Centre dedocumentation
scientifique

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]

Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (December 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 4 (December 2020) . - pp 329 - 342 Titre : Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Cuizhen Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 329 - 342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] extraction de traits caractéristiques [Termes IGN] jeu de données [Termes IGN] Kiangsi (Chine) [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique [Termes IGN] taille du jeu de données Résumé : (auteur) Land cover data is an inventory of objects on the Earth’s surface, which is often derived from remotely sensed imagery. Deep Convolutional Neural Network (DCNN) is a competitive method in image semantic segmentation. Some scholars argue that the inadequacy of training set is an obstacle when applying DCNNs in remote sensing image segmentation. While existing land cover data can be converted to large training sets, the size of training data set needs to be carefully considered. In this paper, we used different portions of a high-resolution land cover map to produce different sizes of training sets to train DCNNs (SegNet and U-Net) and then quantitatively evaluated the impact of training set size on the performance of the trained DCNN. We also introduced a new metric, Edge-ratio, to assess the performance of DCNN in maintaining the boundary of land cover objects. Based on the experiments, we document the relationship between the segmentation accuracy and the size of the training set, as well as the nonstationary accuracies among different land cover types. The findings of this paper can be used to effectively tailor the existing land cover data to training sets, and thus accelerate the assessment and employment of deep learning techniques for high-resolution land cover map extraction. Numéro de notice : A2020-800 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1803402 date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1803402 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96723 [article]

Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 15-28 Titre : Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss Type de document : Article/Communication Auteurs : Xianwei Zheng, Auteur ; Linxi Huan, Auteur ; Gui-Song Xia, Auteur ; Jianya Gong, Auteur Année de publication : 2020 Article en page(s) : pp 15-28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification basée sur les régions [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] image à très haute résolution [Termes IGN] méthode fondée sur le noyau [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique Résumé : (Auteur) Parsing very high resolution (VHR) urban scene images into regions with semantic meaning, e.g. buildings and cars, is a fundamental task in urban scene understanding. However, due to the huge quantity of details contained in an image and the large variations of objects in scale and appearance, the existing semantic segmentation methods often break one object into pieces, or confuse adjacent objects and thus fail to depict these objects consistently. To address these issues uniformly, we propose a standalone end-to-end edge-aware neural network (EaNet) for urban scene semantic segmentation. For semantic consistency preservation inside objects, the EaNet model incorporates a large kernel pyramid pooling (LKPP) module to capture rich multi-scale context with strong continuous feature relations. To effectively separate confusing objects with sharp contours, a Dice-based edge-aware loss function (EA loss) is devised to guide the EaNet to refine both the pixel- and image-level edge information directly from semantic segmentation prediction. In the proposed EaNet model, the LKPP and the EA loss couple to enable comprehensive feature learning across an entire semantic object. Extensive experiments on three challenging datasets demonstrate that our method can be readily generalized to multi-scale ground/aerial urban scene images, achieving 81.7% in mIoU on Cityscapes Test set and 90.8% in the mean F1-score on the ISPRS Vaihingen 2D Test set. Code is available at: https://github.com/geovsion/EaNet. Numéro de notice : A2020-703 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.019 date de publication en ligne : 14/10/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96228 [article]

Network-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1903 - 1929 Titre : Network-constrained bivariate clustering method for detecting urban black holes and volcanoes Type de document : Article/Communication Auteurs : Qiliang Liu, Auteur ; Zhihui Wu, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1903 - 1929 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse bivariée [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] circulation urbaine [Termes IGN] contour [Termes IGN] détection d'anomalie [Termes IGN] méthode de Monte-Carlo [Termes IGN] Pékin (Chine) [Termes IGN] planification urbaine [Termes IGN] réseau de contraintes [Termes IGN] réseau routier [Termes IGN] sécurité publique [Termes IGN] trafic routier [Termes IGN] trajectoire [Termes IGN] trou noir [Termes IGN] voisinage (topologie) [Termes IGN] volcan [Termes IGN] zone urbaine Résumé : (auteur) Urban black holes and volcanoes are typical traffic anomalies that are useful for optimizing urban planning and maintaining public safety. It is still challenging to detect arbitrarily shaped urban black holes and volcanoes considering the network constraints with less prior knowledge. This study models urban black holes and volcanoes as bivariate spatial clusters and develops a network-constrained bivariate clustering method for detecting statistically significant urban black holes and volcanoes with irregular shapes. First, an edge-expansion strategy is proposed to construct the network-constrained neighborhoods without the time-consuming calculation of the network distance between each pair of objects. Then, a network-constrained spatial scan statistic is constructed to detect urban black holes and volcanoes, and a multidirectional optimization method is developed to identify arbitrarily shaped urban black holes and volcanoes. Finally, the statistical significance of multiscale urban black holes and volcanoes is evaluated using Monte Carlo simulation. The proposed method is compared with three state-of-the-art methods using both simulated data and Beijing taxicab spatial trajectory data. The comparison shows that the proposed method can detect urban black holes and volcanoes more accurately and completely and is useful for detecting spatiotemporal variations of traffic anomalies. Numéro de notice : A2020-511 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1720027 date de publication en ligne : 27/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1720027 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95665 [article]

Adaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2412 - 2429 Titre : Adaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation Type de document : Article/Communication Auteurs : Deliang Xiang, Auteur ; Wei Wang, Auteur ; Tao Tang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2412 - 2429 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] chatoiement [Termes IGN] contour [Termes IGN] fusion de données [Termes IGN] image radar [Termes IGN] polarimétrie radar [Termes IGN] radar à antenne synthétique [Termes IGN] segmentation d'image [Termes IGN] superpixel [Termes IGN] vision par ordinateur Résumé : (auteur) This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation by merging superpixels using the statistical region merging (SRM) framework. This article proposes to define a new dissimilarity measure between superpixels, which takes the edge penalty into consideration, leading to a reasonable and accurate merging order for superpixel pairs. With regard to the merging predicate of superpixels, a polarimetric homogeneity measurement (HoM) is used to define the merging threshold, making the merging predicate and merging threshold adaptive to the PolSAR image content. Experimental results on three airborne and one spaceborne PolSAR data sets demonstrate that the proposed approach can effectively improve the computation efficiency and segmentation accuracy in comparison with state-of-the-art merging-based methods for PolSAR data. More importantly, the proposed approach is free of parameters and easy to use. Numéro de notice : A2020-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2949066 date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2949066 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94864 [article]

Contour lines generation in karstic plateaus for topographic maps / Guillaume Touya (2019)    

Permalink

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)  

Permalink

Historic reconstruction of reservoir topography using contour line interpolation and structure from motion photogrammetry / Ana Casado in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

Permalink

Saillance visuelle en imagerie urbaine / Kamel Guissous (2018) 

Permalink

Registration of images to Lidar and GIS data without establishing explicit correspondences / Gabor Barsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)  

Permalink

Absolute orientation based on line coordinates / Qing H. Sheng in Photogrammetric record, vol 32 n° 157 (March - May 2017)  

Permalink

Recherche d’images basée sur la saillance visuelle pour l’imagerie urbaine / Kamel Guissous (2017)    

Permalink

mR-V: Line Simplification through Mnemonic Rasterization / Emmanuel Stefanakis in Geomatica [en ligne], vol 70 n° 4 (December 2016)  

Permalink

Fourier-based multi-scale representation and progressive transmission of cartographic curves on the internet / Pengcheng Liu in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

Permalink

Satellite images analysis for shadow detection and building height estimation / Gregoris Liasis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

Permalink