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A cognitive framework for road detection from high-resolution satellite images / Naveen Chandra in Geocarto international, vol 34 n° 8 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 8 [15/06/2019] . - pp 909 - 924 Titre : A cognitive framework for road detection from high-resolution satellite images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Jayanta Kumar Ghosh, Auteur ; Ashu Sharma, Auteur Année de publication : 2019 Article en page(s) : pp 909 - 924 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] représentation cognitive [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Road network extraction from high-resolution satellite (HRS) imagery is a complex task. It is an important field of research and is widely used in various cartographic applications such as updating and generating maps. The objective of this research work is to develop a novel framework, emulating human cognition, for detection of roads from HRS images. Roads network from HRS images are detected using support vector machines within the different stages of cognitive task analysis. In the first stage, basic information about the cognitive parameters which are required for image interpretation is collected. In the second stage, the rule-based method is used for knowledge representation. Lastly, during knowledge elicitation, the developed rules are used to extract roads from HRS images. The proposed method is validated using 16 HRS images of developed suburban, developed urban, emerging suburban and emerging urban region. Numéro de notice : A2019-515 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1450451 date de publication en ligne : 29/03/2018 En ligne : https://doi.org/10.1080/10106049.2018.1450451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93869 [article]

Multilane roads extracted from the OpenStreetMap urban road network using random forests / Yongyang Xu in Transactions in GIS, vol 23 n° 2 (April 2019)  

Public [article]  inTransactions in GIS > vol 23 n° 2 (April 2019) . - pp 224 - 240 Titre : Multilane roads extracted from the OpenStreetMap urban road network using random forests Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Zhong Xie, Auteur ; Liang Wu, Auteur ; Zhanlong Chen, Auteur Année de publication : 2019 Article en page(s) : pp 224 - 240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] réseau routier Résumé : (Auteur) The volunteered geographic information (VGI) collected in OpenStreetMap (OSM) has been used in many applications. Extracting multilane roads and establishing a high level of expressed detail play important roles in the field of automated cartographic generalization. An accurate and detailed extraction process benefits geographic analysis, urban region division, and road network construction, as well as transportation applications services. The road networks in OSM have a high level of detail and complex structures; however, they also include many duplicate lines, which degrade the efficiency and increase the difficulty of extracting multilane roads. To resolve these problems, this work proposes a machine‐learning‐based approach, in which the road networks are first converted from lines to polygons. Then, various geometric descriptors, including compactness, width, circularity, area, perimeter, complexity, parallelism, shape descriptor, and width‐to‐length ratio, are used to train a random forest (RF) classifier and identify the candidates. Finally, another RF is trained to evaluate the candidates using all the geometric descriptors and topological features; the outputs of this second trained RF are the predicted multilane roads. An experiment using OSM data from Beijing, China validated the proposed method, which achieves a highly effective performance when extracting multilane roads from OSM. Numéro de notice : A2019-250 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12514 date de publication en ligne : 26/12/2018 En ligne : https://doi.org/10.1111/tgis.12514 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93006 [article]

Integration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 29 - 42 Titre : Integration of lidar data and GIS data for point cloud semantic enrichment at the point level Type de document : Article/Communication Auteurs : Harith Aljumaily, Auteur ; Debra F. Laefer, Auteur ; Dolores Cuadra, Auteur Année de publication : 2019 Article en page(s) : pp 29 - 42 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Dublin (Irlande ; ville) [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] flore urbaine [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] interpolation linéaire [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] système de gestion de base de données [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Commercial aerial laser scanning is generally delivered with point-by-point metadata for object identification, but current vendor-generated classification approaches (which rely exclusively on that data) generate high misclassification rates in urban areas. To overcome this problem and provide a fully scalable solution that harnesses distributed computing capabilities, this paper introduces a novel system, employing a MapReduce framework and existing GIS-based data, to provide more detailed and accurate classification. The approach goes beyond traditional gross-level classification (roads, buildings, trees, noise) by enriching the point cloud metadata with detailed semantic information about the object type. The approach was evaluated using two datasets of differing point density, separated by eight years for the same study area in Dublin, Ireland. As evaluated against manually classified data, classification quality ranged from 76% to 91% depending upon category and only 8% remained unclassified, as opposed to the commercial vendor's classification quality which ranged from 43% to 78% with 82% left unclassified. Numéro de notice : A2019-027 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.29 date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.29 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91964 [article]

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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 19 - 28 Titre : Simultaneous chain-forming and generalization of road networks Type de document : Article/Communication Auteurs : Susanne Wenzel, Auteur ; Dimitri Bulatov, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme de Douglas-Peucker [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] axe médian [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] Graz [Termes descripteurs IGN] itération [Termes descripteurs IGN] mise à jour automatique [Termes descripteurs IGN] Munich [Termes descripteurs IGN] objet géographique linéaire [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] polyligne [Termes descripteurs IGN] primitive géométrique [Termes descripteurs IGN] relation topologique [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] squelettisation [Termes descripteurs IGN] zone urbaine [Vedettes matières IGN] Généralisation Résumé : (auteur) Streets are essential entities of urban terrain and their automatic extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological, and semantic aspects. Given a binary image representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. Thereby, we aim at a fusion of raw segments to longer chains which better match to the intuitive perception of what a street is. We propose a two-step approach for simultaneous chain-forming and generalization. First, we obtain an over-segmentation of the raw polylines. Then, a model selection approach is applied to decide whether two neighboring segments should be fused to a new geometric entity. For this purpose, we propose an iterative greedy optimization procedure in order to find a strong minimum of a cost function based on a Bayesian information criterion. Starting at the given initial raw segments, we thus can obtain a set of chains describing long alleys and important roundabouts. Within the procedure, topological attributes, such as junctions and neighborhood structures, are consistently updated, in a way that for the greedy optimization procedure, accuracy, model complexity, and topology are considered simultaneously. The results on two challenging datasets indicate the benefits of the proposed procedure and provide ideas for future work. Numéro de notice : A2019-026 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.19 date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.19 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91962 [article]

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Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree / José Antonio Martin-Jimenez in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 334 - 346 Titre : Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree Type de document : Article/Communication Auteurs : José Antonio Martin-Jimenez, Auteur ; Santiago Zazo, Auteur ; José Juan Arranz Justel, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 334 - 346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] accident de la route [Termes descripteurs IGN] arbre de décision [Termes descripteurs IGN] cohérence géométrique [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Espagne [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] indice de risque [Termes descripteurs IGN] lidar mobile [Termes descripteurs IGN] raisonnement [Termes descripteurs IGN] sécurité routière [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] triangulation de Delaunay Résumé : (auteur) Safe roads are a necessity for any society because of the high social costs of traffic accidents. This challenge is addressed by a novel methodology that allows us to evaluate road safety from Mobile LiDAR System data, taking advantage of the road alignment due to its influence on the accident rate. Automation is obtained through an inductive reasoning process based on a decision tree that provides a potential risk assessment. To achieve this, a 3D point cloud is classified by an iterative and incremental algorithm based on a 2.5D and 3D Delaunay triangulation, which apply different algorithms sequentially. Next, an automatic extraction process of road horizontal alignment parameters is developed to obtain geometric consistency indexes, based on a joint triple stability criterion. Likewise, this work aims to provide a powerful and effective preventive and/or predictive tool for road safety inspections. The proposed methodology was implemented on three stretches of Spanish roads, each with different traffic conditions that represent the most common road types. The developed methodology was successfully validated through as-built road projects, which were considered as “ground truth.” Numéro de notice : A2018-541 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.10.004 date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91565 [article]

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Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut G. Sefercik in Geocarto international, vol 33 n° 10 (October 2018)  

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Generative street addresses from satellite imagery / İlke Demir in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Large-scale road detection in forested mountainous areas using airborne topographic lidar data / António Ferraz in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)  

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Multi-criteria, graph-based road centerline vectorization using ordered weighted averaging operators / Fateme Ameri in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)

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Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation / Fateme Ameri in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)  

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Détection à haute résolution spatiale de la desserte forestière en milieu montagneux / António Ferraz in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015) 

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Semiautomated extraction of street light poles from mobile LiDAR point-clouds / Yongtao Yu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)

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Large scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)    

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