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Navigation and Ionosphere Characterization Using High-Frequency Signals: A Performance Analysis / Yoav Baumgarten in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)  

Public [article]  inNavigation : journal of the Institute of navigation > vol 69 n° 4 (Fall 2022) . - n° 546 Titre : Navigation and Ionosphere Characterization Using High-Frequency Signals: A Performance Analysis Type de document : Article/Communication Auteurs : Yoav Baumgarten, Auteur ; M.L. Psiaki, Auteur ; David L. Hysell, Auteur Année de publication : 2022 Article en page(s) : n° 546 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] algorithme de Gauss-Newton [Termes IGN] correction du signal [Termes IGN] correction ionosphérique [Termes IGN] matrice de covariance [Termes IGN] mesurage de phase [Termes IGN] modèle ionosphérique [Termes IGN] propagation du signal [Termes IGN] récepteur [Termes IGN] teneur verticale totale en électrons Résumé : (auteur) The performance of a proposed high-frequency (HF) navigation concept is analyzed using simulated data. The method relies on pseudorange and beat carrier-phase measurements of signals that propagate in the ionosphere along curved trajectories, where signals are refracted back downwards from the ionosphere. It has been demonstrated that the location of a receiver can be determined if several signals, broadcast from beacons at different locations, are received and processed at a user receiver. A challenge of determining exact signal paths is the uncertainty in the ionosphere’s electron density distribution. This is addressed by a batch filter that simultaneously estimates the receiver position along with corrections to a parametric model of the ionosphere. A previous paper developed the theory and batch filter for this concept. The present study examines its potential performance. Total horizontal position errors on the order of tens to hundreds of meters are achieved, depending on the case’s characteristics. Numéro de notice : A2022-919 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.33012/navi.546 Date de publication en ligne : 19/06/2022 En ligne : https://doi.org/10.33012/navi.546 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102448 [article]

Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds / Zhilin Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 11 (November 2022) . - n° 5705111 Titre : Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds Type de document : Article/Communication Auteurs : Zhilin Tian, Auteur ; Shihua Li, Auteur Année de publication : 2022 Article en page(s) : n° 5705111 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] bois [Termes IGN] branche (arbre) [Termes IGN] chemin le plus court, algorithme du [Termes IGN] données lidar [Termes IGN] échantillonnage de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] feuille (végétation) [Termes IGN] graphe [Termes IGN] Python (langage de programmation) [Termes IGN] segmentation [Termes IGN] semis de points Résumé : (auteur) Terrestrial light detection and ranging (lidar) is capable of resolving trees at the branch/leaf level with accurate and dense point clouds. The separation of leaf and wood components is a prerequisite for the estimation of branch/leaf-scale biophysical properties and realistic tree model reconstruction. Most existing methods have been tested on trees with similar structures; their robustness for trees of different species and sizes remains relatively unexplored. This study proposed a new graph-based leaf–wood separation (GBS) method for individual trees purely using the xyz -information of the point cloud. The GBS method fully utilized the shortest path-based features, as the shortest path can effectively reflect the structures for trees of different species and sizes. Ten types of tree data—covering tropical, temperate, and boreal species—with heights ranging from 5.4 to 43.7 m, were used to test the method performance. The mean accuracy and kappa coefficient at the point level were 94% and 0.78, respectively, and our method outperformed two other state-of-the-art methods. Through further analysis and testing, the GBS method exhibited a strong ability for detecting small and leaf-surrounded branches, and was also sufficiently robust in terms of data subsampling. Our research further demonstrated the potential of the shortest path-based features in leaf–wood separation. The entire framework was provided for use as an open-source Python package, along with our labeled validation data. Numéro de notice : A2022-853 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3218603 Date de publication en ligne : 01/11/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3218603 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102099 [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)  

Public [article]  inComputers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101866 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 [article]

Cost distances and least cost paths respond differently to cost scenario variations: a sensitivity analysis of ecological connectivity modeling / Paul Savary in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 8 (August 2022) . - pp 1652-1676 Titre : Cost distances and least cost paths respond differently to cost scenario variations: a sensitivity analysis of ecological connectivity modeling Type de document : Article/Communication Auteurs : Paul Savary, Auteur ; Jean-Christophe Foltête, Auteur ; Stéphane Garnier, Auteur Année de publication : 2022 Article en page(s) : pp 1652-1676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de sensibilité [Termes IGN] chemin le moins coûteux, algorithme du [Termes IGN] connexité (topologie) [Termes IGN] coût Résumé : (auteur) Biodiversity conservation measures designed to ensure ecological connectivity depend on the reliable modeling of species movements. Least-cost path modeling makes it possible to identify the most likely dispersal paths within a landscape and provide two items of ecological relevance: (i) the spatial location of these least-cost paths (LCPs) and (ii) the accumulated cost along them (’cost distance’, CD). This spatial analysis requires that cost values be assigned to every type of land cover. The sensitivity of both LCPs and CDs to the cost scenarios has not been comprehensively assessed across realistic landscapes and diverging cost scenarios. We therefore assessed it in diverse landscapes sampled over metropolitan France and with widely diverging cost scenarios. The spatial overlap of the LCPs was more sensitive to the cost scenario than the CD values were. In addition, highly correlated CD matrices can be derived from very different cost scenarios. Although the range of the cost values and the properties of each cost scenario significantly influenced the outputs of LCP modeling, landscape composition and configuration variables also explained their variations. Accordingly, we provide guidelines for the use of LCP modeling in ecological studies and conservation planning. Numéro de notice : A2022-614 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2014852 Date de publication en ligne : 21/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2014852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101368 [article]

Geographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis / Chuan Yin in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 360 Titre : Geographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis Type de document : Article/Communication Auteurs : Chuan Yin, Auteur ; Binyu Zhang, Auteur ; Wanzeng Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] attribut sémantique [Termes IGN] granularité (informatique) [Termes IGN] granularité d'image [Termes IGN] matrice de co-occurrence [Termes IGN] plus proche voisin, algorithme du [Termes IGN] relation sémantique [Termes IGN] réseau sémantique [Termes IGN] synonymie Résumé : (auteur) Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy. Numéro de notice : A2022-548 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070360 Date de publication en ligne : 23/06/2022 En ligne : https://doi.org/10.3390/ijgi11070360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101149 [article]

Production of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey / Faruk Yildirim in Geocarto international, vol 37 n° 8 ([01/05/2022])  

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Clustering with implicit constraints: A novel approach to housing market segmentation / Xiaoqi Zhang in Transactions in GIS, vol 26 n° 2 (April 2022)  

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Comparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)

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Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])  

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Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)  

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A robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)  

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BuyTheDips : PathLoss for improved topology-preserving deep learning-based image segmentation / Minh On Vu Ngoc (2022)  

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Road traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods / Sulaiman Yunus in Geomatics, Natural Hazards and Risk, vol 13 (2022)  

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Towards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)  

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Modeling in forestry using mixture models fitted to grouped and ungrouped data / Eric K. Zenner in Forests, vol 12 n° 9 (September 2021)  

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