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Detecting and analyzing mobility hotspots using surface networks / Y. Hu in Transactions in GIS, vol 18 n° 6 (December 2014)  

Public [article]  inTransactions in GIS > vol 18 n° 6 (December 2014) . - pp 911 – 935 Titre : Detecting and analyzing mobility hotspots using surface networks Type de document : Article/Communication Auteurs : Y. Hu, Auteur ; Harvey J. Miller, Auteur ; X. Li, Auteur Année de publication : 2014 Article en page(s) : pp 911 – 935 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] estimation par noyau [Termes IGN] mobilité humaine [Termes IGN] objet mobile [Termes IGN] Shanghai (Chine) [Termes IGN] théorie des graphes [Termes IGN] topologie Résumé : (Auteur) Capabilities for collecting and storing data on mobile objects have increased dramatically over the past few decades. A persistent difficulty is summarizing large collections of mobile objects. This article develops methods for extracting and analyzing hotspots or locations with relatively high levels of mobility activity. We use kernel density estimation (KDE) to convert a large collection of mobile objects into a smooth, continuous surface. We then develop a topological algorithm to extract critical geometric features of the surface; these include critical points (peaks, pits and passes) and critical lines (ridgelines and course-lines). We connect the peaks and corresponding ridgelines to produce a surface network that summarizes the topological structure of the surface. We apply graph theoretic indices to analytically characterize the surface and its changes over time. To illustrate our approach, we apply the techniques to taxi cab data collected in Shanghai, China. We find increases in the complexity of the hotspot spatial distribution during normal activity hours in the late morning, afternoon and evening and a spike in the connectivity of the hotspot spatial distribution in the morning as taxis concentrate on servicing travel to work. These results match with scientific and anecdotal knowledge about human activity patterns in the study area. Numéro de notice : A2014-577 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12076 Date de publication en ligne : 17/02/2014 En ligne : https://doi.org/10.1111/tgis.12076 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74767 [article]

Selective omission of road features based on mesh density for automatic map generalization / J. Chen in International journal of geographical information science IJGIS, vol 23 n° 7-8 (july 2009)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 23 n° 7-8 (july 2009) . - pp 1013 - 1032 Titre : Selective omission of road features based on mesh density for automatic map generalization Type de document : Article/Communication Auteurs : J. Chen, Auteur ; Y. Hu, Auteur ; Z. Li, Auteur ; R. Zhao, Auteur ; L. Meng, Auteur Année de publication : 2009 Article en page(s) : pp 1013 - 1032 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] 1:50.000 [Termes IGN] Chine [Termes IGN] densité [Termes IGN] généralisation cartographique automatisée [Termes IGN] réseau routier [Termes IGN] révision cartographique [Termes IGN] route [Vedettes matières IGN] Généralisation Résumé : (Auteur) Selection of roads is an intractable generalization operation due to the difficulty in retaining the density difference and connectivity of a road network. This paper proposes a novel approach of selective omission for roads based on mesh density. The density of a road network and its local variations are calculated using meshes as units. Since maps at different scales usually reveal different densities, different density thresholds for road networks are determined on the basis of theoretical analysis and empirical study of mesh densities on maps at different scales. The selection process starts with the identification of the meshes that have a density beyond the threshold. The mesh with the largest density is first treated. Its bounding road segments are ordered according to their relative importance. The least important segment is eliminated. The remaining segments are then merged with the adjacent mesh, thus forming a new mesh. The selection procedure is repeated until none of the meshes has a density beyond the threshold. Such a process of eliminating road segments and merging meshes can ensure the road network connectivity. In this study, the meshes are classified depending on the types of road segment. For the different mesh types, their density thresholds are set to be different, which can be used as an indicator for the preservation of the density difference. This proposed approach considers topological, geometric and semantic properties of the road network. It was applied to two sets of road networks, and the results of selection are convincing. This methodology has now been adopted for the updating of 1:50,000 maps of China. Copyright Taylor & Francis Numéro de notice : A2009-344 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810802070730 En ligne : https://doi.org/10.1080/13658810802070730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29974 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-09051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-09052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Mapping the height and above-ground biomass of a mixed forest using lidar and stereo Ikonos images / Benoît Saint-Onge in International Journal of Remote Sensing IJRS, vol 29 n° 5 (March 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n° 5 (March 2008) . - pp 1277 - 1294 Titre : Mapping the height and above-ground biomass of a mixed forest using lidar and stereo Ikonos images Type de document : Article/Communication Auteurs : Benoît Saint-Onge, Auteur ; Y. Hu, Auteur ; Cédric Vega , Auteur Année de publication : 2008 Article en page(s) : pp 1277 - 1294 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] données lidar [Termes IGN] forêt [Termes IGN] hauteur des arbres [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] masse végétale [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] modèle stéréoscopique Résumé : (Auteur) Our objective was to assess the accuracy of the forest height and biomass estimates derived from an Ikonos stereo pair and a lidar digital terrain model (DTM). After the Ikonos scenes were registered to the DTM with submetric accuracy, tree heights were measured individually by subtracting the photogrammetric elevation of the treetop from the lidar ground-level elevation of the tree base. The low residual error (1.66 m) of the measurements confirmed the joint geometric accuracy of the combined models. Matched images of the stereo pair were then used to create a digital surface model. The latter was transformed to a canopy height model (CHM) by subtracting the lidar DTM. Plotwise height percentiles were extracted from the Ikonos-lidar CHM and used to predict the average dominant height and above-ground biomass. The coefficient of determination reached 0.91 and 0.79 for average height and biomass, respectively. In both cases, the accuracy of the Ikonos-lidar CHM predictions was slightly lower than that of the all-lidar reference CHM. Although the CHM heights did not saturate at moderate biomass levels, as do multispectral or radar images, values above 300 Mg ha-1 could not be predicted accurately by the Ikonos-lidar or by the all-lidar CHM. Copyright Taylor & Francis Numéro de notice : A2008-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701736505 En ligne : https://doi.org/10.1080/01431160701736505 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29075 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08031	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Hierarchical recovery of digital terrain models from single and multiple return lidar data / Y. Hu in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 4 (April 2005)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 4 (April 2005) . - pp 425 - 433 Titre : Hierarchical recovery of digital terrain models from single and multiple return lidar data Type de document : Article/Communication Auteurs : Y. Hu, Auteur ; V. Tao, Auteur Année de publication : 2005 Article en page(s) : pp 425 - 433 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] approche hiérarchique [Termes IGN] classification par seuillage sur la limite la plus proche [Termes IGN] convolution (signal) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] interpolation [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de terrain Résumé : (Auteur) A hierarchical terrain recovery approach for generating digital terrain models (DTM) from single and multiple returns lidar data is presented in this paper. The algorithm can intelligently discriminate between terrain and non-terrain points by using adaptive and robust filtering and interpolation techniques. It processes the image pyramid, bottom-up and top-down, to estimate high-quality terrain surfaces from lidar data with varying point densities and scene complexities. Using road and vegetation information, the algorithm is able to adaptively adjust thresholds to be suited to process changing contents in a large scene. The algorithms have been tested extensively using multiple medium - and high - resolution lidar datasets. The worst-case error is below 25 cm Linear Error (LE) 90 comparing the derived DTMs and the raw range images on bare surfaces when testing several lidar datasets. Copyright ASPRS Numéro de notice : A2005-589 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.14358/PERS.71.4.425 En ligne : https://doi.org/10.14358/PERS.71.4.425 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27724 [article]

Photogrammetric exploitation of Ikonos imagery for mapping applications / C. Vincent Tao in International Journal of Remote Sensing IJRS, vol 25 n° 14 (July 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 14 (July 2004) . - pp 2833 - 2853 Titre : Photogrammetric exploitation of Ikonos imagery for mapping applications Type de document : Article/Communication Auteurs : C. Vincent Tao, Auteur ; Y. Hu, Auteur ; W. Jiang, Auteur Année de publication : 2004 Article en page(s) : pp 2833 - 2853 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] extraction automatique [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] modèle par fonctions rationnelles [Termes IGN] orthorectification [Termes IGN] reconstruction 3D [Termes IGN] stéréoscopie [Termes IGN] test de performance Résumé : (Auteur) The launch of IKONOS by Space Imaging opens a new era of high resolution satellite imagery collection and mapping. The IKONOS satellite simultaneously acquires 1m panchromatic and 4m multi-spectral images in four bands that are suitable for high accuracy mapping applications. Space Imaging uses the rational function model (RFM), also known as rational polynomial camera model, instead of the physical IKONOS sensor model to communicate the imaging geometry. As revealed by recent studies from several researchers, the RFM retains the full capability of performing photogrammetric processing in absence of the physical sensor model. This paper presents some RFM-based processing methods and mapping applications developed for 3D feature extraction, orthorectification and RPC model refinement using IKONOS imagery. Comprehensive tests are performed to test the accuracy of 3D reconstruction and orthorectification and to validate the feasibility of the model refinement techniques. Numéro de notice : A2004-293 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160310001618392 En ligne : https://doi.org/10.1080/01431160310001618392 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26820 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-04121	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

3D reconstruction methods based on the rational function model / C. Vincent Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 7 (July 2002)  

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Updating solutions of the rational function model using additional control information / Y. Hu in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 7 (July 2002)  

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