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Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets / Li Geng in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)
[article]
Titre : Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets Type de document : Article/Communication Auteurs : Li Geng, Auteur ; Ke Zhang, Auteur Année de publication : 2023 Article en page(s) : n° 7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] Etats-Unis
[Termes IGN] géobalise
[Termes IGN] mobilité urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routier
[Termes IGN] TwitterRésumé : (auteur) Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ≈0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. Numéro de notice : A2023-105 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.3390/ijgi12010007 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.3390/ijgi12010007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102433
in ISPRS International journal of geo-information > vol 12 n° 1 (January 2023) . - n° 7[article]Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density / Luyen K. Bui in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 7 (January 2023)
[article]
Titre : Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density Type de document : Article/Communication Auteurs : Luyen K. Bui, Auteur ; Craig L. Glennie, Auteur Année de publication : 2023 Article en page(s) : n° 100028 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Hawaii (Etats-Unis)
[Termes IGN] incertitude des données
[Termes IGN] interpolation
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset. Numéro de notice : A2023-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100028 Date de publication en ligne : 17/12/2023 En ligne : https://doi.org/10.1016/j.ophoto.2022.100028 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102494
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 7 (January 2023) . - n° 100028[article]Forest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])
[article]
Titre : Forest road extraction from orthophoto images by convolutional neural networks Type de document : Article/Communication Auteurs : Erhan Çalişkan, Auteur ; Yusuf Sevim, Auteur Année de publication : 2023 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chemin forestier
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction automatique
[Termes IGN] orthoimage
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Continuous monitoring of the forest road infrastructure and keeping track of the changes occurred are important for forestry practices, map updating, forest fire and forest transport decision support systems. In this context, the most of up to date data can be obtained by automatic forest road extraction from satellite images via machine learning (ML). Acquiring sufficient data is one of the most important factors which affect the success of ML and deep learning (DL). DL architectures yield more consistent results for complex data sets compared with ML algorithms. In the present study, three different deep learning (Resnet-18, MobileNet-V2 and Xception) architectures with semantic segmentation architecture were compared for extracting the forest road network from high-resolution orthophoto images and the results were analyzed. The architectures were evaluated through a multiclass statistical analysis based precision, recall, F1 score, intersection over union and overall accuracy (OA). The results present significant values obtained by the Resnet-18 architecture, with 99.72% of OA and 98.87% of precision and by the MobileNet-V2 architecture, with 97.76% of OA and 98.28% of precision. Also the results show that Resnet-18, MobileNet-V2 semantic segmentation architectures can be used efficiently for forest road extraction. Numéro de notice : A2022-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2060319 Date de publication en ligne : 06/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2060319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100380
in Geocarto international > vol 38 n° inconnu [01/01/2023] . - pp[article]Generation of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data / Ji Won Suh in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)
[article]
Titre : Generation of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data Type de document : Article/Communication Auteurs : Ji Won Suh, Auteur ; William Ouimet, Auteur Année de publication : 2023 Article en page(s) : pp 37 - 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] ArcGIS Desktop
[Termes IGN] Connecticut (Etats-Unis)
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] estompage
[Termes IGN] image ancienne
[Termes IGN] MNS lidar
[Termes IGN] orthophotoplan numérique
[Termes IGN] photographie aérienne
[Termes IGN] point d'appui
[Termes IGN] structure-from-motionRésumé : (auteur) This study presents a method to generate historical orthomosaics using Structure-from-Motion (SfM ) photogrammetry, historical aerial photographs, and lidar data, and then analyzes the horizontal accuracy and factors that can affect the quality of historical orthoimagery products made with these approaches. Two sets of historical aerial photographs (1934 and 1951) were analyzed, focused on the town of Woodstock in Connecticut, U.S.A. Ground control points (GCPs) for georeferencing were obtained by overlaying multiple data sets, including lidar elevation data and derivative hillshades, and recent orthoimagery. Root-Mean-Square Error values of check points (CPs ) for 1934 and 1951 orthomosaics without extreme outliers are 0.83 m and 1.37 m, respectively. Results indicate that orthomosaics can be used for standard mapping and geographic information systems (GIS ) work according to the ASPRS 1990 accuracy standard. In addition, results emphasize that three main factors can affect the horizontal accuracy of orthomosaics: (1) types of CPs, (2) the number of tied photos, and (3) terrain. Numéro de notice : A2023-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00063R2 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.14358/PERS.22-00063R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102355
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 1 (January 2023) . - pp 37 - 46[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2023011 SL Revue Centre de documentation Revues en salle Disponible A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)
[article]
Titre : A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction Type de document : Article/Communication Auteurs : Jiayi Li, Auteur ; Xin Huang, Auteur ; Yujin Feng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5600812 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approche hiérarchique
[Termes IGN] carte de profondeur
[Termes IGN] déformation d'objet
[Termes IGN] effet de profondeur cinétique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image aérienne
[Termes IGN] jeu de données
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stéréoscopique
[Termes IGN] reconstruction d'image
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Multiview stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning-based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this article describes a new benchmark dataset called the LuoJia-MVS dataset ( https://irsip.whu.edu.cn/resources/resources_en_v2.php ), as well as a new deep neural network known as the hierarchical deformable cascade MVS network (HDC-MVSNet). The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model (DSM) built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of 3-D convolutional blocks, and “true 3-D” deformable 3-D convolutional layers are specifically designed by considering the aforementioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods and confirmed that the newly built dataset can provide an effective benchmark. Numéro de notice : A2023-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3234694 En ligne : https://doi.org/10.1109/TGRS.2023.3234694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102488
in IEEE Transactions on geoscience and remote sensing > vol 61 n° 1 (January 2023) . - n° 5600812[article]In-camera IMU angular data for orthophoto projection in underwater photogrammetry / Erica Nocerino in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 7 (January 2023)PermalinkA machine learning method for Arctic lakes detection in the permafrost areas of Siberia / Piotr Janiec in European journal of remote sensing, vol 56 n° 1 (2023)PermalinkMachine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)PermalinkA method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination / Kaili Zhang in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkMitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkModeling the gravitational effects of ocean tide loading at coastal stations in the China earthquake gravity network based on GOTL software / Chuandong Zhu in Journal of applied geodesy, vol 17 n° 1 (January 2023)PermalinkMulti-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkA nonlinear Gauss-Helmert model and its robust solution for seafloor control point positioning / Yingcai Kuang in Marine geodesy, vol 46 n° 1 (January 2023)PermalinkPermalinkProduction of orthophoto map using mobile photogrammetry and comparative assessment of cost and accuracy with satellite imagery for corridor mapping: a case study in Manesar, Haryana, India / Manuj Dev in Annals of GIS, vol 29 n° 1 (January 2023)Permalink