data and machine learning / Jiayu Wu in Computers, Environment and Urban Systems, vol 91 (January 2022)PermalinkCultural Heritage and Climate Change: New challenges and perspectives for research / Christopher Ballard (2022)PermalinkDéveloppement d’outils et de méthodes permettant l’acquisition, le traitement et la diffusion de données issues de levés par drone / Guillaume Feuillatre (2022)PermalinkGuidelines for the management of cultural heritage using 3D models for the insertion of heterogeneous data / Gianna Bertacchi (2022)PermalinkInteractive HGIS platform union of Lublin (1569): A geomatic solution for discovering the Jagiellonian heritage of the city / Jakub Kuna in Journal of Cultural Heritage, vol 53 (January–February 2022)PermalinkLevé et numérisation du château de Lichtenberg en vue d’une proposition de visite virtuelle du site à des périodes remarquables / Maxime Rocha (2022)PermalinkA method to produce metadata describing and assessing the quality of spatial landmark datasets in mountain area / Marie-Dominique Van Damme (2022)Permalink