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Titre : Structured learning of geospatial data Type de document : Thèse/HDR Auteurs : Loïc Landrieu , Auteur Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2023 Importance : 179 p. Format : 21 x 30 cm Note générale : Bibliographie
Habilitation à Diriger des Recherches délivrée par l'Université Gustave Eiffel, Spécialité "Sciences et Technologies de l'Information Géographique"Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] apprentissage automatique
[Termes IGN] carte agricole
[Termes IGN] graphe
[Termes IGN] lasergrammétrie
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelle
[Termes IGN] vision par ordinateurRésumé : (auteur) This manuscript presents an overview of my work in the field of geospatial machine learning, a rapidly growing interdisciplinary field that poses many methodological challenges and has a wide range of impactful applications. Throughout my research, I have focused on developing bespoke approaches that leverage the unique properties of geospatial data to create more efficient, precise, and parsimonious models. This manuscript is divided into four main chapters, each covering a different property of geospatial data structures that can be leveraged algorithmically. The first chapter presents a versatile mathematical framework formalizing the concept of spatial regularity with graphs. We propose an efficient algorithm that tackles a broad family of spatial problems and provides novel convergence guarantees and significant speed-ups compared to generic approaches. The second chapter introduces a deep learning method that extends the idea of exploiting graph regularity to the case of massive 3D point clouds. We simplify the task of large-scale semantic segmentation by formulating it as as a small graph labelling problem. Our compact models reach high precision at a fraction of the computational cost of other approaches. In the third chapter, we present a collection of methods designed to take advantage of the data structure inherited from 3D sensors. By considering the sensors’ structure, we develop powerful networks with state-of-the-art accuracy, latency, and robustness for various applications and data types. The last chapter dives into the real-life challenge of automated satellite time series analysis for crop mapping. Recognizing the difference between such data and standard formats used in computer vision, we propose novel and streamlined architectures that achieve unprecedented precision while remaining efficient and economical in memory and preprocessing. We also introduce the task of panoptic segmentation for satellite time series and an efficient architecture to solve this problem at scale. In summary, this manuscript argues that geospatial problems represent a challenging and impactful venue for evaluating the newest machine learning and vision methods and a fertile source of inspiration for designing novel approaches. Note de contenu : 1- Introduction
2- Exploiting graph regularity
3- Exploiting the spatial regularity of 3D data
4- Exploiting the structure of 3D sensors
5- Exploiting the structure of satellite time series
6- Perspectives
7- Curriculum vitaeNuméro de notice : 24107 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : HDR Note de thèse : HDR: Sciences et Technologies de l’Information Geographique : UGE : 2023 Organisme de stage : LASTIG (IGN) DOI : sans En ligne : https://hal.science/tel-04095452v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103248 The 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)
[article]
Titre : The cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Zhenfeng Shao, Auteur ; Akib Javed, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] automate cellulaire
[Termes IGN] carte thématique
[Termes IGN] classification semi-dirigée
[Termes IGN] détection de changement
[Termes IGN] données vectorielles
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] MNS SRTM
[Termes IGN] modèle dynamique
[Termes IGN] occupation du sol
[Termes IGN] Pakistan
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] utilisation du solRésumé : (auteur) Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation. Numéro de notice : A2023-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.14358/PERS.22-00102R2 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.14358/PERS.22-00102R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102357
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 1 (January 2023) . - pp 47 - 55[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2023011 SL Revue Centre de documentation Revues en salle Disponible Tree position estimation from TLS data using hough transform and robust least-squares circle fitting / Maja Michałowska in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)
[article]
Titre : Tree position estimation from TLS data using hough transform and robust least-squares circle fitting Type de document : Article/Communication Auteurs : Maja Michałowska, Auteur ; Jacek Rapinski, Auteur ; Joanna Janicka, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 100863 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] branche (arbre)
[Termes IGN] compensation par moindres carrés
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage du bruit
[Termes IGN] géolocalisation
[Termes IGN] méthode robuste
[Termes IGN] modèle numérique de terrain
[Termes IGN] Pologne
[Termes IGN] semis de points
[Termes IGN] transformation de HoughRésumé : (auteur) Forest management and planning require information regarding the current state of the forest. Remote sensing techniques allow to obtain geospatial data, also for the forestry sector. As one of the remote-sensed technologies datasets, Terrestrial Laser Scanning data is widely used to derive detailed information about tree and forest stand parameters. This article presents the combination of circular Hough transform, denoising procedure, and robust least-square circle fitting method to extract stem positions from Terrestrial Laser Scanning data. In the proposed approach, initial tree stems position was detected with circular Hough transform. Then, obtained results were denoised to exclude most non-tree trunk points and analyze three-dimensional data from laser scanning to find exact circular tree stems with a robust least-square circle fitting method. The developed algorithm is effective in obtaining the trees’ geodetic positions from laser scanning data. The results generated in this study can be used as basics for further automatic determination of tree characteristics, such as tree species, height, or crown range. In this study, 94.8% tree stems delineation was generated with a mean accuracy of 87.2%, 1.64 cm of root mean square error for stem position, and 1.15 cm for tree radius measured at ground level. The process conducted in this research can be used to detect other circle-shaped objects, such as lamps or power towers, for which obtaining dense Terrestrial Laser Scanning data is available. The detected positions of these objects can power the geographic information systems or thematic industry systems, where it is necessary to determine the geodetic object position results from legal regulations. Numéro de notice : A2023-018 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rsase.2022.100863 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102183
in Remote Sensing Applications: Society and Environment, RSASE > vol 29 (January 2023) . - n° 100863[article]Tree 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)
[article]
Titre : Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data Type de document : Article/Communication Auteurs : Ying Quan, Auteur ; Mingze Li, Auteur ; Yuanshuo Hao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2171706 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] espèce végétale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt secondaire
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed mapping of individual tree species. However, the in-depth mining and comprehending of the significance of features derived from high-resolution UAV data for tree species discrimination remains a difficult task. In this study, a state-of-the-art approach combining UAV-borne light detection and ranging (LiDAR) and hyperspectral was used to classify 11 common tree species in a typical natural secondary forest in Northeast China. First, comprehensive relevant structural and spectral features were extracted. Then, the most valuable feature sets were selected by using a hybrid approach combining correlation-based feature selection with the optimized recursive feature elimination algorithm. The random forest algorithm was used to assess feature importance and perform the classification. Finally, the robustness of features derived from point clouds with different structures and hyperspectral images with different spatial resolutions was tested. Our results showed that the best classification accuracy was obtained by combining LiDAR and hyperspectral data (75.7%) compared to that based on LiDAR (60.0%) and hyperspectral (64.8%) data alone. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential LiDAR and hyperspectral derived features, respectively. The selected features were robust in point clouds with a density not lower than 5% (~5 pts/m2) and a resolution not lower than 0.3 m in hyperspectral data. Although canopy surface features were slightly different from original LiDAR features, canopy surface information was also important for tree species classification. This study proved the capabilities of UAV-borne LiDAR and hyperspectral data in natural secondary forest tree species discrimination and the potential for this approach to be transferable to other study areas. Numéro de notice : A2023-194 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/15481603.2023.2171706 Date de publication en ligne : 03/02/2023 En ligne : https://doi.org/10.1080/15481603.2023.2171706 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103075
in GIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2171706[article]Understanding 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)
[article]
Titre : Understanding public perspectives on fracking in the United States using social media big data Type de document : Article/Communication Auteurs : Xi Gong, Auteur ; Yujian Lu, Auteur ; Daniel Beene, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 21 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse socio-économique
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] enquête sociologique
[Termes IGN] Etats-Unis
[Termes IGN] fracturation
[Termes IGN] hétérogénéité spatiale
[Termes IGN] régression géographiquement pondérée
[Termes IGN] TwitterRésumé : (auteur) People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics. Numéro de notice : A2023-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2022.2121856 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1080/19475683.2022.2121856 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102862
in Annals of GIS > vol 29 n° 1 (January 2023) . - pp 21 - 35[article]Urban 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)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])PermalinkGeospatial modelling of overlapping habitats for identification of tiger corridor networks in the Terai Arc landscape of India / Nupur Rautela 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)PermalinkInteractive effects of abiotic factors and biotic agents on Scots pine dieback: A multivariate modeling approach in southeast France / Jean Lemaire in Forest ecology and management, vol 526 (December-15 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)PermalinkAssessment of groundwater potential using multi-criteria decision analysis and geoelectrical surveying / Marzieh Shabani in Geo-spatial Information Science, vol 25 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)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)PermalinkBayesian hyperspectral image super-resolution in the presence of spectral variability / Fei Ye in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (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])PermalinkEffect of climate on cork-ring width and density of Quercus suber L. in Southern Portugal / Augusta Costa in Trees, vol 36 n° 6 (December 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)PermalinkFast calculation of gravitational effects using tesseroids with a polynomial density of arbitrary degree in depth / Fang Ouyang in Journal of geodesy, vol 96 n° 12 (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)PermalinkHigh-precision positioning using plane-constrained RTK method in urban environments / Chen Zhuang in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 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])PermalinkIdentifying spurious cycle slips based on iterative filtering under disturbed ionospheric conditions for undifferenced GNSS observations / Yan Xiang in Advances in space research, vol 70 n° 11 (December 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 geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia / Mitiku Badasa Moisa in Applied geomatics, vol 14 n° 4 (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)PermalinkMulti-frequency simulation of ionospheric scintillation using a phase-screen model / Fernando D. Nunes in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)PermalinkNavigation and Ionosphere Characterization Using High-Frequency Signals: A Performance Analysis / Yoav Baumgarten in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 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)PermalinkA novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)PermalinkOn study of the Earth topography correction for the GRACE surface mass estimation / Fan Yang 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)PermalinkReconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)PermalinkRobust modeling of GNSS orbit and clock error dynamics / Elisa Gallon in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 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 role of wood harvest from sustainably managed forests in the carbon cycle / Ernst Detlef Schulze in Annals of Forest Science, vol 79 n° 1 (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])PermalinkVertical deformation and residual altimeter systematic errors around continental Australia inferred from a Kalman-based approach / Mohammad-Hadi Rezvani in Journal of geodesy, vol 96 n° 12 (December 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 advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations / Kai Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)PermalinkAn improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao 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)PermalinkEvaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments / Sercan Gülci in ISPRS International journal of geo-information, vol 11 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)PermalinkA fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation / Xiaoguo Guan in Survey review, vol 54 n° 387 (November 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)PermalinkGeographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid / Zhen Dai in International journal of geographical information science IJGIS, vol 36 n° 11 (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)PermalinkA high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation / Fang Gao in Computers & geosciences, vol 168 (November 2022)PermalinkHuman mobility and COVID-19 transmission: a systematic review and future directions / Mengxi Zhang in Annals of GIS, vol 28 n° 4 (November 2022)PermalinkImproving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height / Mosbeh R. Kaloop in Survey review, vol 54 n° 387 (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)PermalinkIntegrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability / Benjamin T. Gutierrez in Earth and space science, vol 9 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)PermalinkModelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information / Shaohui Zhang in International journal of applied Earth observation and geoinformation, vol 114 (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)PermalinkA new partial ambiguity resolution method based on modified solution separation and GNSS epoch-differencing / Yang Jiang in Journal of geodesy, vol 96 n° 11 (November 2022)PermalinkPPP–RTK theory for varying transmitter frequencies with satellite and terrestrial positioning applications / Peter J.G. Teunissen in Journal of geodesy, vol 96 n° 11 (November 2022)PermalinkA robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL) / Anchal Kumawat in The Visual Computer, vol 38 n° 11 (November 2022)PermalinkThe employment of quasi-hexagonal grids in spherical harmonic analysis and synthesis for the earth's gravity field / Xingxing Li in Journal of geodesy, vol 96 n° 11 (November 2022)PermalinkTidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran / Kourosh Shahryari Nia in Marine geodesy, vol 45 n° 6 (November 2022)PermalinkDriving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])PermalinkComparison of change and static state as the dependent variable for modeling urban growth / Yongjiu Feng in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkA deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkFlash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkA model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe / Bingbin Wen in Forest ecology and management, vol 522 (October-15 2022)PermalinkAnalysis of the spatial range of service and accessibility of hospitals designated for coronavirus disease 2019 in Yunnan Province, China / Liangting Zheng in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkLand use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkModelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkRaster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkAn estimation method to reduce complete and partial nonresponse bias in forest inventory / James A. Westfall in European Journal of Forest Research, vol 141 n° 5 (October 2022)PermalinkApplication of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkAssessing logging residues availability for energy production by using forest management plans data and geographic information system (GIS) / Luca Nonini in European Journal of Forest Research, vol 141 n° 5 (October 2022)PermalinkAugmented reality for scene text recognition, visualization and reading to assist visually impaired people / Imene Ouali in Procedia Computer Science, vol 207 (2022)Permalink