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Polyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 313 - 337 Titre : Polyline simplification based on the artificial neural network with constraints of generalization knowledge Type de document : Article/Communication Auteurs : Jiawei Du, Auteur ; Jichong Yin, Auteur ; Chengyi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 313 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] descripteur [Termes IGN] données maillées [Termes IGN] données vectorielles [Termes IGN] généralisation cartographique automatisée [Termes IGN] polyligne [Termes IGN] programmation par contraintes [Termes IGN] réseau neuronal artificiel [Termes IGN] simplification de contour [Vedettes matières IGN] Généralisation Résumé : (auteur) The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles. Numéro de notice : A2022-479 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1080/15230406.2021.2013944 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2013944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100885 [article]

VD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification / Jihao Li in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 19 - 33 Titre : VD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification Type de document : Article/Communication Auteurs : Jihao Li, Auteur ; Martin Weinmann, Auteur ; Xian Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 33 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agrégation de détails [Termes IGN] apprentissage profond [Termes IGN] précision de la classification [Termes IGN] qualité du modèle [Termes IGN] semis de points [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) Airborne Laser Scanning (ALS) point cloud classification is a valuable and practical task in the fields of photogrammetry and remote sensing. It takes an extremely important role in many applications of surveying, monitoring, planning, production and living. Recently, driven by the wave of deep learning, the classification of ALS point clouds has also been gradually shifting from traditional feature design to careful deep network architecture construction. Although significant progress has been achieved by leveraging deep learning technology, there are still some matters demanding prompt solution: (1) the coupling phenomenon of high-level semantic features from multiple field-of-views; (2) information propagation without aggregated local–global features in different levels of symmetrical structure; (3) quite serious class-imbalanced distribution problems in large-scale ALS point clouds. In this paper, to tackle these matters, we propose a novel View-Decoupled Network with Local–global Aggregation Bridge (VD-LAB) model. More concretely, a View-Decoupled (VD) grouping method is set at the deepest layer of the network. Then, we establish a Local–global Aggregation Bridge (LAB) between down-sampling path and up-sampling path of the same level. After that, a Self-Amelioration (SA) loss is taken as the optimization objective to train the whole model in an end-to-end manner. Extensive experiments on four challenging ALS point cloud datasets (LASDU, US3D, ISPRS 3D and GML) demonstrate that our VD-LAB is able to achieve state-of-the-art performance in terms of Overall Accuracy (OA) and mean -score (e.g., reaching 88.01% and 78.42% for LASDU dataset, respectively) with very few model parameters and it possesses a strong generalization capability. In addition, the visualization of achieved results also reveals more satisfactory classification for some categories, such as Water in the US3D dataset and Powerline in the ISPRS 3D dataset. Ultimately, the effect of each module in VD-LAB is assessed in detailed ablation analyses as well. Numéro de notice : A2022-067 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.012 Date de publication en ligne : 10/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99789 [article]

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BasqueRoads: a benchmark for road network selection / Guillaume Touya (2022)  

Public Titre : BasqueRoads: a benchmark for road network selection Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Azelle Courtial , Auteur Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 4 Projets : LostInZoom / Touya, Guillaume Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Note générale : bibliographie This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101003012). Langues : Anglais (eng) Descripteur : [Termes IGN] objet géographique [Termes IGN] réseau routier [Termes IGN] simplification de contour [Termes IGN] test de performance [Vedettes matières IGN] Généralisation Résumé : (auteur) [début] Road network selection is one of the major issues of map generalisation, as new papers are proposed every year since the first attempts of automation in the 1990’s (Thomson & Richardson, 1995). New methods are regularly proposed because selecting roads for maps at smaller scales is a complex problem. Roads are at the same time present in maps to enable car navigation tasks, and because they are structuring elements that reveal the nature of the landscape (urban, rural, mountainous…). So road selection is not only about retaining the most important roads of the network, but the preservation of topology and connectivity is essential, as well as the preservation, or the typification of road patterns (e.g. a ring road), and the preservation of local density differences (between urban and rural areas for instance). It is rare to see comparisons of road selection techniques in the literature, because of the lack of open source in map generalisation, but also because of the lack of a common dataset to benchmark these techniques; new propositions on road selection are most of the time tied to their own dataset and use case. This is why we think that this BasqueRoads dataset could be useful to advance on this topic of road network selection. Numéro de notice : C2021-066 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-4-5-2022 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.5194/ica-abs-4-5-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99536

A semantics-based trajectory segmentation simplification method / Minshi Liu in Journal of Geovisualization and Spatial Analysis, vol 5 n° 2 (December 2021)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 5 n° 2 (December 2021) . - n° 19 Titre : A semantics-based trajectory segmentation simplification method Type de document : Article/Communication Auteurs : Minshi Liu, Auteur ; Guifang He, Auteur ; Yi Long, Auteur Année de publication : 2021 Article en page(s) : n° 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] caractérisation [Termes IGN] contenu généré par les utilisateurs [Termes IGN] itinéraire [Termes IGN] précision sémantique [Termes IGN] simplification de contour Résumé : (auteur) With the development of mobile positioning technology, a large amount of mobile trajectory data has been generated. Therefore, to store, process and mine trajectory data in a better way, trajectory data simplification is imperative. Current trajectory data simplification methods are either based on spatiotemporal features or semantic features, such as road network structure, but they do not consider semantic features of a trajectory stop. To overcome this limitation, this study presents a trajectory segmentation simplification method based on stop features. The proposed method first extracts the stop feature of a trajectory, then divides the trajectory into move segments and stop segments based on the stop features, and finally simplifies the obtained segments. The proposed method is verified by experiments on personal trajectory data and taxi trajectory data. Compared with the classic spatiotemporal simplification method, the proposed method has higher spatiotemporal and semantic accuracy under different simplification scales. The proposed method is especially suitable for trajectory data with more stop features. Numéro de notice : A2021-970 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-021-00088-5 Date de publication en ligne : 27/09/2021 En ligne : http://dx.doi.org/10.1007/s41651-021-00088-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100367 [article]

Automatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 9 (September 2021) . - n° 606 Titre : Automatic building detection with polygonizing and attribute extraction from high-resolution images Type de document : Article/Communication Auteurs : Samitha Daranagama, Auteur ; Apichon Witayangkurn, Auteur Année de publication : 2021 Article en page(s) : n° 606 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] apprentissage profond [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] lissage de courbe [Termes IGN] orthophotoplan numérique [Termes IGN] polygonation [Termes IGN] segmentation sémantique Résumé : (auteur) Buildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remote sensing imagery has become a subject of intense study. Here, a modified version of the U-Net architecture with a combination of pre- and post-processing techniques was developed to extract building footprints from high-resolution aerial imagery and unmanned aerial vehicle (UAV) imagery. Data pre-processing with the logarithmic correction image enhancing algorithm showed the most significant improvement in the building detection accuracy for aerial images; meanwhile, the CLAHE algorithm improved the most concerning UAV images. This study developed a post-processing technique using polygonizing and polygon smoothing called the Douglas–Peucker algorithm, which made the building output directly ready to use for different applications. The attribute information, land use data, and population count data were applied using two open datasets. In addition, the building area and perimeter of each building were calculated as geometric attributes. Numéro de notice : A2021-684 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi10090606 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.3390/ijgi10090606 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98410 [article]

A typification method for linear building groups based on stroke simplification / Xiao Wang in Geocarto international, vol 36 n° 15 ([15/08/2021])  

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L’Alpe d’Huez: A benchmark for topographic map generalisation / Guillaume Touya (2021)  

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Image matching from handcrafted to deep features: A survey / Jiayi Ma in International journal of computer vision, vol 29 n° 1 (January 2021)  

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A multi-scale representation model of polyline based on head/tail breaks / Pengcheng Liu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)  

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Road network simplification for location-based services / Abdeltawab M. Hendawi in Geoinformatica [en ligne], vol 24 n° 4 (October 2020)  

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Regression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures / Timofey Samsonov in International journal of cartography, Vol 6 n° 1 (March 2020)  

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Variable DEM generalization using local entropy for terrain representation through scale / Paulo Raposo in International journal of cartography, Vol 6 n° 1 (March 2020)  

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Des images satellites aux cartes vectorielles / Onur Tasar (2020)  

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Champs et objets pour mieux représenter les phénomènes dans leur contexte géographique / Anne Ruas in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)  

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Building detection and regularisation using DSM and imagery information / Yousif A. Mousa in Photogrammetric record, vol 34 n° 165 (March 2019)  

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