Descripteur
Termes IGN > aménagement > infrastructure
infrastructure |
Documents disponibles dans cette catégorie (1601)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Attributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)
[article]
Titre : Attributing pedestrian networks with semantic information based on multi-source spatial data Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Kathleen Stewart, Auteur ; Mengyuan Fang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 31 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] données multisources
[Termes IGN] extraction de données
[Termes IGN] itinéraire piétionnier
[Termes IGN] navigation pédestre
[Termes IGN] ondelette
[Termes IGN] réseau routier
[Termes IGN] segmentation sémantique
[Termes IGN] utilisation du sol
[Termes IGN] Wuhan (Chine)Résumé : (auteur) The lack of associating pedestrian networks, i.e. the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map. Numéro de notice : A2022-083 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1902530 En ligne : https://doi.org/10.1080/13658816.2021.1902530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99480
in International journal of geographical information science IJGIS > vol 36 n° 1 (January 2022) . - pp 31 - 54[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2022011 SL Revue Centre de documentation Revues en salle Disponible
Titre : BasqueRoads: a benchmark for road network selection Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Azelle Courtial , Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 4 Projets : LostInZoom / Touya, Guillaume Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Note générale : bibliographie
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101003012).Langues : Anglais (eng) Descripteur : [Termes IGN] objet géographique
[Termes IGN] réseau routier
[Termes IGN] simplification de contour
[Termes IGN] test de performance
[Vedettes matières IGN] GénéralisationRésumé : (auteur) [début] Road network selection is one of the major issues of map generalisation, as new papers are proposed every year since the first attempts of automation in the 1990’s (Thomson & Richardson, 1995). New methods are regularly proposed because selecting roads for maps at smaller scales is a complex problem. Roads are at the same time present in maps to enable car navigation tasks, and because they are structuring elements that reveal the nature of the landscape (urban, rural, mountainous…). So road selection is not only about retaining the most important roads of the network, but the preservation of topology and connectivity is essential, as well as the preservation, or the typification of road patterns (e.g. a ring road), and the preservation of local density differences (between urban and rural areas for instance). It is rare to see comparisons of road selection techniques in the literature, because of the lack of open source in map generalisation, but also because of the lack of a common dataset to benchmark these techniques; new propositions on road selection are most of the time tied to their own dataset and use case. This is why we think that this BasqueRoads dataset could be useful to advance on this topic of road network selection. Numéro de notice : C2021-066 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-4-5-2022 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.5194/ica-abs-4-5-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99536 A comprehensive assessment of four-satellite QZSS constellation: navigation signals, broadcast ephemeris, availability, SPP, interoperability with GPS, and ISB against GPS / Xuanping Li in Survey review, vol 54 n° 382 (January 2022)
[article]
Titre : A comprehensive assessment of four-satellite QZSS constellation: navigation signals, broadcast ephemeris, availability, SPP, interoperability with GPS, and ISB against GPS Type de document : Article/Communication Auteurs : Xuanping Li, Auteur ; Pan Lin, Auteur ; Wenkun Yu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 17 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse comparative
[Termes IGN] éphémérides de satellite
[Termes IGN] erreur systématique inter-systèmes
[Termes IGN] interopérabilité
[Termes IGN] positionnement par GPS
[Termes IGN] précision du positionnement
[Termes IGN] qualité du signal
[Termes IGN] Quasi-Zenith Satellite SystemRésumé : (auteur) In this study, a comprehensive assessment of four-satellite Quasi-Zenith Satellite System (QZSS) constellation is conducted, including the quality of navigation signals, the accuracy of broadcast ephemeris, the availability of satellite constellation, the performance of single point positioning (SPP), the interoperability with GPS, and the inter-system bias (ISB) against GPS. Regarding the signal quality, no significant difference between QZSS and GPS can be found. The signal-in-space ranging error (SISRE) of QZSS satellites is 0.59–0.62 m. The service rate of QZSS-only positioning is 69.8–77.8% in QZSS service areas. A positioning accuracy of 5.70, 3.20 and 6.99 m in east, north and up directions can be achieved for the QZSS-only SPP. After introducing QZSS observations into GPS-only SPP processing, the positioning accuracy can be slightly improved. The ISB with a short-term stability of 1.75 ns behaves like systematic biases, and thus cannot be ignored in the GPS/QZSS combined SPP. Numéro de notice : A2022-112 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1858256 Date de publication en ligne : 11/12/2020 En ligne : https://doi.org/10.1080/00396265.2020.1858256 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99629
in Survey review > vol 54 n° 382 (January 2022) . - pp 17 - 33[article]Contribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)
Titre : Contribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery Type de document : Thèse/HDR Auteurs : Calimanut-Ionut Cira, Auteur Editeur : Madrid [Espagne] : Universidad politécnica de Madrid Année de publication : 2022 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat en Topographie, Géodésie et cartographie, Universidad politécnica de MadridLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal artificiel
[Termes IGN] route
[Termes IGN] segmentation sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. Remote sensing experts have been actively using deep neural networks to solve object extraction tasks in high-resolution aerial imagery by means of supervised operations. However, the extraction operation is imperfect, due to the nature of remotely sensed images (noise, obstructions, etc.), the limitations of sensing resolution, or the occlusions often present in the scenes. The road network plays an important part in transportation and, nowadays, one of the main related challenges is keeping the existent cartographic support up to date. This task can be considered very challenging due to the complex nature of the geospatial object (continuous, with irregular geometry, and significant differences in width). We also need to take into account that secondary roads represent the largest part of the road transport network, but due to the absence of clearly defined edges, and the different spectral signatures of the materials used for pavement, monitoring, and mapping them represents a great effort for public administration, and their extraction is often omitted altogether. We believe that recent advancements in machine vision can enable a successful extraction of the road structures from high-resolution, remotely sensed imagery and a greater automation of the road mapping operation. In this PhD thesis, we leverage recent computer vision advances and propose a deep learning-based end-to-end solution, capable of efficiently extracting the surface area of roads at a large scale. The novel approach is based on a disjoint execution of three different image processing operations (recognition, semantic segmentation, and post-processing with conditional generative learning) within a common framework. We focused on improving the state-of-the-art results for each of the mentioned components, before incorporating the resulting models into the proposed solution architecture. For the recognition operation, we proposed two framework candidates based on convolutional neural networks to classify roads in openly available aerial orthoimages divided in tiles of 256×256 pixels, with a spatial resolution of 0.5 m. The frameworks are based on ensemble learning and transfer learning and combine weak classifiers to leverage the strengths of different state-of-the-art models that we heavily modified for computational efficiency. We evaluated their performance on unseen test data and compared the results with those obtained by the state-of-the-art convolutional neural networks trained for the same task, observing improvements in performance metrics of 2-3%. Secondly, we implemented hybrid semantic segmentation models (where the default backbones are replaced by neural network specialised in image segmentation) and trained them with high-resolution remote sensing imagery and their correspondent ground-truth masks. Our models achieved mean increases in performance metrics of 2.7-3.5%, when compared to the original state-of-the-art semantic segmentation architectures trained from scratch for the same task. The best-performing model was integrated on a web platform that handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format, and the generation of GeoTIFF results (compatible with geospatial databases). Thirdly, the road surface area extraction task is generally carried out via semantic segmentation over remotely sensed imagery—however, this supervised learning task can be considered very costly because it requires remote sensing images labelled at pixel level and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). We consider that unsupervised learning (not requiring labelled data) can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. For this reason, we also approached the post-processing of the road surface areas obtained with the best performing segmentation model to improve the initial segmentation predictions. In this line, we proposed two post-processing operations based on conditional generative learning for deep inpainting and image-to-image translation operations and trained the networks to learn the distribution of the road network present in official cartography, using a novel dataset covering representative areas of Spain. The first proposed conditional Generative Adversarial Network (cGAN) model was trained for deep inpainting operation and obtained improvements in performance metrics of maximum 1.3%. The second cGAN model was trained for image-to-image translation, is based on a popular model heavily modified for computational efficiency (a 92.4% decrease in the number of parameters in the generator network and a 61.3% decrease in the discriminator network), and achieved a maximum increase of 11.6% in performance metrics. We also conducted a qualitative comparison to visually assess the effectiveness of the generative operations and observed great improvements with respect to the initial semantic segmentation predictions. Lastly, we proposed an end-to-end processing strategy that combines image classification, semantic segmentation, and post-processing operations to extract containing road surface area extraction from high-resolution aerial orthophotography. The training of the model components was carried out on a large-scale dataset containing more than 537,500 tiles, covering approximately 20,800 km2 of the Spanish territory, manually tagged at pixel level. The consecutive execution of the resulting deep learning models delivered higher quality results when compared to state-of-the-art implementations trained for the same task. The versatility and flexibility of the solution given by the disjointed execution of the three separate sub-operations proved its effectiveness and economic efficiency and enables the integration of a web application that alleviates the manipulation of geospatial data, while allowing for an easy integration of future models and algorithms. Resuming, applying the proposed models resulted from this PhD thesis translates to operations aimed to check if the latest existing aerial orthoimages contains the studied continuous geospatial element, to obtain an approximation of its surface area using supervised learning and to improve the initial segmentation results with post-processing methods based on conditional generative learning. The results obtained with the proposed end-to-end-solution presented in this PhD thesis improve the state-of-the-art in the field of road extraction with deep learning techniques and prove the appropriateness of applying the proposed extraction workflow for a more robust and more efficient extraction operation of the road transport network. We strongly believe that the processing strategy can be applied to enhance other similar extraction tasks of continuous geospatial elements (such as the mapping of riverbeds, or railroads), or serve as a base for developing additional extraction workflows of geospatial objects from remote sensing images. Note de contenu : 1- Introduction
2- Methodology
3- Theoretical framework
4- Litterature review
5- Road recognition: A framework based on nestion of convolutional neuronal networks and transfer learning to regognise road elements
6- Road segmentation: An approach based on hybrid semantic segmentation models to extract the surface area of rod elements from aerial orthoimagery
7- Post-processing of semantic segmentation predictions I: A conditional generative adversial network to improve the extraction of road surface areas via deep inpainting operations
8- Post-processing of semantic segmentation predictions II: A lightweight conditional generative adversial network to improve the extraction of road surface areas via image-to-image translation
9- An end-to-end road extraction solution based on regonition, segmentation, and post-processing operations for a large-scale mapping of the road transport network from aerial orthophotography
10- ConclusionsNuméro de notice : 24069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Topographie, Géodésie et cartographie : Universidad politécnica de Madrid : 2022 DOI : 10.20868/UPM.thesis.70152 En ligne : https://doi.org/10.20868/UPM.thesis.70152 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102113 Création d’un indicateur de qualité de la desserte des transports pour des parcelles à une échelle locale / Nick Lin (2022)
Titre : Création d’un indicateur de qualité de la desserte des transports pour des parcelles à une échelle locale Type de document : Mémoire Auteurs : Nick Lin, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 95 p. Format : 21 x 30 cm Note générale : bibliographie
Rapport de fin d'étude, cycle Ingénieur 3e année, cycle CarthagéoLangues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] géomercatique
[Termes IGN] grille
[Termes IGN] immobilier (secteur)
[Termes IGN] indicateur de qualité
[Termes IGN] interopérabilité
[Termes IGN] parcelle cadastrale
[Termes IGN] plan de déplacement urbain
[Termes IGN] Python (langage de programmation)
[Termes IGN] trajet (mobilité)
[Termes IGN] transport public
[Termes IGN] valeur économiqueIndex. décimale : DCAR Mémoires de l'ex DESS cartographie et du Master CARTHAGEO Résumé : (auteur) Le secteur de l’immobilier est un secteur très concurrentiel. Buildrz propose aux acteurs de ce secteur un outil d’estimation de surface de plancher constructible. Mais Buildrz voudrait aussi que son outil puisse aider les promoteurs immobiliers à fixer le prix du mètre carré. Pour ce faire, le prix hypothétique sera fixé en fonction du prix de biens similaires qui ont déjà été vendus. Il faut maintenant trouver comment caractériser des biens semblables. Pour les trouver, il faut caractériser les parcelles en fonction d’indicateurs choisis. Ce rapport se concentre sur la recherche et le développement d’un indicateur de qualité de transport en commun. Note de contenu : Introduction
1. Contexte du stage
1.a Le secteur de l'immobilier
1.b Buildrz
1.c Objectifs du stage
2. L'état de l'art
2.a L'existant
2.b L'exploitation de l'état de l'art
2.c Fichiers GTFS
3. Mise en oeuvre
3.a Les indicateurs vus
3.b Préparation
3.c Développement
3.d Résultats
ConclusionNuméro de notice : 24042 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Buildrz Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101899 Photogrammetric 3D mobile mapping of rail tracks / Philipp Glira in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)PermalinkRepresenting vector geographic information as a tensor for deep learning based map generalisation / Azelle Courtial (2022)PermalinkRoad traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods / Sulaiman Yunus in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkRobust approach for urban road surface extraction using mobile laser scanning 3D point clouds / Abdul Nurunnabi (2022)PermalinkPermalinkSpécification et qualité du réseau cyclable, application à la recherche d’itinéraires / Raphaël Bres (2022)PermalinkUrban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkPermalinkPermalinkAnalyzing contextual linking of heterogeneous information models from the domains BIM and UIM / Stefan F. Beck in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)PermalinkLa photogrammétrie appliquée au récolement des réseaux enterrés : retour d’expérience d’une méthode industrialisée / Jérôme Leroux in XYZ, n° 169 (décembre 2021)PermalinkPoint clouds for use in Building Information Models (BIM) / Robert Klinc in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)PermalinkUnderstanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)PermalinkUtility-pole detection based on interwoven column generation from terrestrial mobile Laser scanner data / Siamak Talebi Nahr in Photogrammetric record, Vol 36 n° 176 (December 2021)PermalinkSemi-automatic extraction of rural roads under the constraint of combined geometric and texture features / Hai Tan in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkAnalyzing routes in Ottoman Greater Syria using historical GIS: The 1849 Saida map / Motti Zohar in Transactions in GIS, vol 25 n° 5 (October 2021)PermalinkImpact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)PermalinkSpatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkA web GIS-based integration of 3D digital models with linked open data for cultural heritage exploration / Ikrom Nishanbaev in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkA framework for ecosystem service assessment using GIS interoperability standards / Martin Lacayo in Computers & geosciences, vol 154 (September 2021)PermalinkA multiagent systems with Petri Net approach for simulation of urban traffic networks / Mauricio Flores Geronimo in Computers, Environment and Urban Systems, vol 89 (September 2021)PermalinkA cellular-automata model for assessing the sensitivity of the street network to natural terrain / Jeeno Soa George in Annals of GIS, vol 27 n° 3 (July 2021)PermalinkImplementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)PermalinkRoad-network-based fast geolocalization / Yongfei Li in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkA scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)PermalinkFast weakly supervised detection of railway-related infrastructures in lidar acquisitions / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkRoadside tree extraction and diameter estimation with MMS lidar by using point-cloud image / Genki Takahashi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkAutomated calibration of smartphone cameras for 3D reconstruction of mechanical pipes / Reza Maalek in Photogrammetric record, vol 36 n° 174 (June 2021)PermalinkReconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles in XYZ, n° 167 (juin 2021)PermalinkReconsidering movement and exposure: Towards a more dynamic health geography / Malcolm Campbell in Geography compass, vol 15 n° 6 (June 2021)PermalinkCellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China / Jiaming Na in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkStructure-aware completion of photogrammetric meshes in urban road environment / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)PermalinkDecision making in the 4th dimension : exploring use cases and technical options for the integration of 4D BIM and GIS during construction / Huaqiu Liu Alyssa in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)PermalinkA trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks / Bozhao Li in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)PermalinkBasin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery / Zifeng Wang in Remote sensing of environment, Vol 255 (March 2021)PermalinkAggregating land-use polygons considering line features as separating map elements / Sven Gedicke in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)PermalinkCharacterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkEnhanced trajectory estimation of mobile laser scanners using aerial images / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkGIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan / Shah Naseer in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkIntegration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkLe nivellement par GNSS chez SNCF Réseau / Antoine Beuvain Pacheco in XYZ, n° 166 (mars 2021)PermalinkTopological integration of BIM and geospatial water utility networks across the building envelope / Thomas Gilbert in Computers, Environment and Urban Systems, vol 86 (March 2021)PermalinkAn improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments / Zohreh Masoumi in Geocarto international, vol 36 n° 2 ([01/02/2021])PermalinkReclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China / Lu Miao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)PermalinkAcquisition lasergrammétrique d’ouvrages d’art pour l’interopérabilité BIM-SIG, cas pratique du syndicat mixte "Routes de Guadeloupe" / Sonia Sermanson (2021)Permalink