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Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100023 Titre : Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data Type de document : Article/Communication Auteurs : Yi-Chun Lin, Auteur ; Ayman Habib, Auteur Année de publication : 2022 Article en page(s) : n° 100023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] autoroute [Termes IGN] couplage GNSS-INS [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] lidar mobile [Termes IGN] pont [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) Emerging mobile LiDAR mapping systems exhibit great potential as an alternative for mapping urban environments. Such systems can acquire high-quality, dense point clouds that capture detailed information over an area of interest through efficient field surveys. However, automatically recognizing and semantically segmenting different components from the point clouds with efficiency and high accuracy remains a challenge. Towards this end, this study proposes a semantic segmentation framework to simultaneously classify bridge components and road infrastructure using mobile LiDAR point clouds while providing the following contributions: 1) a deep learning approach exploiting graph convolutions is adopted for point cloud semantic segmentation; 2) cross-labeling and transfer learning techniques are developed to reduce the need for manual annotation; and 3) geometric quality control strategies are proposed to refine the semantic segmentation results. The proposed framework is evaluated using data from two mobile mapping systems along an interstate highway with 27 highway bridges. With the help of the proposed cross-labeling and transfer learning strategies, the deep learning model achieves an overall accuracy of 84% using limited training data. Moreover, the effectiveness of the proposed framework is verified through test covering approximately 42 miles along the interstate highway, where substantial improvement after quality control can be observed. Numéro de notice : A2022-814 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.ophoto.2022.100023 Date de publication en ligne : 24/10/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101975 [article]

A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])  

Public [article]  inGeocarto international > vol 37 n° 14 [20/07/2022] . - pp 4141 - 4157 Titre : A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway Type de document : Article/Communication Auteurs : Reza Sanayeia, Auteur ; Alireza Vafaeinejad, Auteur ; Jalal Karami, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4141 - 4157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] accident de la route [Termes IGN] autocorrélation [Termes IGN] autoroute [Termes IGN] classification par Perceptron multicouche [Termes IGN] modèle de simulation [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] Téhéran [Termes IGN] transformation en ondelettes Résumé : (auteur) The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method, initially, the raw collision data were analyzed, normalized, and classified via Geographical Information System (GIS). Partial Autocorrelation Function (PACF) was also utilized to evaluate the temporal autocorrelation for consecutive existing daily data. The results of this study showed that the proposed integrated DWT-ANN method provided higher predictive accuracy in daily traffic collision than ANN model by increasing coefficient of determination (R2) from 0.66 to 0.82. Numéro de notice : A2022-650 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1871669 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.1080/10106049.2021.1871669 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101472 [article]

Analytic hierarchy process based spatial biodiversity impact assessment model of highway broadening in Sikkim Himalaya / Polash Banerjee in Geocarto international, vol 35 n° 5 ([01/04/2020])  

Public [article]  inGeocarto international > vol 35 n° 5 [01/04/2020] . - pp 470 - 493 Titre : Analytic hierarchy process based spatial biodiversity impact assessment model of highway broadening in Sikkim Himalaya Type de document : Article/Communication Auteurs : Polash Banerjee, Auteur ; Mrinal K. Ghose, Auteur ; Ratika Pradham, Auteur Année de publication : 2020 Article en page(s) : pp 470 - 493 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de sensibilité [Termes IGN] autoroute [Termes IGN] biodiversité [Termes IGN] étude d'impact [Termes IGN] Himalaya [Termes IGN] montagne [Termes IGN] parcelle forestière [Termes IGN] processus de hiérarchisation analytique [Termes IGN] projet routier Résumé : (auteur) Spatial impacts of highway projects on biodiversity of North-Eastern Himalaya remains largely unexplored. Usually a number of ecological criteria are required in biodiversity impact assessment. However, a wide set of such criteria can be overwhelming for the decision-makers to assess the viability of such projects. SBIAM uses landscape metrics and experts’ opinion to create a single composite biodiversity value map. The weighted area loss under various project alternatives estimated from Biodiversity Value Map is compared to identify the most viable alternative. SBIAM uses AHP and curve fitting method in the biodiversity estimation. The study indicates that the highway broadening project in the study area will cause a moderate biodiversity loss. Sensitivity analysis of SBIAM indicates its robustness, and shows that forest patches near the highway are most sensitive to disturbances and patch proximity. SBIAM can be applied in varied spatial scales, terrains and development projects as a decision support tool. Numéro de notice : A2020-142 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520924 Date de publication en ligne : 22/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1520924 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94768 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 149 - 166 Titre : Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference Type de document : Article/Communication Auteurs : Heidar Rastiveis, Auteur ; Alireza Shams, Auteur ; Wayne A. Sarasua, Auteur ; Jonathan Li, Auteur Année de publication : 2020 Article en page(s) : pp 149 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] autoroute [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] extraction de points [Termes IGN] extraction du réseau routier [Termes IGN] Inférence floue [Termes IGN] lidar mobile [Termes IGN] modélisation 3D [Termes IGN] semis de points [Termes IGN] transformation de Hough Résumé : (Auteur) Mobile LiDAR systems (MLS) are rapid and accurate technologies for acquiring three-dimensional (3D) point clouds that can be used to generate 3D models of road environments. Because manual extraction of desirable features such as road traffic signs, trees, and pavement markings from these point clouds is tedious and time-consuming, automatic information extraction of these objects is desirable. This paper proposes a novel automatic method to extract pavement lane markings (LMs) using point attributes associated with the MLS point cloud based on fuzzy inference. The proposed method begins with dividing the MLS point cloud into a number of small sections (e.g. tiles) along the route. After initial filtering of non-ground points, each section is vertically aligned. Next, a number of candidate LM areas are detected using a Hough Transform (HT) algorithm and considering a buffer area around each line. The points inside each area are divided into “probable-LM” and “non-LM” clusters. After extracting geometric and radiometric descriptors for the “probable-LM” clusters and analyzing them in a fuzzy inference system, true-LM clusters are eventually detected. Finally, the extracted points are enhanced and transformed back to their original position. The efficiency of the method was tested on two different point cloud datasets along 15.6 km and 9.5 km roadway corridors. Comparing the LMs extracted using the algorithm with the manually extracted LMs, 88% of the LM lines were successfully extracted in both datasets. Numéro de notice : A2020-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.009 Date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94558 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020023	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya / Vijendra Kumar Pandey in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 168 - 187 Titre : Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya Type de document : Article/Communication Auteurs : Vijendra Kumar Pandey, Auteur ; Hamid Reza Pourghasemi, Auteur ; Milap Chand Sharma, Auteur Année de publication : 2020 Article en page(s) : pp 168 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] autoroute [Termes IGN] classification dirigée [Termes IGN] effondrement de terrain [Termes IGN] entropie maximale [Termes IGN] Himalaya [Termes IGN] image IRS-LISS [Termes IGN] image Landsat-8 [Termes IGN] Linear Imaging Self-Scanning System [Termes IGN] modèle statistique [Termes IGN] mousson [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] séparateur à vaste marge [Termes IGN] test statistique Résumé : (Auteur) The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to Ghuttu highway corridors in the Garhwal Himalaya. A landslide inventory has been prepared through field surveys and LISS-IV and Landsat 8 satellite images. The datasets of 85 landslides were categorised into training and test sets. In this study 11 landslide conditioning variables were used that are; altitude, slope angle, aspect, plan curvature, topographic wetness index, normalised difference vegetation index (NDVI), land use, soil texture, distance to rivers, distance to faults, and distance to the road. The result produced using MaxEnt and SVM model were subsequently validated using receiver operating characteristics curve (ROC) with test sets of landslide dataset. Both the models have good prediction capabilities. MaxEnt has ROC value of 0.78 while SVM has the highest prediction rate of 0.85. Numéro de notice : A2020-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1510038 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1510038 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94519 [article]

Accuracy assessment of speed values calculated from GNSS tracks of roads obtained from VGI / Antonio Tomás Mozas-Calvache in Survey review, vol 51 n° 367 (July 2019)  

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Exploitation of deep learning in the automatic detection of cracks on paved roads / Won Mo Jung in Geomatica, vol 73 n° 2 (June 2019)  

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Roads, lines, and boundary objects : a critical cartographic look at the development of the Serengeti highway / Eric J. Lovell in Cartographica, vol 52 n° 4 (Winter 2017)  

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Information géographique environnementale et conception d'infrastructure : quel détail pour l'information partagée ? / Charles-Edouard Tolmer in XYZ, n° 147 (juin - août 2016)

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Surveying a mountain highway with UAS : getting accurate results in a rough area / Matteo Luccio in Geoinformatics, vol 18 n° 7 (October - November 2015)

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La télédétection au service des études urbaines : expansion de la ville de Pondichéry entre 1973 et 2009 / Emilien Kieffer in Géomatique expert, n° 95 (01/11/2013)  

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Assessing the effect of landscape change on fauna by agent-based model simulation / Laurence Jolivet (2013)  

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A practical experience on road network generalisation for production device / Jérémy Renard (2013)    

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Shanghai subway tunnels and highways monitoring through Cosmo-SkyMed Persistent Scatterers / Daniele Perissin in ISPRS Journal of photogrammetry and remote sensing, vol 73 (September 2012)  

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Visibility monitoring using conventional roadside cameras : Emerging applications / Raouf Babari in Transportation Research - Part C: Emerging Technologies, vol 22 (June 2012)  

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