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Measuring differential access to facilities between population groups using spatial Lorenz curves and related indices / Gordon A. Cromley in Transactions in GIS, Vol 23 n° 6 (novembre 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 6 (novembre 2019) . - pp 1332 - 1351 Titre : Measuring differential access to facilities between population groups using spatial Lorenz curves and related indices Type de document : Article/Communication Auteurs : Gordon A. Cromley, Auteur Année de publication : 2019 Article en page(s) : pp 1332 - 1351 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] accessibilité [Termes descripteurs IGN] coefficient de Gini [Termes descripteurs IGN] commerce [Termes descripteurs IGN] courbe de Lorenz [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] équipement collectif [Termes descripteurs IGN] inégalité [Termes descripteurs IGN] Ohio (Etats-Unis) [Termes descripteurs IGN] population [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] service public [Termes descripteurs IGN] sociologie [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Access to certain types of facilities can promote health and well‐being. When population and facilities are not uniformly distributed across the landscape, inequities in accessibility may occur. Current research into GIS‐based accessibility measures has focused primarily on spatial inequities between different geographic locations but not directly on differences in accessibility between subgroups of the population. The research presented here develops a new method for measuring differential accessibility to facilities between various segments of the population. The method extends concepts and techniques in spatial point pattern analysis that account for the spatial structure of demand and its relationship to supply. In this approach, the traditional Lorenz curve and its associated indices, the Gini coefficient and the dissimilarity index, which are used to measure inequality, are recast in spatial terms for measuring differences in accessibility between population subgroups. An analysis of spatial accessibility to grocery stores in Akron, OH illustrates the value of the spatial Lorenz curve and its associated indices compared to other methods. Numéro de notice : A2019-567 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12577 date de publication en ligne : 06/10/2019 En ligne : https://onlinelibrary.wiley.com/doi/10.1111/tgis.12577 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94419 [article]

Spatially constrained regionalization with multilayer perceptron / Michael Govorov in Transactions in GIS, Vol 23 n° 5 (October 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 5 (October 2019) . - pp 1048 - 1077 Titre : Spatially constrained regionalization with multilayer perceptron Type de document : Article/Communication Auteurs : Michael Govorov, Auteur ; Giedre Beconyte, Auteur ; Gennady Gienko, Auteur ; Victor Putrenko, Auteur Année de publication : 2019 Article en page(s) : pp 1048 - 1077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] données géologiques [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] régionalisation [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] segmentation par graphes d'adjacence de régions [Termes descripteurs IGN] Ukraine [Termes descripteurs IGN] uranium Résumé : (auteur) In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance. Numéro de notice : A2019-552 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12557 date de publication en ligne : 09/07/2019 En ligne : https://doi.org/10.1111/tgis.12557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94202 [article]

Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165 Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] champ aléatoire conditionnel [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification basée sur les régions [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] modèle 3D de l'espace urbain [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075 [article]

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A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals / Olivier Bock in Statistics and Computing, vol 29 Online First (May 2019)  

Public [article]  inStatistics and Computing > vol 29 Online First (May 2019) . - pp 1 - 13 Titre : A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Xavier Collilieux , Auteur ; François Guillamon, Auteur ; Emilie Lebarbier, Auteur ; Claire Pascal, Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 13 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] analyse de variance [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] points de rupture [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variabilité Résumé : (Auteur) This work is motivated by an application for the homogenization of global navigation satellite system (GNSS)-derived integrated water vapour series. Indeed, these series are affected by abrupt changes due to equipment changes or environmental effects. The detection and correction of the series from these changes are a crucial step before any use for climate studies. In addition to these abrupt changes, it has been observed in the series a non-stationary of the variability. We propose in this paper a new segmentation model that is a breakpoint detection in the mean model of a Gaussian process with heterogeneous variance on known time intervals. In this segmentation case, the dynamic programming algorithm used classically to infer the breakpoints cannot be applied anymore. We propose a procedure in two steps: we first estimate robustly the variances and then apply the classical inference by plugging these estimators. The performance of our proposed procedure is assessed through simulation experiments. An application to real GNSS data is presented. Numéro de notice : A2019-165 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11222-019-09853-5 date de publication en ligne : 03/05/2019 En ligne : https://doi.org/10.1007/s11222-019-09853-5 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92571 [article]

Detecting and characterizing downed dead wood using terrestrial laser scanning / Tuomas Yrttimaa in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 76 - 90 Titre : Detecting and characterizing downed dead wood using terrestrial laser scanning Type de document : Article/Communication Auteurs : Tuomas Yrttimaa, Auteur ; Ninni Saarinen, Auteur ; Ville Luoma, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 76 - 90 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] bois mort [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] placette d'échantillonnage [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] rastérisation [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] tronc [Termes descripteurs IGN] volume en bois Résumé : (Auteur) Dead wood is a key forest structural component for maintaining biodiversity and storing carbon. Despite its important role in a forest ecosystem, quantifying dead wood alongside standing trees has often neglected when investigating the feasibility of terrestrial laser scanning (TLS) in forest inventories. The objective of this study was therefore to develop an automatic method for detecting and characterizing downed dead wood with a diameter exceeding 5 cm using multi-scan TLS data. The developed four-stage algorithm included (1) RANSAC-cylinder filtering, (2) point cloud rasterization, (3) raster image segmentation, and (4) dead wood trunk positioning. For each detected trunk, geometry-related quality attributes such as dimensions and volume were automatically determined from the point cloud. For method development and validation, reference data were collected from 20 sample plots representing diverse southern boreal forest conditions. Using the developed method, the downed dead wood trunks were detected with an overall completeness of 33% and correctness of 76%. Up to 92% of the downed dead wood volume were detected at plot level with mean value of 68%. We were able to improve the detection accuracy of individual trunks with visual interpretation of the point cloud, in which case the overall completeness was increased to 72% with mean proportion of detected dead wood volume of 83%. Downed dead wood volume was automatically estimated with an RMSE of 15.0 m3/ha (59.3%), which was reduced to 6.4 m3/ha (25.3%) as visual interpretation was utilized to aid the trunk detection. The reliability of TLS-based dead wood mapping was found to increase as the dimensions of dead wood trunks increased. Dense vegetation caused occlusion and reduced the trunk detection accuracy. Therefore, when collecting the data, attention must be paid to the point cloud quality. Nevertheless, the results of this study strengthen the feasibility of TLS-based approaches in mapping biodiversity indicators by demonstrating an improved performance in quantifying ecologically most valuable downed dead wood in diverse forest conditions. Numéro de notice : A2019-205 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.007 date de publication en ligne : 16/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92668 [article]

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Journées de la recherche 2019 / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)

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Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. Hossain in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

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A local projection-based approach to individual tree detection and 3-D crown delineation in multistoried coniferous forests using high-density airborne LiDAR data / Aravind Harikumar in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)  

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Integration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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Supervized segmentation with graph-structured deep metric learning / Loïc Landrieu (2019)  

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Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree / José Antonio Martin-Jimenez in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)  

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A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

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Three-dimensional building façade segmentation and opening area detection from point clouds / S.M. Iman Zolanvari in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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