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Étude des outils permettant la classification d’un nuage de points LiDAR aérien et optimisation de la chaîne de traitement dans le cadre du programme national du LiDAR HD in XYZ, n° 179 (juin 2024)
[article]
Titre : Étude des outils permettant la classification d’un nuage de points LiDAR aérien et optimisation de la chaîne de traitement dans le cadre du programme national du LiDAR HD Type de document : Article/Communication Année de publication : 2024 Article en page(s) : pp. 35 - 42 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification
[Termes IGN] image aérienne
[Termes IGN] intelligence artificielle
[Termes IGN] Lidar
[Termes IGN] semis de pointsRésumé : Ce travail présente une étude portant sur la classification de nuages de points issus d’une acquisition aérienne, en se concentrant sur les données acquises dans le cadre du projet national LiDAR HD. Il réalise une analyse critique des outils proposés par Terrascan et des méthodes pa- ramétriques qui offrent un bon rapport temps/qualité, mais il subsiste des confusions qui demandent un temps de correction conséquent. De plus, les outils Terrascan sont limités à la classification du sol, des bâtiments et d’une partie de la végétation. Il n’est pas proposé de méthodes efficaces pour classifier des éléments de la classe du sursol pérenne, comme les pylônes électriques ou les éoliennes notamment. Pour y remédier, une autre méthode innovante, basée sur les descripteurs 3D est proposée. Cette méthode offre une meilleure détection des bâtiments et permet, en outre, de classifier des éléments du sursol pérenne. Enfin, il est étudié les synergies entre les différents outils testés. Puis les performances d’une IA sont introduites afin de discuter de l’avenir de la classification des nuages de points aériens. Numéro de notice : A2024-1792 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103658
in XYZ > n° 179 (juin 2024) . - pp. 35 - 42[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 112-2024021 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Detailed cultural heritage recording produced with traditional methods and laser scanning / Ljubo Lah in Geodetski vestnik, vol 67 n° 4 (December 2023)
[article]
Titre : Detailed cultural heritage recording produced with traditional methods and laser scanning Type de document : Article/Communication Auteurs : Ljubo Lah, Auteur ; Alain Guerreau, Auteur ; Mojca K. Fras, Auteur ; Tilen Urbančič, Auteur Année de publication : 2023 Article en page(s) : pp 442 - 458 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] église
[Termes IGN] matrice orthogonale
[Termes IGN] patrimoine culturel
[Termes IGN] topométrie de précisionRésumé : (auteur) Traditional measurement methods are still widely used for recording cultural heritage objects. On the other hand, geodetic surveying and modern technologies such as 3D laser scanning can provide more accurate, geometrically consistent and extremely detailed data that can be used as a basis for detailed vector maps or 3D models. The main aim of our research was to investigate the complementary approach, using both traditional and modern methods, in order to produce detailed vector maps of the Romanesque church of St. Martin in Chapaize, France, which are essential for further unveiling its historic development. Geometrically, this church is rather extensive and has many irregularities in its shape. Our approach to the documentation process is presented and evaluated in this paper. We applied the Procrustes analysis for the ground floor map, which gave us an objective accuracy assessment. Point clouds of the bell tower acquired by two different laser instruments have also been compared. Numéro de notice : A2023-240 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2023.04.442-458 Date de publication en ligne : 01/12/2023 En ligne : https://dx.doi.org/10.15292/geodetski-vestnik.2023.04.442-458 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103603
in Geodetski vestnik > vol 67 n° 4 (December 2023) . - pp 442 - 458[article]A Powerful Correspondence Selection Method for Point Cloud Registration Based on Machine Learning / Wuyong Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 11 (November 2023)
[article]
Titre : A Powerful Correspondence Selection Method for Point Cloud Registration Based on Machine Learning Type de document : Article/Communication Auteurs : Wuyong Tao, Auteur ; Dong Xu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 703 - 712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de points
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] semis de pointsRésumé : (auteur) Correspondence selection is an indispensable process in point cloud registration. The success of point cloud registration largely depends on a good correspondence selection method. For this purpose, a novel correspondence selection method is proposed in this paper. First, two geometric constraints, one of which is proposed in this paper, are used to compute the compatibility score between two correspondences. Then, the feature vectors of the correspondences are constructed according to the compatibility scores between the correspondence and others. A support vector machine classifier is trained to classify the correct and incorrect correspondences by using the feature vectors. The experimental results demonstrate that our method can choose the right correspondences well and get high precision and F-score performance. Also, our method has the best robustness to noise, pointdensity variation, and partial overlap compared to the other methods. Numéro de notice : A2023-237 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.23-00046R2 En ligne : https://doi.org/10.14358/PERS.23-00046R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103597
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 11 (November 2023) . - pp 703 - 712[article]Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images / Ziyao Xing in Sustainable Cities and Society, vol 92 (May 2023)
[article]
Titre : Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images Type de document : Article/Communication Auteurs : Ziyao Xing, Auteur ; Shuai Yang, Auteur ; Xuli Zan, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104467 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] bâtiment
[Termes IGN] Chine
[Termes IGN] gestion des risques
[Termes IGN] image Streetview
[Termes IGN] inondation
[Termes IGN] milieu urbain
[Termes IGN] planification urbaine
[Termes IGN] Quickbird
[Termes IGN] segmentation sémantique
[Termes IGN] vulnérabilitéRésumé : (auteur) Urban flood risk management requires an extensive investigation of the vulnerability characteristics of buildings. Large-scale field surveys usually cost a lot of time and money, while satellite remote sensing and street view images can provide information on the tops and facades of buildings respectively. Thereupon, this paper develops a building vulnerability assessment framework using remote sensing and street view features. Specifically, a UNet-based semantic segmentation model, FSA-UNet (Fusion-Self-Attention-UNet) is proposed to integrate remote sensing and street view features and the vulnerability information contained in the images is fully exploited. And the building vulnerability index is generated to provide the spatial distribution characteristics of urban building vulnerability. The experiment shows that the mIoU of the proposed model can reach 82% for building vulnerability classification in Hefei, China, which is more accurate than the traditional semantic segmentation models. The results indicate that the integration of street view and remote sensing image features can improve the ability of building vulnerability assessment, and the model proposed in this study can better capture the correlation features of multi-angle images through the self-attention mechanism and combines hierarchy features and edge information to improve the classification effect. This study can support for disaster management and urban planning. Numéro de notice : A2023-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2023.104467 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.1016/j.scs.2023.104467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102826
in Sustainable Cities and Society > vol 92 (May 2023) . - n° 104467[article]Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks / Sina Mohammadi in ISPRS Journal of photogrammetry and remote sensing, vol 198 (April 2023)
[article]
Titre : Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks Type de document : Article/Communication Auteurs : Sina Mohammadi, Auteur ; Mariana Belgiu, Auteur ; Alfred Stein, Auteur Année de publication : 2023 Article en page(s) : pp 272 - 283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage profond
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] cultures
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleRésumé : (auteur) Deep learning methods have achieved promising results in crop mapping using satellite image time series. A challenge still remains on how to better learn discriminative feature representations to detect crop types when the model is applied to unseen data. To address this challenge and reveal the importance of proper supervision of deep neural networks in improving performance, we propose to supervise intermediate layers of a designed 3D Fully Convolutional Neural Network (FCN) by employing two middle supervision methods: Cross-entropy loss Middle Supervision (CE-MidS) and a novel middle supervision method, namely Supervised Contrastive loss Middle Supervision (SupCon-MidS). This method pulls together features belonging to the same class in embedding space, while pushing apart features from different classes. We demonstrate that SupCon-MidS enhances feature discrimination and clustering throughout the network, thereby improving the network performance. In addition, we employ two output supervision methods, namely F1 loss and Intersection Over Union (IOU) loss. Our experiments on identifying corn, soybean, and the class Other from Landsat image time series in the U.S. corn belt show that the best set-up of our method, namely IOU+SupCon-MidS, is able to outperform the state-of-the-art methods by
scores of 3.5% and 0.5% on average when testing its accuracy across a different year (local test) and different regions (spatial test), respectively. Further, adding SupCon-MidS to the output supervision methods improves
scores by 1.2% and 7.6% on average in local and spatial tests, respectively. We conclude that proper supervision of deep neural networks plays a significant role in improving crop mapping performance. The code and data are available at: https://github.com/Sina-Mohammadi/CropSupervision.Numéro de notice : A2023-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.03.007 Date de publication en ligne : 29/03/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103105
in ISPRS Journal of photogrammetry and remote sensing > vol 198 (April 2023) . - pp 272 - 283[article]Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery / Maryam Hosseini in Computers, Environment and Urban Systems, vol 101 (April 2023)PermalinkA network-constrained clustering method for bivariate origin-destination movement data / Wenkai Liu in International journal of geographical information science IJGIS, vol 37 n° 4 (April 2023)PermalinkTowards global scale segmentation with OpenStreetMap and remote sensing / Munazza Usmani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)PermalinkDomain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)PermalinkPoint cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction / Shuo Shi in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)PermalinkSpecies distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal / Cristina Alegria in Forests, vol 14 n° 3 (March 2023)PermalinkThe potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes / Anna Iglseder in International journal of applied Earth observation and geoinformation, vol 117 (March 2023)PermalinkA unified attention paradigm for hyperspectral image classification / Qian Liu in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)PermalinkA spatial distribution: Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil / Jiawei Liu in Science of the total environment, vol 859 n° 1 (February 2023)PermalinkComparative analysis of different CNN models for building segmentation from satellite and UAV images / Batuhan Sariturk in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 2 (February 2023)PermalinkGenerating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network / Jingan Wu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkLarge-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkMeasuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model / Zensheng Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)PermalinkMulti-nomenclature, multi-resolution joint translation: an application to land-cover mapping / Luc Baudoux in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)PermalinkPSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkStochastic multicriteria acceptability analysis as a forest management priority mapping approach based on airborne laser scanning and field inventory data / Parvez Rana in Landscape and Urban Planning, vol 230 (February 2023)PermalinkTopology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds / Xin Xu in International journal of applied Earth observation and geoinformation, vol 116 (February 2023)PermalinkPermalinkPermalinkA CNN based approach for the point-light photometric stereo problem / Fotios Logothetis in International journal of computer vision, vol 131 n° 1 (January 2023)PermalinkA comparative assessment of the statistical methods based on urban population density estimation / Merve Yılmaz in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkDecision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkForest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkGeographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol inconnu (2023)PermalinkA geometry-aware attention network for semantic segmentation of MLS point clouds / Jie Wan in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)PermalinkGeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates / Valerio Marsocci (2023)PermalinkGeospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)PermalinkA hexagon-based method for polygon generalization using morphological operators / Lu Wang in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)PermalinkA hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)PermalinkImprovement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier / Mahmoud Mohamed in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkImproving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar / Andrew W. Whelan in Remote sensing of environment, vol 284 (January 2023)PermalinkInvestigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery / Komeil Rokni in Geodesy and cartography, vol 49 n° 1 (January 2023)PermalinkLarge-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach / Shenglong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)PermalinkA machine learning method for Arctic lakes detection in the permafrost areas of Siberia / Piotr Janiec in European journal of remote sensing, vol 56 n° 1 (2023)PermalinkMachine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)PermalinkMeasuring metro accessibility: An exploratory study of Wuhan based on multi-source urban data / Tao Wu in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)PermalinkA method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination / Kaili Zhang in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkModern vectorization and alignment of historical maps: An application to Paris Atlas (1789-1950) / Yizi Chen (2023)PermalinkMTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction / Du Yin in Geoinformatica, vol 27 n° 1 (January 2023)PermalinkMulti-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkMultipath mitigation for improving GPS narrow-lane uncalibrated phase delay estimation and speeding up PPP ambiguity resolution / Kai Zheng in Measurement, vol 206 (January 2023)PermalinkPrototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation / Zhimin Yuan in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)PermalinkSemi-supervised label propagation for multi-source remote sensing image change detection / Fan Hao in Computers & geosciences, vol 170 (January 2023)PermalinkSolid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach / Bowen Niu in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkSpatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China / Ziyi Cao in Open geosciences, vol 14 n° 1 (January 2023)PermalinkPermalinkThe cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkTree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkUsing Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study / W.D.K.V. Nandasena in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkWavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) / Rolando Carbonari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)PermalinkAssessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])PermalinkAutomatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery / Yuxin Wang in Science of the total environment, vol 853 (December 2022)PermalinkConsistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows / Pramaditya Wicaksono in Geocarto international, vol 37 n° 27 ([20/12/2022])PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)PermalinkAutomatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)PermalinkAutomatic registration of point cloud and panoramic images in urban scenes based on pole matching / Yuan Wang in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkClimate envelope analyses suggests significant rearrangements in the distribution ranges of Central European tree species / Gàbor Illés in Annals of Forest Science, vol 79 n° 1 (2022)PermalinkA comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images / Hessah Albanwan in Photogrammetric record, vol 37 n° 180 (December 2022)PermalinkComparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory / Charlotte Labit in Biodiversity & Conservation, vol 31 n° 13-14 (December 2022)PermalinkA data-driven framework to manage uncertainty due to limited transferability in urban growth models / Jingyan Yu in Computers, Environment and Urban Systems, vol 98 (December 2022)PermalinkDeep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery / Thomas A. Lake in Remote sensing in ecology and conservation, vol 8 n° 6 (December 2022)PermalinkDiscriminating pure Tamarix species and their putative hybrids using field spectrometer / Solomon G. Tesfamichael in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkEstablishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale / Shengwu Qin in Natural Hazards, vol 114 n° 3 (December 2022)PermalinkFusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)PermalinkGeographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)PermalinkHybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkInstance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)PermalinkIntegration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)PermalinkModelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)PermalinkA new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions / Rasit Ulug in Journal of geodesy, vol 96 n° 12 (December 2022)PermalinkProgressive collapse of dual-line rivers based on river segmentation considering cartographic generalization rules / Fubing Zhang in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)PermalinkSea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkSemantic integration of OpenStreetMap and CityGML with formal concept analysis / Somayeh Ahmadian in Transactions in GIS, vol 26 n° 8 (December 2022)PermalinkSemantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)PermalinkA semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkStreet-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data / Yatao Zhang in Transactions in GIS, vol 26 n° 8 (December 2022)PermalinkThe simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)PermalinkUrban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis / Das Subhasis in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkA whale optimization algorithm–based cellular automata model for urban expansion simulation / Yuan Ding in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkAccuracy of vacant housing detection models: An empirical evaluation using municipal and national census datasets / Kanta Sayuda in Transactions in GIS, vol 26 n° 7 (November 2022)PermalinkAn unsupervised framework for extracting multilane roads from OpenStreetMap / Kunkun Wu in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)PermalinkChange alignment-based image transformation for unsupervised heterogeneous change detection / Kuowei Xiao in Remote sensing, vol 14 n° 21 (November-1 2022)PermalinkCross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening / Jiahui Qu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)PermalinkExploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)PermalinkFeatures predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (November-1 2022)PermalinkForeground-aware refinement network for building extraction from remote sensing images / Zhang Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 11 (November 2022)PermalinkGA-Net: A geometry prior assisted neural network for road extraction / Xin Chen in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)PermalinkGraph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds / Zhilin Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)PermalinkImproving deep learning on point cloud by maximizing mutual information across layers / Di Wang in Pattern recognition, vol 131 (November 2022)PermalinkImproving image segmentation with boundary patch refinement / Xiaolin Hu in International journal of computer vision, vol 130 n° 11 (November 2022)PermalinkMachine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)PermalinkMachine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)PermalinkMeasuring visual walkability perception using panoramic street view images, virtual reality, and deep learning / Yunqin Li in Sustainable Cities and Society, vol 86 (November 2022)PermalinkMulti-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)Permalink