Descripteur
Documents disponibles dans cette catégorie (8943)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
A conceptual framework for developing dashboards for big mobility data / Lindsey Conrow in Cartography and Geographic Information Science, Vol 50 n° 5 (June 2023)
[article]
Titre : A conceptual framework for developing dashboards for big mobility data Type de document : Article/Communication Auteurs : Lindsey Conrow, Auteur ; Cheng Fu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 495 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cadre conceptuel
[Termes IGN] données massives
[Termes IGN] mobilité humaine
[Termes IGN] tableau de bordRésumé : (auteur) Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets. Numéro de notice : A2023-236 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2023.2190164 Date de publication en ligne : 11/04/2023 En ligne : https://doi.org/10.1080/15230406.2023.2190164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103595
in Cartography and Geographic Information Science > Vol 50 n° 5 (June 2023) . - pp 495 - 514[article]Research on map emotional semantics using deep learning approach / Daping Xi in Cartography and Geographic Information Science, Vol 50 n° 5 (June 2023)
[article]
Titre : Research on map emotional semantics using deep learning approach Type de document : Article/Communication Auteurs : Daping Xi, Auteur ; Xini Hu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 465 - 480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] émotion
[Termes IGN] réseau neuronal profondRésumé : (auteur) The main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules. Numéro de notice : A2023-235 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2023.2172081 Date de publication en ligne : 21/02/2023 En ligne : https://doi.org/10.1080/15230406.2023.2172081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103594
in Cartography and Geographic Information Science > Vol 50 n° 5 (June 2023) . - pp 465 - 480[article]Resilient GNSS real-time kinematic precise positioning with inequality and equality constraints / Zhetao Zhang in GPS solutions, vol 27 n° 3 (July 2023)
[article]
Titre : Resilient GNSS real-time kinematic precise positioning with inequality and equality constraints Type de document : Article/Communication Auteurs : Zhetao Zhang, Auteur ; Yuan Li, Auteur ; Xiufeng He, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] contrainte d'intégrité
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement par GNSS
[Termes IGN] précision du positionnement
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) How to conduct the GNSS real-time kinematic precise positioning in challenging environments is not an easy problem. The challenging environment mainly refers to frequent signal reflection, refraction, diffraction, and occlusion, inevitably introducing large positioning errors. We propose a resilient positioning method considering the inequality and equality constraints. Specifically, first, we introduce the functional and stochastic models of real-time kinematic (RTK) positioning, considering the impacts of challenging environments. Second, specific iterative procedures of resilient GNSS precise positioning method with inequality and equality constraints are proposed. In addition, a general form of inequality constraints in terms of coordinate components is given that is suitable for real-time kinematic situations. Four 24-h real datasets in canyon environments were collected to verify the performance of the proposed method. The results show that compared with the traditional RTK positioning without inequality constraints, the proposed method can improve the success rates of ambiguity resolution by 42.2% on average. Also, the positioning accuracy of fixed solutions can be improved significantly after applying the proposed method, where the root mean square errors can be reduced by 77.2% on average. Therefore, the proposed method can significantly improve success rates of ambiguity resolution and positioning accuracy, which is especially promising in challenging environments. Numéro de notice : A2023-213 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-023-01454-0 Date de publication en ligne : 26/04/2023 En ligne : https://doi.org/10.1007/s10291-023-01454-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103142
in GPS solutions > vol 27 n° 3 (July 2023) . - n° 116[article]FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach / Martin Schwartz in Earth System Science Data, vol 15 n° inconnu (2023)
[article]
Titre : FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach Type de document : Article/Communication Auteurs : Martin Schwartz, Auteur ; Philippe Ciais, Auteur ; Aurélien de Truchis, Auteur ; Jérôme Chave, Auteur ; Catherine Ottle, Auteur ; Cédric Vega , Auteur ; Jean-Pierre Wigneron, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] biomasse aérienne
[Termes IGN] données allométriques
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle numérique de surface de la canopée
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote sensing measurements (NASA’s GEDI LiDAR mission and ESA’s Copernicus Sentinel 1 & 2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha-1) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from Airborne Laser Scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 Mg ha-1 and 19.6 Mg.ha-1 respectively, providing greater performance than other AGBD products sampled over France. These results highlight the importance of coupling remote sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from https://doi.org/10.5281/zenodo.7840108 (Schwartz et al., 2023). Numéro de notice : A2023-179 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-2023-196 En ligne : https://doi.org/10.5194/essd-2023-196 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103341
in Earth System Science Data > vol 15 n° inconnu (2023)[article]A minimal solution for image-based sphere estimation / Tekla Tóth in International journal of computer vision, vol 131 n° 6 (June 2023)
[article]
Titre : A minimal solution for image-based sphere estimation Type de document : Article/Communication Auteurs : Tekla Tóth, Auteur ; Levente Hajder, Auteur Année de publication : 2023 Article en page(s) : pp 1428 - 1447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de Levenberg-Marquardt
[Termes IGN] cône
[Termes IGN] ellipse
[Termes IGN] Matlab
[Termes IGN] reconstruction d'image
[Termes IGN] représentation géométrique
[Termes IGN] sphère
[Termes IGN] sphère paramétriqueRésumé : (auteur) We propose a novel minimal solver for sphere fitting via its 2D central projection, i.e., a special ellipse. The input of the presented algorithm consists of contour points detected in a camera image. General ellipse fitting problems require five contour points. However, taking advantage of the isotropic spherical target, three points are enough to define the tangent cone parameters of the sphere. This yields the sought ellipse parameters. Similarly, the sphere center can be estimated from the cone if the radius is known. These proposed geometric methods are rapid, numerically stable, and easy to implement. Experimental results—on synthetic, photorealistic, and real images—showcase the superiority of the proposed solutions to the state-of-the-art methods. A real-world LiDAR-camera calibration application justifies the utility of the sphere-based approach resulting in an error below a few centimeters. Numéro de notice : A2023-189 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-023-01766-1 Date de publication en ligne : 02/03/2023 En ligne : https://doi.org/10.1007/s11263-023-01766-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103061
in International journal of computer vision > vol 131 n° 6 (June 2023) . - pp 1428 - 1447[article]Optimized position estimation in mobile multipath environments using machine learning / Nesreen I. Ziedan in Navigation : journal of the Institute of navigation, vol 70 n° 2 (Summer 2023)PermalinkDeblurring low-light images with events / Chu Zhou in International journal of computer vision, vol 131 n° 5 (May 2023)PermalinkDetecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data / Haoyi Xiong in International journal of geographical information science IJGIS, vol 37 n° 5 (May 2023)PermalinkTransform paper-based cadastral data into digital systems using GIS and end-to-end deep learning techniques / Joseph Mango in International journal of geographical information science IJGIS, vol 37 n° 5 (May 2023)PermalinkAutomatic generation of outline-based representations of landmark buildings with distinctive shapes / Peng Ti in International journal of geographical information science IJGIS, vol 37 n° 4 (April 2023)PermalinkEvaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs / Alvin Christopher G. Varquez in Sustainable Cities and Society, vol 91 (April 2023)PermalinkA global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT / Shengli Tao in Earth System Science Data, vol 15 n° 4 (2023)PermalinkImprovement 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)PermalinkLight mode and dark mode: Which one is suitable when using public-facing web maps? An experimental evaluation using eye-tracking / Lige Qiao in Transactions in GIS, vol 27 n° 2 (april 2023)PermalinkMapping 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)PermalinkMethods for matching English language addresses / Keshav Ramani in Transactions in GIS, vol 27 n° 2 (april 2023)PermalinkPoint cloud registration for LiDAR and photogrammetric data: A critical synthesis and performance analysis on classic and deep learning algorithms / Ningli Xu in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)PermalinkAnalyse des performances de levers LiDAR via l’iPad Pro en vue de la réalisation de plans d’intérieurs et de maquettes numériques de bâtiments / Pauline Chardon in XYZ, n° 174 (mars 2023)PermalinkAssessing the cognition of movement trajectory visualizations: interpreting speed and direction / Crystal J. Bae in Cartography and Geographic Information Science, Vol 50 n° 2 (March 2023)PermalinkChatGPT pour la géomatique, potentiel d’utilisation et limites / Emmanuel Clédat in XYZ, n° 174 (mars 2023)PermalinkDeriving map images of generalised mountain roads with generative adversarial networks / Azelle Courtial in International journal of geographical information science IJGIS, vol 37 n° 3 (March 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)PermalinkGeneration of concise 3D building model from dense meshes by extracting and completing planar primitives / Xinyi Liu in Photogrammetric record, vol 38 n° 181 (March 2023)PermalinkHow does the design of landmarks on a mobile map influence wayfinding experts’ spatial learning during a real-world navigation task? / Armand Kapaj in Cartography and Geographic Information Science, Vol 50 n° 2 (March 2023)PermalinkMapping population distribution from open address data: application to mainland Portugal / Nelson Mileu in Journal of maps, vol 18 n° 3 (March 2023)PermalinkDes mesures au sol aux images satellite : quelles données pour étudier la pollution lumineuse ? / Christophe Plotard in XYZ, n° 174 (mars 2023)PermalinkNear real-time global ionospheric total electron content modeling and nowcasting based on GNSS observations / Xulei Jin in Journal of geodesy, vol 97 n° 3 (March 2023)PermalinkProgramme LiDAR HD : vers une nouvelle cartographie 3D du territoire / Terry Moreau in XYZ, n° 174 (mars 2023)PermalinkProvenance in GIServices: A semantic web approach / Zhaoyan Wu in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)PermalinkResidents’ Experiential Knowledge and Its Importance for Decision-Making Processes in Spatial Planning: A PPGIS Based Study / Edyta Bąkowska-Waldmann in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)PermalinkSALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)PermalinkSiamese KPConv: 3D multiple change detection from raw point clouds using deep learning / Iris de Gelis in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)PermalinkA spatiotemporal data model and an index structure for computational time geography / Bi Yu Chen in International journal of geographical information science IJGIS, vol 37 n° 3 (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)PermalinkValidation of Island 3D-mapping based on UAV spatial point cloud optimization: a case study in Dongluo Island of China / Jian Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 3 (March 2023)PermalinkWho owns the map? Data sovereignty and government spatial data collection, use, and dissemination / Peter A. Johnson in Transactions in GIS, vol 27 n° 1 (February 2023)PermalinkAnalysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (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)PermalinkMulti-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services / Mingyue Xu 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)PermalinkNonparametric upscaling of bark beetle infestations and management from plot to landscape level by combining individual-based with Markov chain models / Bruno Walter Pietzsch in European Journal of Forest Research, vol 142 n° 1 (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)PermalinkWhere am I now? modelling disorientation in pan-scalar maps / Guillaume Touya in ISPRS International journal of geo-information, vol 12 n° 2 (February 2023)Permalink3D building metrics for urban morphology / Anna Labetski in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)PermalinkPermalinkA benchmark of nested named entity recognition approaches in historical structured documents / Solenn Tual (2023)PermalinkBIM et enjeux climatiques, ch. City Information Modelling pour des aménagements sobres et durables : potentiel du CIM pour calculer l’intensité urbaine / Adeline Deprêtre (2023)PermalinkPermalinkComparative use of PPK-integrated close-range terrestrial photogrammetry and a handheld mobile laser scanner in the measurement of forest road surface deformation / Remzi Eker in Measurement, vol 206 (January 2023)PermalinkCréation d’une base de connaissances topographiques à partir des "Instructions nautiques" / Helen Mair Rawsthorne (2023)PermalinkCréation d’un graphe de connaissances géohistorique à partir d’annuaires du commerce parisien du 19ème siècle : application aux métiers de la photographie / Solenn Tual (2023)PermalinkCross-supervised learning for cloud detection / Kang Wu in GIScience and remote sensing, vol 60 n° 1 (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])PermalinkPermalinkEstablishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP / Pengfei Xia in GPS solutions, vol 27 n° 1 (January 2023)PermalinkEvaluation of GNSS-based volunteered geographic information for assessing visitor spatial distribution within protected areas: A case study of the Bavarian Forest National Park, Germany / Laura Horst in Applied Geography, vol 150 (January 2023)PermalinkPermalinkGeneration of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data / Ji Won Suh in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 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)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)PermalinkHGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation / Xuefeng Guan in Computers, Environment and Urban Systems, vol 99 (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)PermalinkA hierarchical multiview registration framework of TLS point clouds based on loop constraint / Hao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 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)PermalinkIncorporating ideas of structure and meaning in interactive multi scale mapping environments / Guillaume Touya in International journal of cartography, vol inconnu (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)PermalinkPermalinkModern 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])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)PermalinkPSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)PermalinkA real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration / Tarek Hassan in Journal of applied geodesy, vol 17 n° 1 (January 2023)PermalinkRemote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia / Lifan Ji in European journal of remote sensing, vol 56 n° 1 (2023)PermalinkSemi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)PermalinkSemi-supervised label propagation for multi-source remote sensing image change detection / Fan Hao in Computers & geosciences, vol 170 (January 2023)PermalinkSensing urban soundscapes from street view imagery / Tianhong Zhao in Computers, Environment and Urban Systems, vol 99 (January 2023)PermalinkSimplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area / David Marín-García in Sustainable Cities and Society, vol 88 (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])PermalinkPermalinkA survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)PermalinkThe 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 height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning / Stefano Puliti in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkUnderstanding public perspectives on fracking in the United States using social media big data / Xi Gong in Annals of GIS, vol 29 n° 1 (January 2023)PermalinkUrban infrastructure expansion and artificial light pollution degrade coastal ecosystems, increasing natural-to-urban structural connectivity / Moisés A. Aguilera in Landscape and Urban Planning, vol 229 (January 2023)PermalinkVers une optimisation de la diffusion de l’information dans une ville intelligente / Malika Grim-Yefsah (2023)PermalinkVisual attention and recognition differences based on expertise in a map reading and memorability study / Merve Keskin in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)PermalinkPermalinkAssessing 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])PermalinkBayesian inference on the initiation phase of the 2014 Iquique, Chile, earthquake / Cédric Twardzik in Earth and planetary science letters, vol 600 (15 December 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)PermalinkAn automated approach for clipping geographic data before projection that maintains data integrity and minimizes distortion for virtually any projection method / Jim Graham in Cartographica, Vol 57 n° 4 (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)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)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)PermalinkA deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples / Ali Jamali in International journal of applied Earth observation and geoinformation, vol 115 (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])PermalinkExtracting built-up land area of airports in China using Sentinel-2 imagery through deep learning / Fanxuan Zeng in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkFrom data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping / Tian Lan in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)Permalink