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DEM refinement by low vegetation removal based on the combination of full waveform data and progressive TIN densification / Hongchao Ma in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 260 - 271 Titre : DEM refinement by low vegetation removal based on the combination of full waveform data and progressive TIN densification Type de document : Article/Communication Auteurs : Hongchao Ma, Auteur ; Weiwei Zhou, Auteur ; Liang Zhang, Auteur Année de publication : 2018 Article en page(s) : pp 260 - 271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de Levenberg-Marquardt [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] contour [Termes descripteurs IGN] décomposition de Gauss [Termes descripteurs IGN] densification [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] filtrage de la végétation [Termes descripteurs IGN] forme d'onde pleine [Termes descripteurs IGN] hauteur de la végétation [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] signal laser [Termes descripteurs IGN] Triangulated Irregular Network Résumé : (Auteur) Filtering of low vegetation with height less than approximately 1.5 m is a challenging problem, especially in mountainous areas covered by heavy low foliage, bushes and sub-shrubberies, etc. The paper proposes an approach for obtaining a more accurate Digital Elevation Model (DEM) by removing low vegetation from point cloud. The approach combines point cloud with full waveform data, and begins by filtering point cloud by way of progressive TIN densification (PTD) method. Ground points are thus extracted, but mixed with false ground points, which are mainly from low vegetation and other manmade low objects. Gaussian decomposition by grouping Levenberg–Marquardt (LM) algorithm with F test is performed for the full waveforms corresponding to the extracted ground points. Echo widths and backscattering coefficients are calculated based on the parameters extracted from the decomposition, and used to discriminate points of low vegetation from points of other low objects, allowing the false ground points reflected from low vegetation to be labeled. New elevation values are calculated from the last echoes of the waveforms from low vegetation, and the DEM is updated by replacing the original elevations with the calculated ones. The resultants are assessed both quantitatively by check points and qualitatively by rendered DEM and contour lines generated from it. The accuracy of the refined DEM with low vegetation removal increases by 31% compared with the original DEM in the experiment, showing the effectiveness of the proposed approach. Numéro de notice : A2018-539 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.009 date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91553 [article]

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A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 8 (August 2018) . - pp 4594 - 4604 Titre : A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification Type de document : Article/Communication Auteurs : Zhen Wang, Auteur ; Liqiang Zhang, Auteur ; Liang Zhang, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 4594 - 4604 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre aléatoire [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données hétérogènes [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] pondération [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] semis de points Mots-clés libres : deep neural network with spatial pooling (DNNSP) Résumé : (Auteur) The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. Most works in deep learning have achieved a great success on regular input representations, but they are hard to be directly applied to classify point clouds due to the irregularity and inhomogeneity of the data. In this paper, a deep neural network with spatial pooling (DNNSP) is proposed to classify large-scale point clouds without rasterization. The DNNSP first obtains the point-based feature descriptors of all points in each point cluster. The distance minimum spanning tree-based pooling is then applied in the point feature representation to describe the spatial information among the points in the point clusters. The max pooling is next employed to aggregate the point-based features into the cluster-based features. To assure the DNNSP is invariant to the point permutation and sizes of the point clusters, the point-based feature representation is determined by the multilayer perception (MLP) and the weight sharing for each point is retained, which means that the weight of each point in the same layer is the same. In this way, the DNNSP can learn the features of points scaled from the entire regions to the centers of the point clusters, which makes the point cluster-based feature representations robust and discriminative. Finally, the cluster-based features are input to another MLP for point cloud classification. We have evaluated qualitatively and quantitatively the proposed method using several airborne laser scanning and terrestrial laser scanning point cloud data sets. The experimental results have demonstrated the effectiveness of our method in improving classification accuracy. Numéro de notice : A2018-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2829625 date de publication en ligne : 22/05/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2829625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91253 [article]

Crowdsourcing functions of the living city from Twitter and Foursquare data / Xiaolu Zhou in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

Public [article]  inCartography and Geographic Information Science > vol 43 n° 5 (November 2016) . - pp 393 - 404 Titre : Crowdsourcing functions of the living city from Twitter and Foursquare data Type de document : Article/Communication Auteurs : Xiaolu Zhou, Auteur ; Liang Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 393 - 404 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] Boston (Massachusetts) [Termes descripteurs IGN] Chicago (Illinois) [Termes descripteurs IGN] dimension temporelle [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] ville Résumé : (Auteur) Urban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities. Numéro de notice : A2016-690 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/15230406.2015.1128852 En ligne : https://doi.org/10.1080/15230406.2015.1128852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82018 [article]

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