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Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 50 - 62 Titre : Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification Type de document : Article/Communication Auteurs : Congcong Wen, Auteur ; Lina Yang, Auteur ; Xiang Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 50 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] contrainte de direction [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] traitement de nuage de points Résumé : (auteur) Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. Traditional point cloud classification methods mostly focus on the development of handcrafted point geometry features and employ machine learning-based classification models to conduct point classification. In recent years, the advances of deep learning models have caused researchers to shift their focus towards machine learning-based models, specifically deep neural networks, to classify airborne LiDAR point clouds. These learning-based methods start by transforming the unstructured 3D point sets to regular 2D representations, such as collections of feature images, and then employ a 2D CNN for point classification. Moreover, these methods usually need to calculate additional local geometry features, such as planarity, sphericity and roughness, to make use of the local structural information in the original 3D space. Nonetheless, the 3D to 2D conversion results in information loss. In this paper, we propose a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we design a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method demonstrates superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model achieves a new state-of-the-art performance on powerline, car, and facade categories. Moreover, to demonstrate the generalization abilities of the proposed method, we conduct further experiments on the 2019 Data Fusion Contest Dataset. Our proposed method achieves superior performance than the comparing methods and accomplishes an overall accuracy of 95.6% and an average F1 score of 0.810. Numéro de notice : A2020-119 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.004 date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94743 [article]

Modelling discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines / Kassel Hingee in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 159 - 171 Titre : Modelling discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines Type de document : Article/Communication Auteurs : Kassel Hingee, Auteur ; Peter Caccetta, Auteur ; Louis Caccetta, Auteur Année de publication : 2019 Article en page(s) : pp 159 - 171 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] Australie [Termes descripteurs IGN] discontinuité [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] filtrage de points [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] valeur aberrante Résumé : (Auteur) Models of ground surface elevations are crucial to many applications of remotely sensed data, including estimates of the height relative to ground of non-ground objects, such as buildings and vegetation. In highly engineered regions, such as cities, there are many discontinuities in both the ground surface and the surface of non-ground objects. Sub-metre resolution elevation data for these regions are increasingly available. At these resolutions there is sufficient information and a growing need to improve model accuracies by incorporating discontinuities. Here we provide a new method for generating high resolution models of discontinuous ground surfaces from breakline data and digital surface models derived from remotely sensed data. The method uses segment based filtering, outlier removal and multiresolution thin-plate spline surface fitting. Breaklines are included in the fitted surface using partial derivatives and a breakline-aware method for transferring between different resolutions. We demonstrate our method using elevation data derived from photogrammetry for suburban regions of Perth, Western Australia, and Vaihingen, Germany. We produced ground surface models with noticeable qualitative and quantitative improvements when breaklines are included, at an increased computational cost of approximately 10% when all other parameters remained the same. For LiDAR derived elevations, we report our residual error against a number of other methods recorded using the ISPRS Ground Filtering Test Sites. Numéro de notice : A2019-314 Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.004 date de publication en ligne : 24/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93340 [article]

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3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 200 - 214 Titre : 3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Karl Segl, Auteur ; Luis Guanter, Auteur ; Bodo Bookhagen, Auteur Année de publication : 2019 Article en page(s) : pp 200 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] capteur hyperspectral [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] impulsion laser [Termes descripteurs IGN] niveau de détail [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] semis de points Résumé : (Auteur) Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land-cover mapping and environmental applications. Numéro de notice : A2019-119 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.022 date de publication en ligne : 06/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92448 [article]

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An exploratory analysis of usability of Flickr tags for land use/land cover attribution / Yingwei Yan in Geo-spatial Information Science, vol 22 n° 1 (March 2019)  

Public [article]  inGeo-spatial Information Science > vol 22 n° 1 (March 2019) . - pp 12 - 22 Titre : An exploratory analysis of usability of Flickr tags for land use/land cover attribution Type de document : Article/Communication Auteurs : Yingwei Yan, Auteur ; Michael Schultz, Auteur ; Alexander Zipf, Auteur Année de publication : 2019 Article en page(s) : pp 12 - 22 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] San Diego Résumé : (Auteur) This study explored the land use/land cover (LULC) separability by the machine-generated and user-generated Flickr photo tags (i.e. the auto-tags and the user-tags, respectively), based on an authoritative LULC dataset for San Diego County in the United States. Ten types of LULCs were derived from the authoritative dataset. It was observed that certain types of the reclassified LULCs had abundant tags (e.g. the parks) or a high tag density (e.g. the commercial lands), compared with the less populated ones (e.g. the agricultural lands). Certain highly weighted terms of the tags derived based on a term frequency–inverse document frequency weighting scheme were helpful for identifying specific types of the LULCs, especially for the commercial recreation lands (e.g. the zoos). However, given the 10 sets of tags retrieved from the corresponding 10 types of LULCs, one set of tags (all the tags located at one specific type of the LULCs) could not fully delineate the corresponding LULC due to semantic overlaps, according to a latent semantic analysis. Numéro de notice : A2019-241 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2018.1560044 date de publication en ligne : 08/01/2019 En ligne : https://doi.org/10.1080/10095020.2018.1560044 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92934 [article]

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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 29 - 42 Titre : Integration of lidar data and GIS data for point cloud semantic enrichment at the point level Type de document : Article/Communication Auteurs : Harith Aljumaily, Auteur ; Debra F. Laefer, Auteur ; Dolores Cuadra, Auteur Année de publication : 2019 Article en page(s) : pp 29 - 42 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Dublin (Irlande ; ville) [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] flore urbaine [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] interpolation linéaire [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] système de gestion de base de données [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Commercial aerial laser scanning is generally delivered with point-by-point metadata for object identification, but current vendor-generated classification approaches (which rely exclusively on that data) generate high misclassification rates in urban areas. To overcome this problem and provide a fully scalable solution that harnesses distributed computing capabilities, this paper introduces a novel system, employing a MapReduce framework and existing GIS-based data, to provide more detailed and accurate classification. The approach goes beyond traditional gross-level classification (roads, buildings, trees, noise) by enriching the point cloud metadata with detailed semantic information about the object type. The approach was evaluated using two datasets of differing point density, separated by eight years for the same study area in Dublin, Ireland. As evaluated against manually classified data, classification quality ranged from 76% to 91% depending upon category and only 8% remained unclassified, as opposed to the commercial vendor's classification quality which ranged from 43% to 78% with 82% left unclassified. Numéro de notice : A2019-027 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.29 date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.29 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91964 [article]

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A cross-analysis framework for multi-source volunteered, crowdsourced, and authoritative geographic information : The case study of volunteered personal traces analysis against transport network data / Gloria Bordogna in Geo-spatial Information Science, vol 21 n° 3 (October 2018)  

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OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project / Amin Mobasheri in Geo-spatial Information Science, vol 21 n° 3 (October 2018)  

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