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Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery / Maryam Hosseini in Computers, Environment and Urban Systems, vol 101 (April 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 101 (April 2023) . - n° 101950 Titre : Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery Type de document : Article/Communication Auteurs : Maryam Hosseini, Auteur ; Andres Sevtsuk, Auteur ; Fabio Miranda, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'objet [Termes IGN] Etats-Unis [Termes IGN] image aérienne [Termes IGN] navigation pédestre [Termes IGN] segmentation sémantique [Termes IGN] système d'information géographique [Termes IGN] trottoir [Termes IGN] vision par ordinateur Résumé : (auteur) While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. Numéro de notice : A2023-187 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2023.101950 Date de publication en ligne : 22/02/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2023.101950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102961 [article]

Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 199 - 211 Titre : Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence Type de document : Article/Communication Auteurs : Sidi Wu, Auteur ; Konrad Schindler, Auteur ; Magnus Heitzler, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 199 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte ancienne [Termes IGN] cartographie historique [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] données anciennes [Termes IGN] matrice de co-occurrence [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence. Numéro de notice : A2023-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.01.021 Date de publication en ligne : 14/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102804 [article]

Comparative use of PPK-integrated close-range terrestrial photogrammetry and a handheld mobile laser scanner in the measurement of forest road surface deformation / Remzi Eker in Measurement, vol 206 (January 2023)  

Public [article]  inMeasurement > vol 206 (January 2023) Titre : Comparative use of PPK-integrated close-range terrestrial photogrammetry and a handheld mobile laser scanner in the measurement of forest road surface deformation Type de document : Article/Communication Auteurs : Remzi Eker, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre [Termes IGN] analyse comparative [Termes IGN] cartographie et localisation simultanées [Termes IGN] chemin forestier [Termes IGN] déformation de surface [Termes IGN] lidar mobile [Termes IGN] positionnement cinématique [Termes IGN] semis de points [Termes IGN] structure-from-motion [Termes IGN] télémétrie laser terrestre [Termes IGN] Turquie Résumé : (auteur) This study aimed to compare a handheld mobile laser scanning (HMLS), called TORCH that uses the SLAM algorithm, and a PPK-integrated close-range terrestrial photogrammetry (CRTP) to measure forest road surface deformation. The PPK-integrated CRTP includes a multiband GNSS-module and a camera mounted on a 5-m prism pole. 3D point-clouds were gathered/produced at three different dates with approximately 3-month intervals. And then road surface deformations were determined by applying the M3C2 algorithm. Each method was compared by considering some advantages and disadvantages. PPK-integrated CRTP, which could only be used in areas where the GPS signal is not blocked, provided highly denser 3D point clouds than HMLS. However, for the first period, the difference of mean deformation values between the two methods was not statistically significant, whereas it was statistically significant for the second period. Both methods can be suggested to use in forest road surface deformation yet considering their limitations. Numéro de notice : A2023-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.measurement.2022.112322 Date de publication en ligne : 14/12/2022 En ligne : https://doi.org/10.1016/j.measurement.2022.112322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102330 [article]

Mobile mapping mesh change detection and update / Teng Wu (2023)  

Public Titre : Mobile mapping mesh change detection and update Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Cédric Demonceaux, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : 7 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] maillage par triangles [Termes IGN] mosaïquage d'images [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] Stéréopolis [Termes IGN] système de numérisation mobile [Termes IGN] vision par ordinateur Résumé : (auteur) Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simu- lation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions. In this paper, we propose a fully automatic pipeline to address these two issues by formulating the problem of merging meshes with different quality, coverage and acquisition time. Our method is based on a combined distance and visibility based change detection, a time series analysis to assess the sustainability of changes, a mesh mosaicking based on a global boolean optimization and finally a stitching of the resulting mesh pieces boundaries with triangle strips. Finally, our method is demonstrated on Robotcar and Stereopolis datasets. Numéro de notice : P2023-003 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2303.07182 Date de publication en ligne : 13/03/2023 En ligne : https://doi.org/10.48550/arXiv.2303.07182 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102860

Improving deep learning on point cloud by maximizing mutual information across layers / Di Wang in Pattern recognition, vol 131 (November 2022)  

Public [article]  inPattern recognition > vol 131 (November 2022) . - n° 108892 Titre : Improving deep learning on point cloud by maximizing mutual information across layers Type de document : Article/Communication Auteurs : Di Wang, Auteur ; Lulu Tang, Auteur ; Xu Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] entropie de Shannon [Termes IGN] information sémantique [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] transformation géométrique [Termes IGN] vision par ordinateur [Termes IGN] visualisation 3D Résumé : (auteur) It is a fundamental and vital task to enhance the perception capability of the point cloud learning network in 3D machine vision applications. Most existing methods utilize feature fusion and geometric transformation to improve point cloud learning without paying enough attention to mining further intrinsic information across multiple network layers. Motivated to improve consistency between hierarchical features and strengthen the perception capability of the point cloud network, we propose exploring whether maximizing the mutual information (MI) across shallow and deep layers is beneficial to improve representation learning on point clouds. A novel design of Maximizing Mutual Information (MMI) Module is proposed, which assists the training process of the main network to capture discriminative features of the input point clouds. Specifically, the MMI-based loss function is employed to constrain the differences of semantic information in two hierarchical features extracted from the shallow and deep layers of the network. Extensive experiments show that our method is generally applicable to point cloud tasks, including classification, shape retrieval, indoor scene segmentation, 3D object detection, and completion, and illustrate the efficacy of our proposed method and its advantages over existing ones. Our source code is available at https://github.com/wendydidi/MMI.git. Numéro de notice : A2022-780 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.1016/j.patcog.2022.108892 Date de publication en ligne : 08/07/2022 En ligne : https://doi.org/10.1016/j.patcog.2022.108892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101859 [article]

Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest / Daniel Kükenbrink in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

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Deep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)  

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Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms / Yunhao Li in Transactions in GIS, vol 26 n° 6 (September 2022)  

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PKS: A photogrammetric key-frame selection method for visual-inertial systems built on ORB-SLAM3 / Arash Azimi in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)  

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Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners / Artu Ellmann in Survey review, vol 54 n° 385 (July 2022)  

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Investigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)  

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Spatial-temporal attentive LSTM for vehicle-trajectory prediction / Rui Jiang in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)  

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A method of vision aided GNSS positioning using semantic information in complex urban environment / Rui Zhai in Remote sensing, vol 14 n° 4 (February-2 2022)  

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3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)  

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Adaptation d'un algorithme SLAM pour la vision panoramique multi-expositions dans des scènes à haute gamme dynamique / Eva Goichon (2022)  

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