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Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)
Titre : Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments Type de document : Article/Communication Auteurs : Yuhao Jiang, Auteur ; María-Jesús Lobo , Auteur ; Sidonie Christophe , Auteur ; Christophe Jouffrais, Auteur Editeur : Göttingen : Copernicus publications Année de publication : 2023 Collection : AGILE GIScience Series num. 4 Conférence : AGILE 2023, 26th international AGILE Conference on Geographic Information Science, Spatial data for design 13/06/2023 16/06/2023 Delft Pays-Bas OA Proceedings Importance : n° 29 ; 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] carrefour
[Termes IGN] carte sur mesure
[Termes IGN] carte tactile
[Termes IGN] chaîne de traitement
[Termes IGN] OpenStreetMap
[Termes IGN] personne malvoyanteIndex. décimale : 39.00 Cartographie - généralités - Cartologie Résumé : (auteur) Street intersections are very challenging for people with visual impairments. Manually produced tactile maps are an important support in teaching and assisting independent journeys as they can be customized to serve the visually impaired audience with diverse tactile reading and mobility skills in different use scenarios. But the manual map production involves a huge workload that makes the maps less accessible. This paper explores the possibility of semi-automatically producing customizable tactile maps for street intersections. It presents a parameterized semi-automated pipeline based on OSM data that allows the maps to be customized in size, map features, geometry processing choices, and symbolizations. It produces street intersection maps in two scales of three sizes, with different levels of details and styles. Numéro de notice : C2023-013 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-4-29-2023 Date de publication en ligne : 06/06/2023 En ligne : https://doi.org/10.5194/agile-giss-4-29-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103307 Semi-supervised label propagation for multi-source remote sensing image change detection / Fan Hao in Computers & geosciences, vol 170 (January 2023)
[article]
Titre : Semi-supervised label propagation for multi-source remote sensing image change detection Type de document : Article/Communication Auteurs : Fan Hao, Auteur ; Zong-Fang Ma, Auteur ; Hong Peng Tian, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 105249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification barycentrique
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] étiquette
[Termes IGN] filtrage du bruit
[Termes IGN] image multi sourcesRésumé : (auteur) Remote sensing image change detection remains a challenging task. Most existing approaches are based on fully supervised learning, but labeled data are so scarce for change detection. It is difficult to exhibit high detection performance with a limited amount of labeled data. In this paper, we propose a semi-supervised Label Propagation (SSLP) approach for multi-source remote sensing image change detection. First, a clustering label propagation (CLP) method is designed to cluster pre and post images, respectively, and assign pseudo labels to unlabeled pixel pairs that have similar mapping relationships to labeled pixel pairs. Second, a pixel density metric is investigated to filter out the data with low density and retain the data with high density, which can ensure the reliability of the propagated data. Third, a secondary expansion method based on pixel neighborhood is used to generate enough training data for training a classifier. Finally, the effectiveness of SSLP is validated on three real datasets by comparing to other related methods. Numéro de notice : A2023-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105249 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105249 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102292
in Computers & geosciences > vol 170 (January 2023) . - n° 105249[article]Sensing urban soundscapes from street view imagery / Tianhong Zhao in Computers, Environment and Urban Systems, vol 99 (January 2023)
[article]
Titre : Sensing urban soundscapes from street view imagery Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Xiucheng Liang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] bruit (audition)
[Termes IGN] distribution spatiale
[Termes IGN] image Streetview
[Termes IGN] paysage sonore
[Termes IGN] planification urbaine
[Termes IGN] pollution acoustique
[Termes IGN] Shenzhen
[Termes IGN] Singapour
[Termes IGN] ville durable
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) A healthy acoustic environment is an essential component of sustainable cities. Various noise monitoring and simulation techniques have been developed to measure and evaluate urban sounds. However, sensing large areas at a fine resolution remains a great challenge. Based on machine learning, we introduce a new application of street view imagery — estimating large-area high-resolution urban soundscapes, investigating the premise that we can predict and characterize soundscapes without laborious and expensive noise measurements. First, visual features are extracted from street-level imagery using computer vision. Second, fifteen soundscape indicators are identified and a survey is conducted to gauge them solely from images. Finally, a prediction model is constructed to infer the urban soundscape by modeling the non-linear relationship between them. The results are verified with extensive field surveys. Experiments conducted in Singapore and Shenzhen using half a million images affirm that street view imagery enables us to sense large-scale urban soundscapes with low cost but high accuracy and detail, and provides an alternative means to generate soundscape maps. reaches 0.48 by evaluating the predicted results with field data collection. Further novelties in this domain are revealing the contributing visual elements and spatial laws of soundscapes, underscoring the usability of crowdsourced data, and exposing international patterns in perception. Numéro de notice : A2023-014 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101915 Date de publication en ligne : 20/11/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101915 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102131
in Computers, Environment and Urban Systems > vol 99 (January 2023) . - n° 101915[article]Simplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area / David Marín-García in Sustainable Cities and Society, vol 88 (January 2023)
[article]
Titre : Simplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area Type de document : Article/Communication Auteurs : David Marín-García, Auteur ; Juan Rubio-Gómez-Torga, Auteur ; Manuel Duarte-Pinheiro, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104251 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] acquisition de données
[Termes IGN] Andalousie
[Termes IGN] apprentissage automatique
[Termes IGN] bassin hydrographique
[Termes IGN] bâtiment
[Termes IGN] cartographie des risques
[Termes IGN] coefficient de corrélation
[Termes IGN] dommage matériel
[Termes IGN] évaluation des paramètres
[Termes IGN] image à haute résolution
[Termes IGN] modèle de simulation
[Termes IGN] zone inondableRésumé : (auteur) Flooding due to overflowing rivers affects the construction elements of many buildings. Although significant progress has been made in predicting this damage, there is still a need to continue studying this issue. For this reason, the main goal of this research focuses on finding out if, based on a small dataset of cases of a given area, it is possible to predict at least three degrees of affectation in buildings, considering only three environmental factors (minimum distance from the river, unevenness and possible water communication). To meet this goal, the methodological approach followed considers scientific literature review and collection and analysis of a small dataset from 101 buildings that have been affected by floods in the Guadalquivir River basin (Andalusia. Spain). After analyzing this data, algorithms based on machine learning (ML) are applied to predict the degree of affection. The results, analysis and conclusions indicate that, if the study focuses on a specific area and similar buildings, using a correlation matrix and ML algorithms such as the "Decision Tree" with cross-validation, around 90% can be achieved in the "Recall" and "Precision" of "High-Level-Affection" class, and an “Accuracy” around 80% in general. Numéro de notice : A2023-006 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2022.104251 Date de publication en ligne : 15/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104251 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102093
in Sustainable Cities and Society > vol 88 (January 2023) . - n° 104251[article]Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach / Bowen Niu in Geocarto international, vol 38 n° 1 ([01/01/2023])
[article]
Titre : Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach Type de document : Article/Communication Auteurs : Bowen Niu, Auteur ; Quanlong Feng, Auteur ; Jianyu Yang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2164361 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] cartographie thématique
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contour
[Termes IGN] déchet
[Termes IGN] fusion de données
[Termes IGN] image à très haute résolution
[Termes IGN] Inde
[Termes IGN] Mexique
[Termes IGN] urbanisationRésumé : (auteur) The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people’s wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. Numéro de notice : A2023-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2164361 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1080/10106049.2022.2164361 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102407
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2164361[article]PermalinkA survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)PermalinkThe cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkTree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning / Stefano Puliti in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkUnderstanding public perspectives on fracking in the United States using social media big data / Xi Gong in Annals of GIS, vol 29 n° 1 (January 2023)PermalinkUrban infrastructure expansion and artificial light pollution degrade coastal ecosystems, increasing natural-to-urban structural connectivity / Moisés A. Aguilera in Landscape and Urban Planning, vol 229 (January 2023)PermalinkVers une optimisation de la diffusion de l’information dans une ville intelligente / Malika Grim-Yefsah (2023)PermalinkVisual attention and recognition differences based on expertise in a map reading and memorability study / Merve Keskin in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)PermalinkPermalinkAssessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])Permalink