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A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1309 - 1345 Titre : A scalable method to construct compact road networks from GPS trajectories Type de document : Article/Communication Auteurs : Yuejun Guo, Auteur ; Anton Bardera, Auteur ; Marta Fort, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1309 - 1345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] chevauchement [Termes IGN] compensation par faisceaux [Termes IGN] contour [Termes IGN] généralisation automatique de données [Termes IGN] méthode heuristique [Termes IGN] noeud [Termes IGN] réseau routier [Termes IGN] segmentation par décomposition-fusion [Termes IGN] squelettisation [Termes IGN] trajectographie par GPS [Termes IGN] trajectoire Résumé : (auteur) The automatic generation of road networks from GPS tracks is a challenging problem that has been receiving considerable attention in the last years. Although dozens of methods have been proposed, current techniques suffer from two main shortcomings: the quality of the produced road networks is still far from those produced manually, and the methods are slow, making them not scalable to large inputs. In this paper, we present a fast four-step density-based approach to construct a road network from a set of trajectories. A key aspect of our method is the use of an improved version of the Slide method to adjust trajectories to build a more compact density surface. The network has comparable or better quality than that of state-of-the-art methods and is simpler (includes fewer nodes and edges). Furthermore, we also propose a split-and-merge strategy that allows splitting the data domain into smaller regions that can be processed independently, making the method scalable to large inputs. The performance of our method is evaluated with extensive experiments on urban and hiking data. Numéro de notice : A2021-447 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1832229 date de publication en ligne : 16/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1832229 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97859 [article]

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An incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 445 - 455 Titre : An incremental isomap method for hyperspectral dimensionality reduction and classification Type de document : Article/Communication Auteurs : Yi Ma, Auteur ; Zezhong Zheng, Auteur ; Yutang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 445 - 455 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification barycentrique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] échantillonnage de données [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] squelettisation [Termes IGN] utilisation du sol Résumé : (Auteur) Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We present in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature variation algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine. Numéro de notice : A2021-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.445 date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.7.445 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97829 [article]

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Dynamic human body reconstruction and motion tracking with low-cost depth cameras / Kangkan Wang in The Visual Computer, vol 37 n° 3 (March 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 3 (March 2021) . - pp 603 - 618 Titre : Dynamic human body reconstruction and motion tracking with low-cost depth cameras Type de document : Article/Communication Auteurs : Kangkan Wang, Auteur ; Guofeng Zhang, Auteur ; Jian Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 603 - 618 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement de formes [Termes IGN] déformation de projection [Termes IGN] données d'apprentissage [Termes IGN] filtrage spatiotemporel [Termes IGN] maillage [Termes IGN] modèle dynamique [Termes IGN] modélisation 3D [Termes IGN] mouvement [Termes IGN] objet mobile [Termes IGN] reconstruction d'objet [Termes IGN] squelettisation Résumé : (auteur) We present a novel approach for dynamic human body reconstruction and motion tracking using low-cost depth cameras. Our reconstruction system is able to produce a sequence of dynamic 3D human body models from the noisy input depth data. To accurately align the template model with noisy input data, we combine skeleton-driven deformation and mesh deformation techniques to enhance the registration robustness to depth missing, occlusions, and severe noise. In addition, a novel data-driven 3D human body model is introduced to efficiently reconstruct human body models with wide shape and pose variations only using a limited number of training databases with standard standing pose. We perform quantitative and qualitative experiments to evaluate our method and compare it with other methods for body reconstruction on both synthetic and real datasets. Experimental results demonstrate the effectiveness of the proposed approach. Numéro de notice : A2021-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01826-4 date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01826-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97579 [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

Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 56 - 73 Titre : Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution Type de document : Article/Communication Auteurs : Vitor Martins, Auteur ; Amy L. Kaleita, Auteur ; Brian K. Gelder, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 56 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données multiéchelles [Termes IGN] étiquetage sémantique [Termes IGN] hétérogénéité environnementale [Termes IGN] image à haute résolution [Termes IGN] occupation du sol [Termes IGN] reconnaissance d'objets [Termes IGN] segmentation d'image [Termes IGN] squelettisation Résumé : (auteur) Convolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km2. Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (“medial axis”) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented better classification accuracy (overall accuracy ~87.2%) compared to traditional patch-based CNN (81.6%) and fixed-input OCNN (82%). In addition, the results showed that this framework is 8.1 and 111.5 times faster than traditional pixel-wise CNN16 or CNN256, respectively. Multiple CNNs and object analysis have proved to be essential for accurate and fast classification. While multi-OCNN produced a high-level of spatial details in the land cover product, misclassification was observed for some classes, such as road versus buildings or shadow versus lake. Despite these minor drawbacks, our results also demonstrated the benefits of IowaNet training dataset in the model performance; overfitting process reduces as the number of samples increases. The limitations of multi-OCNN are partially explained by segmentation quality and limited number of spectral bands in the aerial data. With the advance of deep learning methods, this study supports the claim of multi-OCNN benefits for operational large-scale land cover product at 1-m resolution. Numéro de notice : A2020-634 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.004 date de publication en ligne : 13/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96057 [article]

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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images / Zhihua Xua in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)  

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A local structure and direction-aware optimization approach for three-dimensional tree modeling / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

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Crowd-sourced reconstruction of building interiors / Michael Peter (2016)  

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Pattern-mining approach for conflating crowdsourcing road networks with POIs / Bisheng Yang in International journal of geographical information science IJGIS, vol 29 n° 5 (May 2015)  

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Weighted straight skeletons in the plane / Therese Biedl in Computational Geometry : theory and applications, vol 48 n° 2 (February 2015)  

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3D tree reconstruction from simulated small footprint waveform lidar / Jiaying Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)  

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Derivation of tree skeletons and error assessment using LiDAR point cloud data of varying quality / Magnus Bremer in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)

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2D arrangement-based hierarchical spatial partitioning: an application to pedestrian network generation / Murat Yirci (2013)  

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