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Delineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

Public [article]  inGeodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 344 - 378 Titre : Delineation of vacant building land using orthophoto and lidar data object classification Type de document : Article/Communication Auteurs : Dejan Jenko, Auteur ; Mojca Foški, Auteur ; Krištof Oštir, Auteur ; Žiga Kokalj, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 378 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] couche thématique [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] logement [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] Slovénie Résumé : (Auteur) Exact data about the location and area of vacant building land have been a major issue in several Slovene municipalities. This article deals with automatic vacant building land delineation. The presented methodology is based on the object-based classification that derives the land cover layer from orthophoto and laser scanning data. With post-processing and data cleaning in GIS, we create the vacant building land layer. The methodology was tested in study areas in the Municipality of Trebnje. The results were compared to the vacant building land layer generated by visual interpretation (manual vectorisation). We found that the presented methodology of automatic delineation of vacant buildings can speed up the processing and lower the cost of manual vectorisation and, in particular, data updating but we cannot completely replace manual work. Numéro de notice : A2019-500 Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.344-378 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.344-378 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93782 [article]

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Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 70 - 83 Titre : Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours Type de document : Article/Communication Auteurs : David Griffiths, Auteur ; Jan Boehm, Auteur Année de publication : 2019 Article en page(s) : pp 70 - 83 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] bati [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données publiques [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] Royaume-Uni [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] zone rurale Résumé : (Auteur) Robust and reliable automatic building detection and segmentation from aerial images/point clouds has been a prominent field of research in remote sensing, computer vision and point cloud processing for a number of decades. One of the largest issues associated with deep learning methods is the high quantity of data required for training. To help address this we present a method to improve public GIS building footprint labels by using Morphological Geodesic Active Contours (MorphGACs). We demonstrate by improving the quality of building footprint labels for detection and semantic segmentation, more robust and reliable models can be obtained. We evaluate these methods over a large UK-based dataset of 24556 images containing 169835 building instances. This is achieved by training several Mask/Faster R-CNN and RetinaNet deep convolutional neural networks. Networks are supplied with both RGB and fused RGB-lidar data. We offer quantitative analysis on the benefits of the inclusion of depth data for building segmentation. By employing both methods we achieve a detection accuracy of 0.92 (mAP@0.5) and segmentation f1 scores of 0.94 over a 4911 test images ranging from urban to rural scenes. Numéro de notice : A2019-265 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.013 date de publication en ligne : 06/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.013 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93079 [article]

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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 91 - 105 Titre : Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network Type de document : Article/Communication Auteurs : Jianfeng Huang, Auteur ; Xinchang Zhang, Auteur ; Qinchuan Xin, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 91 - 105 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] résidu [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Automated extraction of buildings from remotely sensed data is important for a wide range of applications but challenging due to difficulties in extracting semantic features from complex scenes like urban areas. The recently developed fully convolutional neural networks (FCNs) have shown to perform well on urban object extraction because of the outstanding feature learning and end-to-end pixel labeling abilities. The commonly used feature fusion or skip-connection refine modules of FCNs often overlook the problem of feature selection and could reduce the learning efficiency of the networks. In this paper, we develop an end-to-end trainable gated residual refinement network (GRRNet) that fuses high-resolution aerial images and LiDAR point clouds for building extraction. The modified residual learning network is applied as the encoder part of GRRNet to learn multi-level features from the fusion data and a gated feature labeling (GFL) unit is introduced to reduce unnecessary feature transmission and refine classification results. The proposed model - GRRNet is tested in a publicly available dataset with urban and suburban scenes. Comparison results illustrated that GRRNet has competitive building extraction performance in comparison with other approaches. The source code of the developed GRRNet is made publicly available for studies. Numéro de notice : A2019-206 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.019 date de publication en ligne : 20/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92669 [article]

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Coastline extraction from SAR images using robust ridge tracing / Dailiang Wang in Marine geodesy, vol 42 n° 3 (May 2019)  

Public [article]  inMarine geodesy > vol 42 n° 3 (May 2019) . - pp 286 - 315 Titre : Coastline extraction from SAR images using robust ridge tracing Type de document : Article/Communication Auteurs : Dailiang Wang, Auteur ; Xiaoyan Liu, Auteur Année de publication : 2019 Article en page(s) : pp 286 - 315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] érosion côtière [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] littoral [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] trait de côte [Termes descripteurs IGN] variance Résumé : (auteur) Although ridge tracing has the advantages of continuity and high positioning accuracy compared with other edge-based methods, it is difficult to use ridge tracing to extract coastlines from Synthetic Aperture Radar (SAR) images because of the speckle noise that occurs in SAR images. This paper presents a new coastline extraction method for SAR images based on a more robust ridge tracing method. First, according to the statistical properties of the pixel intensities in the land and sea regions in a SAR image, an edge magnitude map that characterizes the boundary between them is produced by the ratio of the variance to the mean such that the magnitude at the land-sea boundary is much higher than that at other locations. Second, the pixel with the maximum magnitude in the map is adopted as the starting point for tracing, and strip windows, which reduce tracing failures, are adopted to obtain different average magnitudes corresponding to the eight neighborhood pixels around the starting point. Then, the neighborhood pixel with the maximum magnitude is adopted as the next tracing point. The above procedure is repeated to determine the direction of the next point. This process achieves part of the tracing operation. The complete coastline is then extracted by performing the other part of the tracing operation. The experimental results show that the proposed method is more robust than traditional methods, and we demonstrate its effectiveness with RADARSAT-2 and Sentinel-1A data. Numéro de notice : A2019-280 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2019.1583147 date de publication en ligne : 29/03/2019 En ligne : https://doi.org/10.1080/01490419.2019.1583147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93114 [article]

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

Public [article]  inPhotogrammetric record > vol 34 n° 165 (March 2019) . - pp 85 - 107 Titre : Building detection and regularisation using DSM and imagery information Type de document : Article/Communication Auteurs : Yousif A. Mousa, Auteur ; Petra Helmholz, Auteur ; David Belton, Auteur ; Dimitri Bulatov, Auteur Année de publication : 2019 Article en page(s) : pp 85 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] masque [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] polygone [Termes descripteurs IGN] régularisation [Termes descripteurs IGN] simplification de contour Résumé : (Auteur) An automatic method for the regularisation of building outlines is presented, utilising a combination of data‐ and model‐driven approaches to provide a robust solution. The core part of the method includes a novel data‐driven approach to generate approximate building polygons from a list of given boundary points. The algorithm iteratively calculates and stores likelihood values between an arbitrary starting boundary point and each of the following boundary points using a function derived from the geometrical properties of a building. As a preprocessing step, building segments have to be identified using a robust algorithm for the extraction of a digital elevation model. Evaluation results on a challenging dataset achieved an average correctness of 96·3% and 95·7% for building detection and regularisation, respectively. Numéro de notice : A2019-454 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12275 date de publication en ligne : 26/03/2019 En ligne : https://doi.org/10.1111/phor.12275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92867 [article]

Repeated structure detection for 3D reconstruction of building façade from mobile lidar data / Yanming Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)  

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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)  

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Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

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A greyscale voxel model for airborne lidar data applied to building detection / Liying Wang in Photogrammetric record, vol 33 n° 164 (December 2018)  

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Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images / Fabien Hubert Wagner in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part B (November 2018)  

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Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut G. Sefercik in Geocarto international, vol 33 n° 10 (October 2018)  

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Extraction of building roof planes with stratified random sample consensus / André C. Carrilho in Photogrammetric record, vol 33 n° 163 (September 2018)  

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Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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Fusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)

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An (almost) automated process to track the Martians dunes : ac.GetPreciseShifts / Arthur Coqué (2018) 

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