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Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks / Lauwrence V. Stanislawski in International journal of cartography, Vol 6 n° 1 (March 2020)  

Public [article]  inInternational journal of cartography > Vol 6 n° 1 (March 2020) . - pp 4 - 21 Titre : Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks Type de document : Article/Communication Auteurs : Lauwrence V. Stanislawski, Auteur ; Michael P. Finn, Auteur ; Barbara P. Buttenfield, Auteur Année de publication : 2020 Article en page(s) : pp 4 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme de généralisation [Termes descripteurs IGN] altitude [Termes descripteurs IGN] base de données hydrographiques [Termes descripteurs IGN] cartographie des flux [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] cours d'eau [Termes descripteurs IGN] drainage [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] pente [Termes descripteurs IGN] perméabilité du sol [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] réseau hydrographique [Termes descripteurs IGN] ruissellement [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] traitement automatique de données [Vedettes matières IGN] Généralisation Résumé : (auteur) Automated generalization software must accommodate multi-scale representations of hydrographic networks across a variety of geographic landscapes, because scale-related hydrography differences are known to vary in different physical conditions. While generalization algorithms have been tailored to specific regions and landscape conditions by several researchers in recent years, the selection and characterization of regional conditions have not been formally defined nor statistically validated. This paper undertakes a systematic classification of landscape types in the conterminous United States to spatially subset the country into workable units, in preparation for systematic tailoring of generalization workflows that preserve hydrographic characteristics. The classification is based upon elevation, standard deviation of elevation, slope, runoff, drainage and bedrock density, soil and bedrock permeability, area of inland surface water, infiltration-excess of overland flow, and a base flow index. A seven class solution shows low misclassification rates except in areas of high landscape diversity such as the Appalachians, Rocky Mountains, and Western coastal regions. Numéro de notice : A2020-070 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2018.1443759 date de publication en ligne : 20/03/2018 En ligne : https://doi.org/10.1080/23729333.2018.1443759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94632 [article]

Tree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 971 - 981 Titre : Tree annotations in LiDAR data using point densities and convolutional neural networks Type de document : Article/Communication Auteurs : Ananya Gupta, Auteur ; Jonathan Byrne, Auteur ; David Moloney, Auteur Année de publication : 2020 Article en page(s) : pp 971 - 981 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] Dublin (Irlande ; ville) [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] extraction d'arbres [Termes descripteurs IGN] image spectrale [Termes descripteurs IGN] Montréal (Québec) [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] voxel [Termes descripteurs IGN] zone urbaine Résumé : (auteur) LiDAR provides highly accurate 3-D point clouds. However, data need to be manually labeled in order to provide subsequent useful information. Manual annotation of such data is time-consuming, tedious, and error prone, and hence, in this article, we present three automatic methods for annotating trees in LiDAR data. The first method requires high-density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3-D convolutional neural network on low-density LiDAR data sets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelization process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an F score of 82.1% on the ISPRS benchmark data set, comparable to the state-of-the-art methods but with increased efficiency. Numéro de notice : A2020-095 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2942201 date de publication en ligne : 11/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2942201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94658 [article]

Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 691 - 703 Titre : Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data Type de document : Article/Communication Auteurs : Guo-Hui Yao, Auteur ; Chang-qing Ke, Auteur ; Xiaobing Zhou, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 691 - 703 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] bande L [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] glacier [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interferométrie différentielle [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] segmentation Résumé : (auteur) To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification. Numéro de notice : A2020-077 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939430 date de publication en ligne : 25/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2939430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94613 [article]

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 (November 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 6 (November 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)  

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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)  

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

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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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