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Urban land-use analysis using proximate sensing imagery: a survey / Zhinan Qiao in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : Urban land-use analysis using proximate sensing imagery: a survey Type de document : Article/Communication Auteurs : Zhinan Qiao, Auteur ; Xiaohui Yuan, Auteur Année de publication : 2021 Article en page(s) : pp 2129 - 2148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] image Streetview
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with the Global Positioning System (GPS) greatly proliferates proximate sensing images taken near or on the ground at a close distance to urban targets. Studies leveraging proximate sensing images have demonstrated great potential to address the need for local data in the urban land-use analysis. This paper reviews and summarizes the state-of-the-art methods and publicly available data sets from proximate sensing to support land-use analysis. We identify several research problems in the perspective of examples to support the training of models and means of integrating diverse data sets. Our discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing images in urban land-use studies. Numéro de notice : A2021-759 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1919682 Date de publication en ligne : 03/05/2021 En ligne : https://doi.org/10.1080/13658816.2021.1919682 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98788
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2129 - 2148[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation / Hamza Ben Addou in Géomatique expert, n° 136 (novembre - décembre 2021)
[article]
Titre : Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 42 -47 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] apprentissage profond
[Termes IGN] CityGML
[Termes IGN] classification automatique d'objets
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] emprise au sol
[Termes IGN] maquette numérique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique du bâti
[Termes IGN] modélisation 3D
[Termes IGN] niveau de détail
[Termes IGN] orthoimage
[Termes IGN] primitive géométrique
[Termes IGN] toitRésumé : (Auteur) Post-traitement des données issues de l’algorithme d’apprentissage profond et modélisation 3D urbaine automatique Numéro de notice : A2021-919 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/11/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99335
in Géomatique expert > n° 136 (novembre - décembre 2021) . - pp 42 -47[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002286 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt A vector-based method for drainage network analysis based on LiDAR data / Fangzheng Lyu in Computers & geosciences, vol 156 (November 2021)
[article]
Titre : A vector-based method for drainage network analysis based on LiDAR data Type de document : Article/Communication Auteurs : Fangzheng Lyu, Auteur ; Xinlin Ma, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse vectorielle
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] interpolation spatiale
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau hydrographique
[Termes IGN] semis de pointsRésumé : (auteur) Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. Numéro de notice : A2021-755 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104892 Date de publication en ligne : 24/07/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98733
in Computers & geosciences > vol 156 (November 2021)[article]STC-Det: A slender target detector combining shadow and target information in optical satellite images / Zhaoyang Huang in Remote sensing, vol 13 n° 20 (October-2 2021)
[article]
Titre : STC-Det: A slender target detector combining shadow and target information in optical satellite images Type de document : Article/Communication Auteurs : Zhaoyang Huang, Auteur ; Feng Wang, Auteur ; Hongjian You, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4183 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] fusion de données
[Termes IGN] image satellite
[Termes IGN] ombreRésumé : (auteur) Object detection has made great progress. However, due to the unique imaging method of optical satellite remote sensing, the detection of slender targets is still insufficient. Specifically, the perspective of optical satellites is small, and the characteristics of slender targets are severely lost during imaging, resulting in insufficient detection task information; at the same time, the appearance of slender targets in the image is greatly affected by the satellite perspective, which is likely to cause insufficient generalization capabilities of conventional detection models. In response to these two points, we have made some improvements. First, in this paper, we introduce the shadow as auxiliary information to complement the trunk features of the target lost in imaging. Second, to reduce the impact of satellite perspective on imaging, in this paper, we use the characteristic that shadow information is not affected by satellite perspective to design STC-Det. STC-Det treats the shadow and the target as two different types of targets and uses the shadow information to assist the detection, reducing the impact of the satellite perspective on detection. Among them, in order to improve the performance of STC-Det, we propose an automatic matching method (AMM) of shadow and target and a feature fusion method (FFM). Finally, this paper proposes a new method to calculate the heatmaps of detectors, which verifies the effectiveness of the proposed network in a visual way. Experiments show that when the satellite perspective is variable, the precision of STC-Det is increased by 1.7%, and when the satellite perspective is small, the precision of STC-Det is increased by 5.2%. Numéro de notice : A2021-804 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204183 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.3390/rs13204183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98860
in Remote sensing > vol 13 n° 20 (October-2 2021) . - n° 4183[article]Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)
[article]
Titre : Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images Type de document : Article/Communication Auteurs : Zhenjiang Wu, Auteur ; Jiahua Zhang, Auteur ; Fan Deng, Auteur Année de publication : 2021 Article en page(s) : n° 4067 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Chine
[Termes IGN] classification par algorithme génétique
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] prairie
[Termes IGN] précision de la classification
[Termes IGN] superpixel
[Termes IGN] texture d'imageRésumé : (auteur) Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale. Numéro de notice : A2021-805 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204067 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.3390/rs13204067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98862
in Remote sensing > vol 13 n° 20 (October-2 2021) . - n° 4067[article]Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkAn internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images / Sihang Zhang in Geo-spatial Information Science, vol 24 n° 4 (October 2021)PermalinkAnthropogenic degradation of dunes within a city: a disappearing feature of the cultural landscape of Toruń (Poland) / Pawel Molewski in Journal of maps, vol 17 n° 4 (October 2021)PermalinkBi- and three-dimensional urban change detection using sentinel-1 SAR temporal series / Meiqin Che in Geoinformatica, vol 25 n° 4 (October 2021)PermalinkComparison of digital elevation models through the analysis of geomorphic surface remnants in the Desatoya Mountains, Nevada / Bernadett Dobre in Transactions in GIS, vol 25 n° 5 (October 2021)PermalinkA deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkDisaster Image Classification by Fusing Multimodal Social Media Data / Zhiqiang Zou in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkDisaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkEndmember bundle extraction based on multiobjective optimization / Rong Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkEvaluation of methods for connecting InSAR to a terrestrial reference frame in the Latrobe Valley, Australia / P.J. Johnston in Journal of geodesy, vol 95 n° 10 (October 2021)PermalinkExtracting 3D indoor maps with any shape accurately using building information modeling data / Qi Qiu in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkA feature based change detection approach using multi-scale orientation for multi-temporal SAR images / R. Vijaya Geetha in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkFlood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model / Gaurav Talukdar in Natural Hazards, vol 109 n° 1 (October 2021)PermalinkGeomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques / Peter G. Chirico in Journal of maps, vol 17 n° 4 (October 2021)PermalinkGPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings / Bin Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkIntegrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images / Yijie Tang in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkIntegration of heterogeneous terrain data into Discrete Global Grid Systems / Mingke Li in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)PermalinkInvestigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)PermalinkA methodology for producing realistic hill-shading map based on shaded relief map, digital orthophotographic map fusion and IHS transformation / Hongyun Zeng in Annals of GIS, vol 27 n° 4 (October 2021)PermalinkA novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkOn the suitability of a unified GIS-BIM-HBIM framework for cataloguing and assessing vulnerability in Historic Urban Landscapes: a critical review / Rafael Ramirez Eudave in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)PermalinkOrbit error removal in InSAR/MTInSAR with a patch-based polynomial model / Yanan Du in International journal of applied Earth observation and geoinformation, vol 102 (October 2021)PermalinkPhase unmixing of TerraSAR-X staring spotlight interferograms in building scale for PS height and deformation / Peng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkQuantifying historical landscape change with repeat photography: an accuracy assessment of geospatial data obtained through monoplotting / Ulrike Bayr in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)PermalinkRecognition of crevasses with high-resolution digital elevation models: Application of geomorphometric modeling and texture analysis / Olga T. Ishalina in Transactions in GIS, vol 25 n° 5 (October 2021)PermalinkSpectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkUnsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])PermalinkUrban geospatial information acquisition mobile mapping system based on close-range photogrammetry and IGS site calibration / Ming Guo in Geo-spatial Information Science, vol 24 n° 4 (October 2021)PermalinkAerial and UAV images for photogrammetric analysis of Belvedere Glacier evolution in the period 1977–2019 / Carlo Lapige De Gaetani in Remote sensing, vol 13 n° 18 (September-2 2021)PermalinkMapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)PermalinkRecurrent-based regression of Sentinel time series for continuous vegetation monitoring / Anatol Garioud in Remote sensing of environment, vol 263 (15 September 2021)PermalinkAutomatic 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)PermalinkClassification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery / Simbarashe Jombo in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkCombining photogrammetric and bathymetric data to build a 3D model of a canal tunnel / Emmanuel Moisan in Photogrammetric record, Vol 36 n° 175 (September 2021)PermalinkA comparison of ALS and dense photogrammetric point clouds for individual tree detection in radiata pine plantations / Irfan A. Iqbal in Remote sensing, vol 13 n° 17 (September-1 2021)PermalinkConiferous and broad-leaved forest distinguishing using L-band polarimetric SAR data / Fang Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkA deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkDetection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds / Alwin A. Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)PermalinkDetermining optimal photogrammetric adjustment of images obtained from a fixed-wing UAV / Karolina Pargiela in Photogrammetric record, Vol 36 n° 175 (September 2021)PermalinkDevelopment of a GIS-based alert system to mitigate flash flood impacts in Asyut governorate, Egypt / Soha A. Mohamed in Natural Hazards, vol 108 n° 3 (September 2021)PermalinkDouble adaptive intensity-threshold method for uneven Lidar data to extract road markings / Chengming Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkGaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds / Longjie Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkGIS models for vulnerability of coastal erosion assessment in a tropical protected area / Luís Russo Vieira in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)PermalinkHyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkLes journées de la Recherche IGN 2021 / Anonyme in Géomatique expert, n° 135 (septembre 2021)Permalink