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Historical Vltava River valley–various historical sources within web mapping environment / Jiří Krejčí in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 35 Titre : Historical Vltava River valley–various historical sources within web mapping environment Type de document : Article/Communication Auteurs : Jiří Krejčí, Auteur ; Jiří Cajthaml, Auteur Année de publication : 2022 Article en page(s) : n° 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] ArcGIS [Termes IGN] carte ancienne [Termes IGN] changement d'utilisation du sol [Termes IGN] données anciennes [Termes IGN] géoréférencement [Termes IGN] modélisation 3D [Termes IGN] point d'appui [Termes IGN] République Tchèque [Termes IGN] rivière [Termes IGN] système d'information historique [Termes IGN] vectorisation [Termes IGN] web mapping Résumé : (auteur) The article deals with a comprehensive information system of the historic Vltava River valley. This system contains a number of resources, which are described. For old maps, which are the basis of the whole system, their georeferencing and potential problems in creating seamless mosaics are described. Other sources of data include old photographs, which are localized and stored in the system, along with the definition point of the place from which they were probably taken. The vectorization of data is described, not only for area features used for the analysis of land-use changes, but also for the vectorization of contours. These were vectorized from old maps and are substantial for the creation of historic DEM. Vectorized footprints of buildings and vectors of other functional areas subsequently serve as a basis for the procedural modeling of the virtual 3D landscape. The creation of such a complex and broad information system cannot be described in one article. The aim of this text is to draw attention to a possible approach to the presentation and visualization of the historic landscape, along with links to important documents. Numéro de notice : A2022-038 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010035 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.3390/ijgi11010035 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99380 [article]

Comparative analysis for methods of building digital elevation models from topographic maps using geoinformation technologies / Vadim Belenok in Geodesy and cartography, vol 47 n° 4 (December 2021)  

Public [article]  inGeodesy and cartography > vol 47 n° 4 (December 2021) . - pp 191 - 199 Titre : Comparative analysis for methods of building digital elevation models from topographic maps using geoinformation technologies Type de document : Article/Communication Auteurs : Vadim Belenok, Auteur ; Yuriy Velikodsky, Auteur ; Oleksandr Nikolaienko, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 191 - 199 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse comparative [Termes IGN] ArcGIS [Termes IGN] carte topographique [Termes IGN] contour [Termes IGN] données altimétriques [Termes IGN] image SRTM [Termes IGN] interpolation linéaire [Termes IGN] interpolation polynomiale [Termes IGN] modèle numérique de surface [Termes IGN] Python (langage de programmation) [Termes IGN] régression [Termes IGN] Russie [Termes IGN] vectorisation Résumé : (auteur) The article considers the question of estimating the accuracy of interpolation methods for building digital elevation models using Soviet topographic maps. The territory of the Kursk region of the Russian Federation was used as the study area, because it is located on the Central Russian Upland and characterized by the complex structure of the vertical and horizontal dissection of the relief. Contour lines automatically obtained using a Python algorithm were used as the initial elevation data to build a digital elevation model. Digital elevation models obtained by thirteen different interpolation methods in ArcGIS and Surfer software were built and analyzed. Special attention is paid to the ANUDEM method, which allows to obtain hydrologically correct digital elevation models. Recommendations for the use of one or another method of interpolation are given. The results can be useful for professionals who use topographic maps in their work and deals with the design using digital elevation models. Numéro de notice : A2021-925 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3846/gac.2021.13208 Date de publication en ligne : 13/12/2021 En ligne : https://doi.org/10.3846/gac.2021.13208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99287 [article]

Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 57 - 74 Titre : Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization Type de document : Article/Communication Auteurs : Jiali Han, Auteur ; Mengqi Rong, Auteur ; Hanqing Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 57 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire de Markov [Termes IGN] données lidar [Termes IGN] espace intérieur [Termes IGN] maillage [Termes IGN] programmation linéaire [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] vectorisation Résumé : (Auteur) Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Field (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches. Numéro de notice : A2021-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.019 Date de publication en ligne : 15/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97779 [article]

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Indoor mapping and modeling by parsing floor plan images / Yijie Wu in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 6 (June 2021) . - pp 1205 - 1231 Titre : Indoor mapping and modeling by parsing floor plan images Type de document : Article/Communication Auteurs : Yijie Wu, Auteur ; Jianga Shang, Auteur ; Pan Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1205 - 1231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte d'intérieur [Termes IGN] chevauchement [Termes IGN] CityGML [Termes IGN] construction [Termes IGN] format Industry foudation classes IFC [Termes IGN] intégrité topologique [Termes IGN] mur [Termes IGN] optimisation spatiale [Termes IGN] positionnement en intérieur [Termes IGN] vectorisation Résumé : (auteur) A large proportion of indoor spatial data is generated by parsing floor plans. However, a mature and automatic solution for generating high-quality building elements (e.g., walls and doors) and space partitions (e.g., rooms) is still lacking. In this study, we present a two-stage approach to indoor mapping and modeling (IMM) from floor plan images. The first stage vectorizes the building elements on the floor plan images and the second stage repairs the topological inconsistencies between the building elements, separates indoor spaces, and generates indoor maps and models. To reduce the shape complexity of indoor boundary elements, i.e., walls and openings, we harness the regularity of the boundary elements and extract them as rectangles in the first stage. Furthermore, to resolve the overlaps and gaps of the vectorized results, we propose an optimization model that adjusts the rectangle vertex coordinates to conform to the topological constraints. Experiments demonstrate that our approach achieves a considerable improvement in room detection without conforming to Manhattan World Assumption. Our approach also outputs instance-separate walls with consistent topology, which enables direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML). Numéro de notice : A2021-385 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1781130 Date de publication en ligne : 08/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1781130 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97642 [article]

Automatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)  

Public Titre : Automatic object extraction from airborne laser scanning point clouds for digital base map production Type de document : Thèse/HDR Auteurs : Elyta Widyaningrum, Auteur Editeur : Delft [Pays-Bas] : Delft University of Technology Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] axe médian [Termes IGN] chaîne de traitement [Termes IGN] détection d'objet [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction du réseau routier [Termes IGN] image aérienne [Termes IGN] orthoimage [Termes IGN] squelettisation [Termes IGN] transformation de Hough [Termes IGN] vectorisation Résumé : (auteur) A base map provides essential geospatial information for applications such as urban planning, intelligent transportation systems, and disaster management. Buildings and roads are the main ingredients of a base map and are represented by polygons. Unfortunately, manually delineating their boundaries from remote sensing data is time consuming and labour intensive. Airborne laser scanning (ALS) point clouds provide dense and accurate 3D positional information. Automatic extraction of buildings and roads from 3D point clouds is challenging because of their irregular shapes, occlusions in the data, and irregularity of ALS point clouds. This study focuses on two particular objectives: (i) accurate classification of a large volume of ALS 3D point clouds; and (ii) smooth and accurate building and road outline extraction. To achieve the classification objective, we perform point-wise deep learning to classify an ALS point cloud of a complex urban scene in Surabaya, Indonesia. The point cloud is colored by airborne orthophotos. Training data is obtained from an existing 2D topographic base map by a semi-automatic method proposed in this research. A dynamic-graph convolutional neural network is used to classify the point cloud into four classes: bare land, trees, buildings, and roads. We investigate effective input feature combinations for outdoor point cloud classification. A highly acceptable classification result of 91.8% overall accuracy is achieved when using the full combination of RGB color and LiDAR features. To address the objective of outline extraction, we propose building and road outline extraction methods that run directly on ALS point cloud data. For accurate and smooth building outline extraction, we propose two different methods. First, we develop the ordered Hough transform (OHT), which is an extension of the traditional Hough transform, by explicitly incorporating the sequence of points to form the outline. Second, we propose a new method based on Medial Axis Transform (MAT) skeletons which takes advantage of the skeleton points to detect building corners. The OHT method is resistant to noise but it requires prior knowledge on a building’s main directions. On the contrary, the MAT-based method does not require such orientation initialization but is more sensitive to noise on building edges. We compare the results of our building outline extraction methods to an existing RANSAC-based method, in terms of geometric accuracy, completeness of building corners, and computation time, and demonstrate that the MAT-based approach has the highest geometric accuracy, results in more complete building corners, and is slightly faster than other methods. For road network extraction, we develop a method based on skeletonization, which results in complete and continuous road centerlines and boundaries. In our study area, several roads are disrupted and disconnected due to trees. We design a tree-constrained approach to fill road gaps and integrate road width estimated from a medial axis algorithm. Comparison to reference data shows that the proposed method is able to extract almost all existing roads in the study area, and even detects roads that were not present in the reference due to human errors. We conclude that our object extraction methods enable a complete automatic procedure, extracting more accurate building and road outlines from ALS point cloud data. This contributes to a higher automation readiness level for a faster and cheaper base map production. Numéro de notice : 17664 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Sciences : TU Delft: 2021 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.4233/uuid:8900fac8-a76c-482a-b280-e1758783b5b3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97984

Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)  

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Geometric approximation of structured scenes from images / Muxingzi Li (2021)  

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Vectorization of historical maps using deep edge filtering and closed shape extraction / Yizi Chen (2021)  

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Assessing historical maps for characterizing fluvial corridor changes at a regional network scale / Samuel Dunesme in Cartographica, vol 55 n° 4 (Winter 2020)  

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Can we characterize river corridor evolution at a continental scale from historical topographic maps? A first assessment from the comparison of four countries / J. Horacio Garcia in River Research and Applications, vol 36 n° 6 (July 2020)  

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A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Détection et vectorisation automatiqued’objets linéaires dans des nuages de points de voirie / Etienne Barçon (2020)  

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

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Learning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)  

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Very high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)  

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