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Incremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)
[article]
Titre : Incremental road network update method with trajectory data and UAV remote sensing imagery Type de document : Article/Communication Auteurs : Jianxin Qin, Auteur ; Wenjie Yang, Auteur ; Tao Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 502 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] données spatiotemporelles
[Termes IGN] extraction du réseau routier
[Termes IGN] image captée par drone
[Termes IGN] mise à jour de base de données
[Termes IGN] modèle de Markov caché
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routier
[Termes IGN] segmentation
[Termes IGN] trace au solRésumé : (auteur) GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method. Numéro de notice : A2022-791 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi11100502 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.3390/ijgi11100502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101904
in ISPRS International journal of geo-information > vol 11 n° 10 (October 2022) . - n° 502[article]Machine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)
[article]
Titre : Machine learning for spatial analyses in urban areas: a scoping review Type de document : Article/Communication Auteurs : Ylenia Casali, Auteur ; Nazli Yonca Aydin, Auteur ; Tina Comes, Auteur Année de publication : 2022 Article en page(s) : n° 104050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse spatio-temporelle
[Termes IGN] apprentissage automatique
[Termes IGN] distribution spatiale
[Termes IGN] espace urbain
[Termes IGN] littérature
[Termes IGN] source de données
[Termes IGN] urbanisme
[Termes IGN] ville durable
[Termes IGN] zone urbaineRésumé : (auteur) The challenges for sustainable cities to protect the environment, ensure economic growth, and maintain social justice have been widely recognized. Along with the digitization, availability of large datasets, Machine Learning (ML) and Artificial Intelligence (AI) are promising to revolutionize the way we analyze and plan urban areas, opening new opportunities for the sustainable city agenda. Especially urban spatial planning problems can benefit from ML approaches, leading to an increasing number of ML publications across different domains. What is missing is an overview of the most prominent domains in spatial urban ML along with a mapping of specific applied approaches. This paper aims to address this gap and guide researchers in the field of urban science and spatial data analysis to the most used methods and unexplored research gaps. We present a scoping review of ML studies that used geospatial data to analyze urban areas. Our review focuses on revealing the most prominent topics, data sources, ML methods and approaches to parameter selection. Furthermore, we determine the most prominent patterns and challenges in the use of ML. Through our analysis, we identify knowledge gaps in ML methods for spatial data science and data specifications to guide future research. Numéro de notice : A2022-765 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104050 Date de publication en ligne : 12/07/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104050 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101786
in Sustainable Cities and Society > vol 85 (October 2022) . - n° 104050[article]PPP rapid ambiguity resolution using Android GNSS raw measurements with a low-cost helical antenna / Xingxing Li in Journal of geodesy, vol 96 n° 10 (October 2022)
[article]
Titre : PPP rapid ambiguity resolution using Android GNSS raw measurements with a low-cost helical antenna Type de document : Article/Communication Auteurs : Xingxing Li, Auteur ; Hao Wang, Auteur ; Xin Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 65 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] Androïd
[Termes IGN] antenne
[Termes IGN] données GNSS
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement ponctuel précis
[Termes IGN] précision du positionnement
[Termes IGN] rapport signal sur bruit
[Termes IGN] résolution d'ambiguïté
[Termes IGN] téléphone intelligentRésumé : (auteur) The release of GNSS raw measurement acquisition privileges on Google Android makes high-precision positioning on the low-cost smart devices possible. However, influenced by the inner linearly polarized antenna, the pseudorange and carrier phase noises of the smart device are much larger than those of the geodetic receiver. As a result, only meter-level positioning accuracy can be obtained based on the smart device’s original antenna. With the external survey-grade antenna enhancing, positioning accuracy of decimeter-level to centimeter-level can be obtained, but it still takes tens of minutes to converge and fix the ambiguity. However, a PPP-RTK method is proposed to achieve rapid integer ambiguity resolution (AR) with the regional atmospheric augmentation. In this contribution, an uncombined PPP-RTK model is developed using Android GNSS raw measurements with an external antenna, after carefully considering the coexistence of single- and dual-frequency signals on smart devices. A low-cost helical antenna is employed to enhance the Android GNSS data as it is capable to provide observation data of comparable quality with the survey-grade antenna and has several advantages of low weight, low-power consumption, and portability. Moreover, a series of quality control methods in the data preprocessing and ambiguity resolution are proposed for smartphone-based PPP-RTK to enhance the positioning results. To validate the proposed method, several experiments are carried out using raw measurements of Xiaomi Mi8 with an external low-cost helical antenna. The result shows that the ambiguity fixed solution can be obtained within 3 min in both static and kinematic scenarios. After the ambiguity resolution, centimeter-level positioning accuracy of (1.7, 2.1, 4.1) cm and (7.2, 4.5, 8.1) cm for the east, north, and up components can be achieved in static and kinematic scenarios, respectively. Numéro de notice : A2022-735 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01661-6 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1007/s00190-022-01661-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101706
in Journal of geodesy > vol 96 n° 10 (October 2022) . - n° 65[article]Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data / Michael J. Campbell in Computers, Environment and Urban Systems, vol 97 (October 2022)
[article]
Titre : Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data Type de document : Article/Communication Auteurs : Michael J. Campbell, Auteur ; Philip E. Dennison, Auteur ; Matthew Thompson, Auteur Année de publication : 2022 Article en page(s) : n° 101866 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] base de données localisées
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] chemin le moins coûteux, algorithme du
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] durée de trajet
[Termes IGN] mobilité urbaine
[Termes IGN] navigation pédestre
[Termes IGN] pente
[Termes IGN] planification urbaine
[Termes IGN] trace GPS
[Termes IGN] Utah (Etas-Unis)Résumé : (auteur) Accurately predicting pedestrian travel times is critically valuable in emergency response, wildland firefighting, disaster management, law enforcement, and urban planning. However, the relationship between pedestrian movement and landscape conditions is highly variable between individuals, making it difficult to estimate how long it will take broad populations to get from one location to another on foot. Although functions exist for predicting travel rates, they typically oversimplify the inherent variability of pedestrian travel by assuming the effects of landscapes on movement are universal. In this study, we present an approach for predicting the variability in pedestrian travel rates and times using a large, crowdsourced database of GPS tracks. Acquired from the outdoor recreation website AllTrails, these tracks represent nearly 2000 hikes on a diverse range of trails in Utah and California, USA. We model travel rates as a function of the slope of the terrain by generating a series of non-linear percentile models from the 2.5 th to the 97.5 th by 2.5 percentiles. The 50 th percentile model, representing the hiking speed of the typical individual, demonstrates marked improvement over existing slope-travel rate functions when compared to an independent test dataset. Our results demonstrate novel capacity to estimate travel time variability, with modeled percentiles being able to predict actual percentiles with less than 10% error. Travel rate functions can also be applied to least cost path analysis to provide variability in travel times. Numéro de notice : A2022-599 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2022.101866 Date de publication en ligne : 20/08/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101866 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101452
in Computers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101866[article]Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)
[article]
Titre : Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding Type de document : Article/Communication Auteurs : Faxi Yuan, Auteur ; Yuanchang Xu, Auteur ; Qingchun Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101870 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] catastrophe naturelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] graphe
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] polynôme de Chebysheff
[Termes IGN] prévention des risques
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] zone urbaineRésumé : (auteur) The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans. Numéro de notice : A2022-656 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101870 Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101870 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101506
in Computers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101870[article]3D LiDAR aided GNSS/INS integration fault detection, localization and integrity assessment in urban canyons / Zhipeng Wang in Remote sensing, vol 14 n° 18 (September-2 2022)PermalinkDevelopment of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkAmbiguity resolution for smartphone GNSS precise positioning: effect factors and performance / Bofeng Li in Journal of geodesy, vol 96 n° 9 (September 2022)PermalinkAssessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)PermalinkCartographic enclosure and urban cadastral mapping in the Ethiopian Somali capital / Romy Emmenegger in Cartographica, vol 57 n° 3 (September 2022)PermalinkDeep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)PermalinkExploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)PermalinkFlood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)PermalinkIdentification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators / Luis Izquierdo-Horna in Computers, Environment and Urban Systems, vol 96 (September 2022)PermalinkA map matching-based method for electric vehicle charging station placement at directional road segment level / Zhoulin Yu in Sustainable Cities and Society, vol 84 (September 2022)Permalink