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Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 339-348 Titre : Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery Type de document : Article/Communication Auteurs : Neema Nicodemus Lyimo, Auteur ; Fang Luo, Auteur ; Qimin Cheng, Auteur ; Hao Peng, Auteur Année de publication : 2021 Article en page(s) : pp 339-348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] corrélation d'images [Termes IGN] distance euclidienne [Termes IGN] données d'apprentissage [Termes IGN] données hétérogènes [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] données ouvertes [Termes IGN] image Landsat [Termes IGN] incertitude des données [Termes IGN] jeu de données localisées [Termes IGN] qualité des données [Termes IGN] système à base de connaissances [Termes IGN] zone urbaine Résumé : (Auteur) Quality assessment of training samples collected from heterogeneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncertainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geographic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre-classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to develop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification. Numéro de notice : A2021-366 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.339 date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.339 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97695 [article]

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An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 114 - 131 Titre : An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds Type de document : Article/Communication Auteurs : Fei Su, Auteur ; Haihong Zhu, Auteur ; Taoyi Chen, Auteur Année de publication : 2021 Article en page(s) : pp 114 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] adjacence [Termes IGN] appariement de graphes [Termes IGN] arc [Termes IGN] bloc d'ancrage [Termes IGN] classification orientée objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] jeu de données localisées [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] noeud [Termes IGN] objet 3D [Termes IGN] orientation [Termes IGN] positionnement en intérieur [Termes IGN] semis de points Résumé : (auteur) Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can be used to classify freely oriented objects. However, this approach suffers from missing anchor detection since valid detection relies heavily on the completeness of an anchor’s point clouds and is sensitive to missing data. This paper presents an anchor-based graph method to detect and classify arbitrarily oriented indoor objects. The anchors of each object are extracted by the structurally adjacent relationship among parts instead of the parts’ geometric metrics. In the case of adjacency, an anchor can be correctly extracted even with missing parts since the adjacency between an anchor and other parts is retained irrespective of the area extent of the considered parts. The best graph matching is achieved by finding the optimal corresponding node-pairs in a super-graph with fully connecting nodes based on maximum likelihood. The performances of the proposed method are evaluated with three indicators (object precision, object recall and object F1-score) in seven datasets. The experimental tests demonstrate the effectiveness of dealing with TLS point clouds, RGBD point clouds and Panorama RGBD point clouds, resulting in performance scores of approximately 0.8 for object precision and recall and over 0.9 for chair precision and table recall. Numéro de notice : A2021-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.007 date de publication en ligne : 29/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96852 [article]

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Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin / Luis Felipe Galizia in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)  

Public [article]  inNatural Hazards and Earth System Sciences > vol 21 n° 1 (January 2021) . - pp 73 - 86 Titre : Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin Type de document : Article/Communication Auteurs : Luis Felipe Galizia, Auteur ; Thomas Curt, Auteur ; Renaud Barbero, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 73 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bassin méditerranéen [Termes IGN] cartographie des risques [Termes IGN] exactitude des données [Termes IGN] image Terra-MODIS [Termes IGN] incendie [Termes IGN] incertitude des données [Termes IGN] jeu de données localisées Résumé : (auteur) Recently, many remote-sensing datasets providing features of individual fire events from gridded global burned area products have been released. Although very promising, these datasets still lack a quantitative estimate of their accuracy with respect to historical ground-based fire datasets. Here, we compared three state-of-the-art remote-sensing datasets (RSDs; Fire Atlas, FRY, and GlobFire) with a harmonized ground-based dataset (GBD) compiled by fire agencies monitoring systems across the southwestern Mediterranean Basin (2005–2015). We assessed the agreement between the RSDs and the GBD with respect to both burned area (BA) and number of fires (NF). RSDs and the GBD were aggregated at monthly and 0.25∘ resolutions, considering different individual fire size thresholds ranging from 1 to 500 ha. Our results show that all datasets were highly correlated in terms of monthly BA and NF, but RSDs severely underestimated both (by 38 % and 96 %, respectively) when considering all fires > 1 ha. The agreement between RSDs and the GBD was strongly dependent on individual fire size and strengthened when increasing the fire size threshold, with fires  > 100 ha denoting a higher correlation and much lower error (BA 10 %; NF 35 %). The agreement was also higher during the warm season (May to October) in particular across the regions with greater fire activity such as the northern Iberian Peninsula. The Fire Atlas displayed a slightly better performance with a lower relative error, although uncertainty in the gridded BA product largely outpaced uncertainties across the RSDs. Overall, our findings suggest a reasonable agreement between RSDs and the GBD for fires larger than 100 ha, but care is needed when examining smaller fires at regional scales. Numéro de notice : A2021-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/nhess-21-73-2021 date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.5194/nhess-21-73-2021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96995 [article]

Spatial dataset search: Building a dedicated knowledge graph / Mehdi Zhral (2021)  

Public Titre : Spatial dataset search: Building a dedicated knowledge graph Type de document : Article/Communication Auteurs : Mehdi Zhral , Auteur ; Bénédicte Bucher , Auteur ; Marie-Dominique Van Damme , Auteur ; Fayçal Hamdi , Auteur Editeur : AGILE Alliance Année de publication : 2021 Conférence : AGILE 2021, 24th AGILE Conference on Geographic Information Science 19/07/2021 22/07/2021 Aurora - Colorado Etats-Unis OA Proceedings Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] données massives [Termes IGN] données ouvertes [Termes IGN] graphe [Termes IGN] INSPIRE [Termes IGN] jeu de données localisées [Termes IGN] précision sémantique [Termes IGN] requête spatiale [Termes IGN] réseau sémantique [Termes IGN] ressources web [Termes IGN] service web géographique [Termes IGN] terminologie [Termes IGN] Web des données Résumé : (auteur) A growing number of spatial datasets arepublished every year. These can usually be found indedicated web portals with different structures andspecificities. However, finding the dataset that fits userneeds is a real challenge as prior knowledge of theseportals is needed to retrieve it efficiently. In this article,we present the problem of spatial dataset search andhow the use of a geographic Knowledge Graph couldimprove it. A proposed direction for future work, ex-tending these contributions, is then presented. Numéro de notice : C2021-008 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Autre URL associée : https://agile-giss.copernicus.org/articles/2/43/2021/agile-giss-2-43-2021.pdf Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-2-43-2021 En ligne : https://doi.org/10.5194/agile-giss-2-43-2021 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97855

Bayesian transfer learning for object detection in optical remote sensing images / Changsheng Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7705 - 7719 Titre : Bayesian transfer learning for object detection in optical remote sensing images Type de document : Article/Communication Auteurs : Changsheng Zhou, Auteur ; Jiangshe Zhang, Auteur ; Junmin Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7705 - 7719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] chaîne de traitement [Termes IGN] détection d'objet [Termes IGN] distribution de Fisher [Termes IGN] jeu de données localisées [Termes IGN] théorème de Bayes Résumé : (auteur) In the literature of object detection in optical remote sensing images, a popular pipeline is first modifying an off-the-shelf deep neural network, then initializing the modified network by pretrained weights on a source data set, and finally fine-tuning the network on a target data set. The procedure works well in practice but might not make full use of underlying knowledge implied by pretrained weights. In this article, we propose a novel method, referred to as Fisher regularization, for efficient knowledge transferring. Based on Bayes’ theorem, the method stores underlying knowledge into a Fisher information matrix and fine-tunes parameters based on the knowledge. The proposed method would not introduce extra parameters and is less sensitive to hyperparameters than classical weight decay. Experiments on NWPUVHR-10 and DOTA data sets show that the proposed method is effective and works well with different object detectors. Numéro de notice : A2020-679 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2983201 date de publication en ligne : 14/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2983201 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96182 [article]

Advancing the theory and practice of system evaluation: a case study in geovisual analytics of social media / Alexander Savelyev in International journal of cartography, Vol 6 n° 2 (July 2020)  

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A review of techniques for 3D reconstruction of indoor environments / Zhizhong Kang in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

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GIS-based modeling for selection of dam sites in the Kurdistan region, Iraq / Arsalan Ahmed Othman in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

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Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n° 12 (December 2019)  

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An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping / Qi Zhou in Transactions in GIS, Vol 23 n° 6 (November 2019)  

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PPD: Pyramid Patch Descriptor via convolutional neural network / Jie Wan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)  

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Semantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)  

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Surface reconstruction of incomplete datasets: A novel Poisson surface approach based on CSRBF / Jules Morel in Computers and graphics, vol 74 (August 2018)  

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Assessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets / Ming Li in Geoinformatica [en ligne], vol 22 n° 3 (July 2018)  

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Un modèle pour l’intégration spatiale et temporelle de données géolocalisées / Helbert Arenas in Revue internationale de géomatique, vol 28 n° 2 (avril - juin 2018)  

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