Centre dedocumentation
scientifique

A history of laser scanning, Part 1: space and defense applications / Adam P. Spring in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 419-429 Titre : A history of laser scanning, Part 1: space and defense applications Type de document : Article/Communication Auteurs : Adam P. Spring, Auteur Année de publication : 2020 Article en page(s) : pp 419-429 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] capteur à balayage [Termes descripteurs IGN] défense nationale [Termes descripteurs IGN] histoire des sciences et techniques [Termes descripteurs IGN] navigation autonome [Termes descripteurs IGN] secteur spatial [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] véhicule sans pilote Résumé : (Auteur) This article presents the origins and evolution of midrange terrestrial laser scanning (TLS), spanning primarily from the 1950s to the time of publication. Particular attention is given to developments in hardware and software that document the physical dimensions of a scene as a point cloud. These developments include parameters for accuracy, repeatability, and resolution in the midrange—millimeter and centimeter levels when recording objects at building and landscape scales up to a kilometer away. The article is split into two parts: Part one starts with early space and defense applications, and part two examines the survey applications that formed around TLS technologies in the 1990s. The origins of midrange TLS, ironically, begin in space and defense applications, which shaped the development of sensors and information processing via autonomous vehicles. Included are planetary rovers, space shuttles, robots, and land vehicles designed for relative navigation in hostile environments like space and war zones. Key people in the midrange TLS community were consulted throughout the 10-year period over which this article was written. A multilingual and multidisciplinary literature review—comprising media written or produced in Chinese, English, French, German, Japanese, Italian, and Russian—was also an integral part of this research. Numéro de notice : A2020-381 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.419 date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.419 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95426 [article]

A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 193 - 213 Titre : A method for urban population density prediction at 30m resolution Type de document : Article/Communication Auteurs : Krishnachandran Balakrishnan, Auteur Année de publication : 2020 Article en page(s) : pp 193 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] hauteur du bâti [Termes descripteurs IGN] image Cartosat-1 [Termes descripteurs IGN] Inde [Termes descripteurs IGN] logiciel de traitement d'image [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modélisation du bâti [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] véhicule automobile Résumé : (auteur) This paper proposes a new method for urban population density prediction at 30 m resolution. Using data for Bangalore, the paper demonstrates that population within each 30 m residential built-up cell can be modeled as a function of cell-level data on street density and building heights and ward-level data on car ownership. Building-height data were generated from Cartosat-1 stereo imagery using an open-source satellite stereo image processing software. Using this building-height data in conjunction with the other datasets, the paper demonstrates that a 30 m resolution population density surface can be generated such that, when summed to the ward level, the median absolute percentage error between predicted population and known census population at the ward level is 8.29%. The paper also shows that the relationship between population density, street density, building height, and ward level car ownership is spatially non-stationary. A fine-grained understanding of urban population densities, as enabled by the proposed method, can be beneficial to research, policy, and practice related to cities. Numéro de notice : A2020-168 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1687014 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1687014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94839 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2020031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS / Dogus Guler in Annals of GIS, vol 26 n° 2 (April 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 2 (April 2020) . - pp 169 - 189 Titre : Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS Type de document : Article/Communication Auteurs : Dogus Guler, Auteur ; Tahsin Yomralioglu, Auteur Année de publication : 2020 Article en page(s) : pp 169 - 189 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] approche holistique [Termes descripteurs IGN] électricité [Termes descripteurs IGN] outil d'aide à la décision [Termes descripteurs IGN] pondération [Termes descripteurs IGN] processus d'analyse hiérarchisée floue [Termes descripteurs IGN] station [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] véhicule automobile [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Electric vehicles arouse interest since they not only contribute economies of countries in the context of dependency to oil but also support to more livable and sustainable urban areas. The location selection of electric vehicle charging stations is one of the most vital topics in order to enhance the use of electric vehicles. In this sense, the aim of this paper is to propose an approach that integrates Geographic Information System (GIS) techniques and Multi-Criteria Decision Making (MCDM) methods for finding suitable locations of the electric vehicle charging stations. In this regard, the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP) methods are used to calculate the weights of criteria. While the two different weights for each criterion are obtained by means of AHP in terms of environmental impact and accessibility, another weight for each criterion is obtained as a means of applying the FAHP. The intersection of three different suitability indexes is determined so as to achieve a holistic, credible result. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the alternative locations. The results show that the proposed approach offers a notable solution to be selected suitable charging station locations. Moreover, policymakers and administrators could benefit from these results in order to make efficient decisions for forward planning and strategies. Numéro de notice : A2020-322 Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1737226 date de publication en ligne : 09/03/2020 En ligne : https://doi.org/10.1080/19475683.2020.1737226 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95189 [article]

Scene context-driven vehicle detection in high-resolution aerial images / Chao Tao in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7339 - 7351 Titre : Scene context-driven vehicle detection in high-resolution aerial images Type de document : Article/Communication Auteurs : Chao Tao, Auteur ; Li Mi, Auteur ; Yansheng Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7339 - 7351 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] véhicule automobile Résumé : (auteur) As the spatial resolution of remote sensing images is improving gradually, it is feasible to realize “scene-object” collaborative image interpretation. Unfortunately, this idea is not fully utilized in vehicle detection from high-resolution aerial images, and most of the existing methods may be promoted by considering the variability of vehicle spatial distribution in different image scenes and treating vehicle detection tasks scene-specific. With this motivation, a scene context-driven vehicle detection method is proposed in this paper. At first, we perform scene classification using the deep learning method and, then, detect vehicles in roads and parking lots separately through different vehicle detectors. Afterward, we further optimize the detection results using different postprocessing rules according to different scene types. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in terms of higher detection accuracy rate and lower false alarm rate. Numéro de notice : A2019-535 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2912985 date de publication en ligne : 03/06/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2912985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94131 [article]

Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 172 - 186 Titre : Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2019 Article en page(s) : pp 172 - 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] véhicule automobile Résumé : (Auteur) Real-time unmanned aerial vehicles (UAVs)-based thermal infrared images processing, due to high spatial resolution and knowledge of the various infrared radiant energy level distribution of solid bodies, has important applications such as monitoring and control of the various phenomena in different natural situations. One of these applications is monitoring the ground vehicles in cities by using detection or semantic segmentation of them in the thermal images. In this research, our purpose is to improve the performance of deep learning combined model by using Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) specifications for the segmentation of the ground vehicles from UAV-based thermal infrared imagery. The proposed model is studied in three steps. First, designing the proposed model by using an encoder-decoder structure and addition of extracted features from convolutional layers and restricted Boltzmann machine in the network. Second, the implementation of the research goals on four sets of UAV-based thermal infrared imagery named NPU_CS_UAV_IR_DATA that was collected from some streets of China by using FLIR TAU2 thermal infrared sensor in 2017. Finally, analyzing the performance of the proposed model by using five state-of-the-art models in semantic segmentation. The results evaluated the performance of the proposed model as a robust model with the average precision and average processing time of approximately 0.97, and 19.73 s for all datasets, respectively. Numéro de notice : A2019-315 Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.009 date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93341 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019091	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019093	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019092	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Vehicle detection in aerial images / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)  

Permalink

De la navigation connectée à la voiture autonome - partie 2 Et mon tout est un véhicule autonome / Hubert d' Erceville in SIGmag, n° 17 (juin 2018)

Permalink

De la navigation connectée à la voiture autonome - partie 1 la navigation au coeur de l'automobile / Hubert d' Erceville in SIGmag, n° 16 (mars 2018)

Permalink

Deep learning based vehicular mobility models for intelligent transportation systems / Jian Zhang (2018)  

Permalink

An analysis of movement patterns between zones using taxi GPS data / Zhanlong Chen in Transactions in GIS, vol 21 n° 6 (December 2017)  

Permalink

Generating a hazard map of dynamic objects using lidar mobile mapping / Alexander Schlichting in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 12 (December 2016)  

Permalink

Quelle carte numérique pour le véhicule autonome ? / Pascal Vasseur in Transport environnement circulation TEC, n° 231 (novembre 2016)  

Permalink

Modeling spatiotemporal topological relationships between moving object trajectories along road networks based on region connection calculus / Linbing Ma in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

Permalink

A novel methodology for identifying environmental exposures using GPS data / Andreea Cetateanu in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

Permalink

Quo vademus : Future automotive GNSS positioning in urban scenarios / Martin Escher in GPS world, vol 27 n° 5 (May 2016)  

Permalink