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A spatiotemporal structural graph for characterizing land cover changes / Bin Wu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
[article]
Titre : A spatiotemporal structural graph for characterizing land cover changes Type de document : Article/Communication Auteurs : Bin Wu, Auteur ; Ballang Yu, Auteur ; Song Shu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 397 - 425 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] changement d'occupation du sol
[Termes IGN] changement temporel
[Termes IGN] graphe
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] objet géographique
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena. Numéro de notice : A2021-041 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1778706 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1080/13658816.2020.1778706 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96753
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 397 - 425[article]3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)
Titre : 3D urban scene understanding by analysis of LiDAR, color and hyperspectral data Type de document : Thèse/HDR Auteurs : David Duque-Arias, Auteur ; Beatriz Marcotegui, Directeur de thèse ; Jean-Emmanuel Deschaud, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 191 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université PSL, Spécialité : Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de scène 3D
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] image optique
[Termes IGN] modélisation géométrique de prise de vue
[Termes IGN] monde virtuel
[Termes IGN] morphologie mathématique
[Termes IGN] navigation autonome
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] traitement d'imageIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Point clouds have attracted the interest of the research community over the last years. Initially, they were mostly used for remote sensing applications. More recently, thanks to the development of low-cost sensors and the publication of some open source libraries, they have become very popular and have been applied to a wider range of applications. One of them is the autonomous vehicle where many efforts have been made in the last century to make it real. A very important bottleneck nowadays for the autonomous vehicle is the evaluation of the proposed algorithms. Due to the huge number of possible scenarios, it is not feasible to perform it in real life. An alternative is to simulate virtual environments where all possible configurations can be set up beforehand. However, they are not as realistic as the real world is. In this thesis, we studied the pertinence of including hyperspectral images in the creation of new virtual environments. Furthermore, we proposed new methods to improve 3D scene understanding for autonomous vehicles. During this research, we addressed the following topics. Firstly, we analyzed the spectrum in color and hyperspectral images because it provides a description about the electromagnetic radiation at different frequencies. Some applications rely only on visible colors. In other cases, such as the characterization of materials, the study of the invisible range is required. For this purpose, we proposed a simplified spectrum representation that preserves its diversity, the Graph-based color lines (GCL) model. Secondly, we studied the integration of hyperspectral images, color images and point clouds in urban scenes. The analysis was carried out by using the data acquired during this thesis in the context of the REPLICA project FUI 24. We inspected spectral signatures of different objects and reflectance histograms of the images. The obtained results demonstrate that urban scenes are challenging scenarios for current technology of hyperspectral cameras due to the presence of uncontrolled light conditions and moving actors. Thirdly, we worked with 3D point clouds from urban scenes that have proved to be a reliable type of data, much less sensitive to illumination variations than cameras. They are more accurate than color images and permit to obtain precise 3D models of urban environments. Deep learning techniques are very popular in this domain. A key element of these techniques is the loss function that drives the optimization process. We proposed two new loss functions to perform semantic segmentation tasks: power Jaccard loss and hierarchical loss. They obtained a higher performance in evaluated scenarios than classical losses not only in 3D point clouds but also in color and gray scale images. Moreover, we proposed a new dataset (Paris Carla 3D Dataset) composed of synthetic and real point clouds from urban scenes. It is expected to be used by the research community for different automatic tasks such as semantic segmentation, instance segmentation and scene completion. Finally, we conducted a detailed analysis of the influence of RGB features in semantic segmentation of urban point clouds. We compared several training scenarios and identified that color systematically improves the performance in certain classes. It demonstrates that including a more detailed description of the spectrum, when the hyperspectral cameras technology increases its sensitivity, can be useful to improve scene description of urban scenes. Note de contenu : 1- Introduction
2- Data used in this thesis
3- Graph based color lines (GCL)
4- Study of REPLICA data
5- Power Jaccard losses for semantic segmentation
6- Segmentation of point clouds
7- Conclusions and perspectivesNuméro de notice : 28464 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris sciences et lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://pastel.hal.science/tel-03434199/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99076 Détection et reconstruction 3D d’arbres urbains par segmentation de nuages de points : apport de l’apprentissage profond / Victor Alteirac (2021)
Titre : Détection et reconstruction 3D d’arbres urbains par segmentation de nuages de points : apport de l’apprentissage profond Type de document : Mémoire Auteurs : Victor Alteirac, Auteur Editeur : Strasbourg : Institut National des Sciences Appliquées INSA Strasbourg Année de publication : 2021 Importance : 115 p. Format : 21 x 30 cm Note générale : bibliographie
mémoire de fin d'études INSA Strasbourg, Spécialité : Topographie et Master IRIVLangues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] ilot thermique urbain
[Termes IGN] microclimat
[Termes IGN] modélisation 3D
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation
[Termes IGN] semis de pointsIndex. décimale : INSAS Mémoires d'ingénieur de l'INSA Strasbourg - Topographie, ex ENSAIS Résumé : (auteur) Les avancées technologiques de ces dernières années ont permis à l’intelligence artificielle de se démocratiser et de devenir de plus en plus performante. Présente dans de nombreux domaines comme l’automobile, l’aviation ou encore la médecine, cette dernière est très sollicitée. Les laboratoires de recherche tels que ICube abordent des problèmes complexes nécessitant des données tridimensionnelles. Au cours de ce projet de fin d'études, des recherches sont réalisées concernant la segmentation d'arbres urbains pour la modélisation et la prédiction d'ilots de chaleur en ville. Après un état de l'art, le réseau de neurones retenu est PointNet ++. À la suite du paramétrage de ce dernier, une acquisition est effectuée dans la ville de Strasbourg. Le nuage de points ainsi récupéré est donc segmenté en utilisant l’entrainement du réseau. Des résultats de près de 96% de bonnes segmentations sont obtenus sur la détection d’arbres, avec un résultat global de 85%. Pour finir, les segmentations sont alors récupérées pour la modélisation d'arbres afin d’amorcer leur insertion dans des modèles de microclimat. Tout cela apporte des perspectives très prometteuses pour l’automatisation de la segmentation d’arbres urbains grâce aux techniques d’apprentissage profond. Note de contenu : 1- Présentation de l’étude
2- Etat de l’art, segmentation automatique
3- Choix et implémentation du réseau de neurones
4- Application et adaptation du réseau
ConclusionNuméro de notice : 15271 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Mémoire ingénieur INSAS Organisme de stage : Laboratoire ICube En ligne : http://eprints2.insa-strasbourg.fr/4493/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99408 Finding the most navigable path in road networks / Ramneek Kaur in Geoinformatica, vol 25 n° 1 (January 2021)
[article]
Titre : Finding the most navigable path in road networks Type de document : Article/Communication Auteurs : Ramneek Kaur, Auteur ; Vikram Goyal, Auteur ; Venkata M. V. Gunturi, Auteur Année de publication : 2021 Article en page(s) : pp 207 - 240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] arc
[Termes IGN] calcul d'itinéraire
[Termes IGN] durée de trajet
[Termes IGN] programmation dynamique
[Termes IGN] réseau routierRésumé : (Auteur) Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks. Numéro de notice : A2021-095 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00428-5 Date de publication en ligne : 03/01/2021 En ligne : https://doi.org/10.1007/s10707-020-00428-5 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96935
in Geoinformatica > vol 25 n° 1 (January 2021) . - pp 207 - 240[article]Hyperspectral and multispectral image fusion via graph Laplacian-guided coupled tensor decomposition / Yuanyang Bu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : Hyperspectral and multispectral image fusion via graph Laplacian-guided coupled tensor decomposition Type de document : Article/Communication Auteurs : Yuanyang Bu, Auteur ; Yong-Qiang Zhao, Auteur ; Jize Xue, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 648 - 662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] calcul tensoriel
[Termes IGN] équation de Laplace
[Termes IGN] fusion d'images
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] optimisation (mathématiques)
[Termes IGN] tenseur
[Termes IGN] théorie des variétésRésumé : (auteur) We propose a novel graph Laplacian-guided coupled tensor decomposition (gLGCTD) model for fusion of hyperspectral image (HSI) and multispectral image (MSI) for spatial and spectral resolution enhancements. The coupled Tucker decomposition is employed to capture the global interdependencies across the different modes to fully exploit the intrinsic global spatial–spectral information. To preserve local characteristics, the complementary submanifold structures embedded in high-resolution (HR)-HSI are encoded by the graph Laplacian regularizations. The global spatial–spectral information captured by the coupled Tucker decomposition and the local submanifold structures are incorporated into a unified framework. The gLGCTD fusion framework is solved by a hybrid framework between the proximal alternating optimization (PAO) and the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real data sets demonstrate that the gLGCTD fusion method is superior to state-of-the-art fusion methods with a more accurate reconstruction of the HR-HSI. Numéro de notice : A2021-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992788 Date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992788 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96738
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 648 - 662[article]Introducing diversion graph for real-time spatial data analysis with location based social networks / Sameera Kannangara (2021)PermalinkPermalinkPermalinkNonlocal graph convolutional networks for hyperspectral image classification / Lichao Mou in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkA graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkRecognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)PermalinkA semantic graph database for the interoperability of 3D GIS data / Eva Savina Malinverni in Applied geomatics, vol 12 n° 3 (September 2020)PermalinkA point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkLearning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkPermalinkPermalinkImage processing applications in object detection and graph matching: from Matlab development to GPU framework / Beibei Cui (2020)PermalinkPermalinkPermalinkA space-time varying graph for modelling places and events in a network / Ikechukwu Maduako in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)PermalinkAnalysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)PermalinkComputing and querying strict, approximate, and metrically refined topological relations in linked geographic data / Blake Regalia in Transactions in GIS, vol 23 n° 3 (June 2019)PermalinkDeeply integrating linked data with geographic information systems / Gengchen Mai in Transactions in GIS, vol 23 n° 3 (June 2019)PermalinkPiecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)PermalinkA graph-based approach for the structural analysis of road and building layouts / Mathieu Domingo in Geo-spatial Information Science, vol 22 n° 1 (March 2019)Permalink