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Lithological mapping based on fully convolutional network and multi-source geological data / Ziye Wang in Remote sensing, vol 13 n° 23 (December-1 2021)
[article]
Titre : Lithological mapping based on fully convolutional network and multi-source geological data Type de document : Article/Communication Auteurs : Ziye Wang, Auteur ; Renguang Zuo, Auteur ; Hao Liu, Auteur Année de publication : 2021 Article en page(s) : n° 4860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] carte géologique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données géologiques
[Termes IGN] fusion de données multisource
[Termes IGN] Himalaya
[Termes IGN] lithologie
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping. Numéro de notice : A2021-878 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13234860 Date de publication en ligne : 30/11/2021 En ligne : https://doi.org/10.3390/rs13234860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99146
in Remote sensing > vol 13 n° 23 (December-1 2021) . - n° 4860[article]Modeling transit-assisted hurricane evacuation through socio-spatial networks / Yan Yang in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)
[article]
Titre : Modeling transit-assisted hurricane evacuation through socio-spatial networks Type de document : Article/Communication Auteurs : Yan Yang, Auteur ; Sara Metcalf, Auteur ; Liang Mao, Auteur Année de publication : 2021 Article en page(s) : pp 2424 - 2441 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] comportement
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] gestion de crise
[Termes IGN] modèle de simulation
[Termes IGN] modèle orienté agent
[Termes IGN] réseau social
[Termes IGN] système multi-agents
[Termes IGN] tempête
[Termes IGN] trafic routier
[Termes IGN] transport publicRésumé : (auteur) Increasing intensity and frequency of hurricane events underscores the need for efficient and inclusive evacuation plans, particularly for carless and disabled populations. Hurricane evacuation intrinsically involves both social and spatial processes. People’s decision to evacuate spreads over social networks; once their decisions are made, they flee through spatial transportation networks. This article describes a novel effort to integrate socio-spatial networks into an agent-based evacuation simulation model, taking the Florida Keys in the USA as a study area. In the model, households, as agents, were synthesized from Census data, then connected by a ‘home-workplace-neighborhood’ social network, and registered to a spatial road network. A threshold decision model was used to simulate social contagion of households’ decision to evacuate. The resulting travel demands were input into the TRANSIMS platform to generate on-road traffic. The model analyzed scenarios of automobile-only and public transit-assisted evacuation. The results show that the simulated traffic under the automobile-only scenario aligns with the observed traffic dynamics, which validates our socio-spatially integrated model. Adding public transportation capacity significantly reduces the traffic load and evacuation time, and provides a practical, accessible, and equitable route to safety for low mobility populations. Numéro de notice : A2021-874 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1828590 Date de publication en ligne : 02/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1828590 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99137
in International journal of geographical information science IJGIS > vol 35 n° 12 (December 2021) . - pp 2424 - 2441[article]Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)
[article]
Titre : Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images Type de document : Article/Communication Auteurs : Yiying Hua, Auteur ; Xuesheng Zhao, Auteur Année de publication : 2021 Article en page(s) : n° 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] bande infrarouge
[Termes IGN] coefficient de corrélation
[Termes IGN] couvert forestier
[Termes IGN] détection de contours
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle statistique
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] régression
[Termes IGN] surveillance de la végétationRésumé : (auteur) In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Numéro de notice : A2021-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12121768 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.3390/f12121768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99318
in Forests > vol 12 n° 12 (December 2021) . - n° 1768[article]National scale mapping of larch plantations for Wales using the Sentinel-2 data archive / Suvarna M. Punalekar in Forest ecology and management, vol 501 (December-1 2021)
[article]
Titre : National scale mapping of larch plantations for Wales using the Sentinel-2 data archive Type de document : Article/Communication Auteurs : Suvarna M. Punalekar, Auteur ; Carole Planque, Auteur ; Richard M. Lucas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] carte forestière
[Termes IGN] coupe rase (sylviculture)
[Termes IGN] gestion forestière
[Termes IGN] image infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Larix decidua
[Termes IGN] maladie phytosanitaire
[Termes IGN] modélisation de la forêt
[Termes IGN] Pays de Galles
[Termes IGN] surveillance forestièreRésumé : (auteur) Accurate spatial information regarding forest types and tree species is immensely important for efficient forest management strategies. In the UK and particularly in Wales, creating a spatial inventory of larch (Larix sps.) plantations that encompasses both the public and private forests has become one of the highest priorities of woodland management policies, particularly given the need to respond to the rapid spread of Phytophthora ramorum fungal disease. For directing disease control measures, national scale, regularly updated mapping of larch distributions is essential. In this study, we applied a ExtraTree classifier machine learning algorithm to multi-year (June 2015 and December 2019) multi-path composites of vegetation indices derived from 10 m Sentinel-2 satellite data (spectral range used in this study: 490–2190 nm) to map the extent of larch plantations across Wales. For areas identified as woody vegetation, areas under larch plantations were associated with a needle-leaved leaf type and deciduous phenology, allowing differentiation from broad-leaved deciduous and needle-leaved evergreen types. The model accuracies for validation, which included overall accuracy, producer’s and user’s accuracies, exceeded 95% and the F1-score was greater than 0.97 for all forest types. Comparison against an independent reference dataset indicated all map accuracies above 90% (F1-score higher than 0.92) with the lowest value being 90.3% for the producer’s accuracy for larch. Short wave infrared and red-edge based indices were particularly useful for discriminating larch from other forest types. Capacity for updating information on clear-felling of larch stands through annual updates of a woody mask was also introduced. The resulting maps of larch plantations for Wales are the most current for Wales covering public as well as private woodlands and can be routinely updated. The classification approach has potential to be transferred to a wider geographical area given the availability of open-source multi-year Sentienl-2 datasets and robust calibration datasets. Numéro de notice : A2021-741 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119679 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98657
in Forest ecology and management > vol 501 (December-1 2021) . - n° 119679[article]Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])
[article]
Titre : Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images Type de document : Article/Communication Auteurs : Mohammad Hossein Gamshadzaei, Auteur ; Majid Rahimzadegan, Auteur Année de publication : 2021 Article en page(s) : pp 2264 - 2278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multibande
[Termes IGN] analyse spectrale
[Termes IGN] Arménie
[Termes IGN] bande infrarouge
[Termes IGN] cartographie thématique
[Termes IGN] détection d'objet
[Termes IGN] eau de surface
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Google Earth
[Termes IGN] image à haute résolution
[Termes IGN] image satellite
[Termes IGN] indice d'humidité
[Termes IGN] Iran
[Termes IGN] occupation du sol
[Termes IGN] optimisation par essaim de particules
[Termes IGN] polygoneRésumé : (auteur) Various spectral indices have been introduced to detect water extent from satellite images with different performances in various regions. The aim of this study is to provide an efficient index using particle swarm optimization (PSO) algorithm to detect water spread areas from satellite images with similar performance in different regions. This index is introduced for images containing water absorption bands from visible to middle infrared wavelengths. Eleven images were prepared from different satellites and water bodies with various environmental conditions. In addition, 40 pixels from water and 40 pixels from non-water regions were selected as training data for PSO algorithm. Results were evaluated using digitized polygons of water bodies on high-resolution images of Google Earth. The best results in PSO-based water index (PSOWI) were obtained by the combination of two bands (red and middle infrared). PSOWI represented proper performance in the selected various land covers and satellite images. Numéro de notice : A2021-831 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1700554 Date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/10106049.2019.1700554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99004
in Geocarto international > vol 36 n° 20 [01/12/2021] . - pp 2264 - 2278[article]La photogrammétrie appliquée au récolement des réseaux enterrés : retour d’expérience d’une méthode industrialisée / Jérôme Leroux in XYZ, n° 169 (décembre 2021)PermalinkReal-time web map construction based on multiple cameras and GIS / Xingguo Zhang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)PermalinkThe method of detection and localization of configuration defects in geodetic networks by means of Tikhonov regularization / Roman Kadaj in Reports on geodesy and geoinformatics, vol 112 n° 1 (December 2021)PermalinkUnderstanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)PermalinkUsing textual volunteered geographic information to model nature-based activities: A case study from Aotearoa New Zealand / Ekaterina Egorova in Journal of Spatial Information Science (JoSIS), n° 23 (2021)PermalinkVGI3D: an interactive and low-cost solution for 3D building modelling from street-level VGI images / Chaoquan Zhang in Journal of Geovisualization and Spatial Analysis, vol 5 n° 2 (December 2021)PermalinkFeature matching for multi-epoch historical aerial images: A new pipeline feature detection pipeline in open-source MicMac / Lulin Zhang in Blog de la RFPT, sans n° ([17/11/2021])PermalinkCrop rotation modeling for deep learning-based parcel classification from satellite time series / Félix Quinton in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkForest structural complexity tool: An open source, fully-automated tool for measuring forest point clouds / Sean Krisanski in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkA semantics-based approach for simplifying IFC building models to facilitate the use of BIM models in GIS / Junxiang Zhu in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkAccuracy assessment of RTK-GNSS equipped UAV conducted as-built surveys for construction site modelling / Sander Varbla in Survey review, Vol 53 n° 381 (November 2021)PermalinkAnalytics of location-based big data for smart cities: Opportunities, challenges, and future directions / Haosheng Huang in Computers, Environment and Urban Systems, vol 90 (November 2021)PermalinkBagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)PermalinkCalibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation / Jingyan Yu in Computers, Environment and Urban Systems, vol 90 (November 2021)PermalinkA comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkDiffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkDownscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)PermalinkEvaluation of watershed soil erosion hazard using combination weight and GIS: a case study from eroded soil in Southern China / Shifa Chen in Natural Hazards, vol 109 n° 2 (November 2021)PermalinkFeature matching for multi-epoch historical aerial images / Lulin Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)PermalinkFully automated pose estimation of historical images in the context of 4D geographic information systems utilizing machine learning methods / Ferdinand Maiwald in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkGIS-based study on the environmental sensitivity to pollution and susceptibility to eutrophication in Burullus Lake, Egypt / Muhammad A. El-Alfy in Marine geodesy, vol 44 n° 6 (November 2021)PermalinkInteractive maps for the production of knowledge and the promotion of participation from the perspective of communication, journalism, and digital humanities / Pedro Molina Rodríguez-Navas in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkIonospheric tomographic common clock model of undifferenced uncombined GNSS measurements / German Olivares-Pulido in Journal of geodesy, vol 95 n° 11 (November 2021)PermalinkMobile mapping et PCRS / Clément Benoît in Géomatique expert, n° 136 (novembre - décembre 2021)PermalinkMulti-objective CNN-based algorithm for SAR despeckling / Sergio Vitale in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)PermalinkPoint-of-interest (POI) data validation methods: An urban case study / Lih Wei Yeow in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkPose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior / Maximilian Alexander Coenen in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkQGIS 3.22, A.K.A. Bialowieza / Anonyme in Géomatique expert, n° 136 (novembre - décembre 2021)PermalinkA quantitative comparison of regionalization methods / Orhun Aydun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkQuels besoins de connaissances pour le futur des forêts en France ? Au-delà du plan de relance / Maya Leroy in Revue forestière française, vol 73 n° 1 (2021)PermalinkReal-time GNSS precise point positioning using improved robust adaptive Kalman filter / Abdelsatar Elmezayen in Survey review, Vol 53 n° 381 (November 2021)PermalinkRobust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features / Bai Zhu in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkSpatially–encouraged spectral clustering: a technique for blending map typologies and regionalization / Levi John Wolf in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkThe geography of social media data in urban areas: Representativeness and complementarity / Alvaro Bernabeu-Bautista in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkUtilisation 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)PermalinkSTC-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)PermalinkDétection des forêts dégradées en Guinée à partir des images satellites Sentinel-2 : évaluation de l'apport potentiel des nouveaux capteurs satellitaires optiques et radars / An Vo Quang in Blog de la RFPT, sans n° ([11/10/2021])PermalinkEvaluating urban intensity through a city information model - intermediate results from an action research project / Adeline Deprêtre in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol VIII-4/W2-2021 ([07/10/2021])PermalinkAdaptive 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)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)PermalinkDeep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (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)PermalinkEarly detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (October-1 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)PermalinkField scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques / Rajkumar Dhakar in Geocarto international, vol 36 n° 18 ([01/10/2021])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)PermalinkGROOPS: A software toolkit for gravity field recovery and GNSS processing / Torsten Mayer-Gürr in Computers & geosciences, vol 155 (October 2021)PermalinkImpact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)PermalinkInvestigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)PermalinkLandslide susceptibility prediction based on image semantic segmentation / Bowen Du in Computers & geosciences, vol 155 (October 2021)PermalinkLeast squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing / Radhika Ravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (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)PermalinkSpatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkSpatial thinking in cartography teaching for schoolchildren / Sonia Maria Vanzella Castellar in International journal of cartography, vol 7 n° 3 (October 2021)PermalinkThe integration of GPS/BDS real-time kinematic positioning and visual–inertial odometry based on smartphones / Zun Niu in ISPRS International journal of geo-information, vol 10 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])PermalinkA web GIS-based integration of 3D digital models with linked open data for cultural heritage exploration / Ikrom Nishanbaev in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkAssessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])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)PermalinkThe impact of landscape characteristics on the performance of upscaled maps / Peijun Sun in Geocarto international, vol 36 n° 17 ([15/09/2021])Permalink3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (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)PermalinkBenford’s law and geographical information – the example of OpenStreetMap / Franz-Benjamin Mocnik in International journal of geographical information science IJGIS, vol 35 n° 9 (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 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)PermalinkA framework for ecosystem service assessment using GIS interoperability standards / Martin Lacayo in Computers & geosciences, vol 154 (September 2021)PermalinkGIScience integrated with computer vision for the examination of old engravings and drawings / Motti Zohar in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)PermalinkIndoor space as the basis for modelling of buildings in a 3D Cadastre / Jernej Tekavec in Survey review, Vol 53 n° 380 (September 2021)PermalinkLes journées de la Recherche IGN 2021 / Anonyme in Géomatique expert, n° 135 (septembre 2021)PermalinkA learning-based approach to automatically evaluate the quality of sequential color schemes for maps / Taisheng Chen in Cartography and Geographic Information Science, Vol 48 n° 5 (September 2021)PermalinkMetaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs / Arnaud Flori in Machine Vision and Applications, vol 32 n° 5 (September 2021)PermalinkMise en place d'un dispositif expérimental numérique pour l'enseignement des risques naturels avec le jeu vidéo Minetest / Jérôme Staub in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)PermalinkLa modélisation automatique d'un bâtiment à partir d'un nuage de points / Thibault Mauger in XYZ, n° 168 (septembre 2021)PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkA multiagent systems with Petri Net approach for simulation of urban traffic networks / Mauricio Flores Geronimo in Computers, Environment and Urban Systems, vol 89 (September 2021)PermalinkRelevés de la grotte Cosquer : partie 1, la grotte, les premiers relevés, un monument et une histoire chaotiques / Bertrand Chazaly in XYZ, n° 168 (septembre 2021)PermalinkRelevés de la grotte Cosquer : partie 2, vers une dématérialisation de la grotte par digitalisation des processus et numérisations 3D, comment offrir à la communauté scientifique et au grand public l’accès au plus inaccessible des grands sanctuaires ornés d’Europe / Bertrand Chazaly in XYZ, n° 168 (septembre 2021)PermalinkStochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network / Jussi Leinonen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkThree-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)PermalinkDeep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)PermalinkA hybrid data model for dynamic GIS: application to marine geomorphological dynamics / Younes Hamdani in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)PermalinkImproving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkIntegrating GIS and location modeling: A relational approach / Ting L. Lei in Transactions in GIS, Vol 25 n° 4 (August 2021)PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkMeasuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)PermalinkPredicting user activity intensity using geographic interactions based on social media check-in data / Jing Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkRandom forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)Permalink