Descripteur
Documents disponibles dans cette catégorie (8808)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Development and assessment of rainwater harvesting suitability map using analytical hierarchy process, GIS and RS techniques / Khaled S. Balkhair in Geocarto international, vol 36 n° 4 ([01/03/2021])
![]()
[article]
Titre : Development and assessment of rainwater harvesting suitability map using analytical hierarchy process, GIS and RS techniques Type de document : Article/Communication Auteurs : Khaled S. Balkhair, Auteur ; Khalil Ur Rahman, Auteur Année de publication : 2021 Article en page(s) : pp 421 - 448 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] aide à la décision
[Termes IGN] analyse de sensibilité
[Termes IGN] Arabie Saoudite
[Termes IGN] bassin hydrographique
[Termes IGN] carte hydrographique
[Termes IGN] eau pluviale
[Termes IGN] écoulement des eaux
[Termes IGN] gestion de l'eau
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] système d'information géographiqueRésumé : (auteur) Rainwater harvesting (RWH), which is the collection and storage of rainwater for multiple purposes, is gaining recognition in water supply issues. Selection of harvesting sites is the most critical factor in RWH projects. The objective of this study is to develop a suitability map of RWH sites for a basin in Saudi Arabia. The method used, constitute the identification and assigning weights to criteria, and generation of suitability map using Analytical Hierarchy Process (AHP). Eight appropriate criteria were considered. Results showed that excellent and good sites covered about 40.6% of the total available sites. Sensitivity analysis showed that the curve number (CN), slope, rainfall and soil were the most influential criteria. The maximum increase in the percentage area of excellent sites was 92% while good and moderate classes decreased by 43 and 53%, respectively. The developed suitability maps provide useful information to the decision maker for use in water management. Numéro de notice : A2021-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.160859 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1608591 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97082
in Geocarto international > vol 36 n° 4 [01/03/2021] . - pp 421 - 448[article]Extraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image / Wenfu Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)
![]()
[article]
Titre : Extraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image Type de document : Article/Communication Auteurs : Wenfu Wu, Auteur ; Jiahua Teng, Auteur ; Qimin Cheng, Auteur ; Songjing Guo, Auteur Année de publication : 2021 Article en page(s) : pp 161-170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] chatoiement
[Termes IGN] cohérence (physique)
[Termes IGN] cohérence temporelle
[Termes IGN] extraction automatique
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] segmentation d'image
[Termes IGN] segmentation multi-échelle
[Termes IGN] série temporelle
[Termes IGN] surface imperméableRésumé : (Auteur) The continuous increasing of impervious surface (IS) hinders the sustainable development of cities. Using optical images alone to extract IS is usually limited by weather, which obliges us to develop new data sources. The obvious differences between natural and artificial targets in interferometric synthetic-aperture radar coherence images have attracted the attention of researchers. A few studies have attempted to use coherence images to extract IS—mostly single-temporal coherence images, which are affected by de-coherence factors. And due to speckle, the results are rather fragmented. In this study, we used time-series coherence images and introduced multi-resolution segmentation as a postprocessing step to extract IS. From our experiments, the results from the proposed method were more complete and achieved considerable accuracy, confirming the potential of time-series coherence images for extracting IS. Numéro de notice : A2021-240 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.3.161 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.14358/PERS.87.3.161 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97264
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 3 (March 2021) . - pp 161-170[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021031 SL Revue Centre de documentation Revues en salle Disponible Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
![]()
[article]
Titre : Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships Type de document : Article/Communication Auteurs : Sensen Wu, Auteur ; Zhongyi Wang, Auteur ; Zhenhong Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 582 - 608 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal
[Termes IGN] espace-temps
[Termes IGN] estimation par noyau
[Termes IGN] littoral
[Termes IGN] modélisation environnementale
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression linéaireRésumé : (auteur) Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships. Although these methods have been widely used in geographical modeling and spatiotemporal analysis, they face challenges in adequately expressing space-time proximity and constructing a kernel with optimal weights. This probably results in an insufficient estimation of spatiotemporal non-stationarity. To address complex non-linear interactions between time and space, a spatiotemporal proximity neural network (STPNN) is proposed in this paper to accurately generate space-time distance. A geographically and temporally neural network weighted regression (GTNNWR) model that extends geographically neural network weighted regression (GNNWR) with the proposed STPNN is then developed to effectively model spatiotemporal non-stationary relationships. To examine its performance, we conducted two case studies of simulated datasets and environmental modeling in coastal areas of Zhejiang, China. The GTNNWR model was fully evaluated by comparing with ordinary linear regression (OLR), GWR, GNNWR, and GTWR models. The results demonstrated that GTNNWR not only achieved the best fitting and prediction performance but also exactly quantified spatiotemporal non-stationary relationships. Further, GTNNWR has the potential to handle complex spatiotemporal non-stationarity in various geographical processes and environmental phenomena. Numéro de notice : A2021-167 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1775836 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1080/13658816.2020.1775836 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97102
in International journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 582 - 608[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021031 SL Revue Centre de documentation Revues en salle Disponible A graph-based semi-supervised approach to classification learning in digital geographies / Pengyuan Liu in Computers, Environment and Urban Systems, vol 86 (March 2021)
![]()
[article]
Titre : A graph-based semi-supervised approach to classification learning in digital geographies Type de document : Article/Communication Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2021 Article en page(s) : n° 101583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage profond
[Termes IGN] approche participative
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification semi-dirigée
[Termes IGN] données spatiotemporelles
[Termes IGN] partage de données localisées
[Termes IGN] prise en compte du contexte
[Termes IGN] réseau social
[Termes IGN] segmentation sémantique
[Termes IGN] Time-geographyRésumé : (auteur) As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks. Numéro de notice : A2021-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101583 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101583 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96608
in Computers, Environment and Urban Systems > vol 86 (March 2021) . - n° 101583[article]Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
![]()
[article]
Titre : Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Type de document : Article/Communication Auteurs : Xiongfeng Yan, Auteur ; Tinghua Ai, Auteur ; Min Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 490 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] codage
[Termes IGN] données vectorielles
[Termes IGN] graphe
[Termes IGN] mesure géométrique
[Termes IGN] modélisation du bâti
[Termes IGN] représentation cognitive
[Termes IGN] représentation spatialeRésumé : (auteur) The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching. Numéro de notice : A2021-166 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768260 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97100
in International journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 490 - 512[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021031 SL Revue Centre de documentation Revues en salle Disponible Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates / Franz Schug in Plos one, vol 16 n° 3 (March 2021)
PermalinkImpact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
PermalinkImproving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])
PermalinkIntegration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)
PermalinkIntegrity investigation of global ionospheric TEC maps for high-precision positioning / Jiaojiao Zhao in Journal of geodesy, vol 95 n° 3 (March 2021)
PermalinkLandslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)
PermalinkLearning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
PermalinkLightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios / Xiao Ke in Machine Vision and Applications, vol 32 n° 2 (March 2021)
PermalinkMachine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)
PermalinkMinimum-error world map projections defined by polydimensional meshes / Justin H. Kunimune in International journal of cartography, vol 7 n° 1 (March 2021)
PermalinkModélisation des délais ionosphériques appliquée au traitement PPP-RTK centimétrique avec ambiguïtés entières de phase / Camille Parra in XYZ, n° 166 (mars 2021)
PermalinkPermalinkON GLONASS pseudo-range inter-frequency bias solution with ionospheric delay modeling and the undifferenced uncombined PPP / Zheng Zhang in Journal of geodesy, vol 95 n° 3 (March 2021)
PermalinkOn the polarimetric variable improvement via alignment of subarray channels in PPAR using weather returns / Igor R. Ivić in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
PermalinkOntology-based semantic conceptualisation of historical built heritage to generate parametric structured models from point clouds / Elisabetta Colucci in Applied sciences, vol 11 n° 6 (March 2021)
PermalinkPan-sharpening via multiscale dynamic convolutional neural network / Jianwen Hu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
PermalinkPBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery / Xian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
PermalinkProgressive TIN densification with connection analysis for urban Lidar data / Tao Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)
PermalinkRecent increase in European forest harvests as based on area estimates (Ceccherini et al. 2020a) not confirmed in the French case / Nicolas Picard in Annals of Forest Science, vol 78 n° 1 (March 2021)
PermalinkRecognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)
PermalinkRobust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
PermalinkSaline-soil deformation extraction based on an improved time-series InSAR approach / Wei Xiang in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)
PermalinkSimple method for identification of forest windthrows from Sentinel-1 SAR data incorporating PCA / Milan Lazecky in Procedia Computer Science, vol 181 (2021)
PermalinkSpace-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
PermalinkSuitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)
PermalinkSusceptibilité aux glissements de terrain dans la ville d’Al Hoceima et sa périphérie : application de la méthode de la théorie de l’évidence / Taoufik Byou in Geomatica, vol 75 n° 1 (Mars 2021)
PermalinkToward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)
PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)
PermalinkUsing geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs / Roholah Yazdan in Geomatica, vol 75 n° 1 (Mars 2021)
PermalinkAssessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([15/02/2021])
PermalinkModelling potential density of natural regeneration of European oak species (Quercus robur L., Quercus petraea (Matt.) Liebl.) depending on the distance to the potential seed source: Methodological approach for modelling dispersal from inventory data at forest enterprise level / Maximilian Axer in Forest ecology and management, vol 482 ([15/02/2021])
PermalinkActivity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)
PermalinkAgricultural land partitioning model based on irrigation efficiency using a multi‐objective artificial bee colony algorithm / Mehrdad Bijandi in Transactions in GIS, Vol 25 n° 1 (February 2021)
PermalinkAn anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
PermalinkAn ecological approach to climate change-informed tree species selection for reforestation / William H. MacKenzie in Forest ecology and management, vol 481 (February 2021)
PermalinkAn improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments / Zohreh Masoumi in Geocarto international, vol 36 n° 2 ([01/02/2021])
PermalinkAn improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards / Geraldo Moura Ramos Filho in Natural Hazards, Vol 105 n° 3 (February 2021)
PermalinkAn integrated method for DEM simplification with terrain structural features and smooth morphology preserved / Wenhao Yu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
PermalinkAssessment of mass-induced sea level variability in the Tropical Indian Ocean based on GRACE and altimeter observations / Shiva Shankar Manche in Journal of geodesy, vol 95 n° 2 (February 2021)
PermalinkA comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
PermalinkComprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1 / Matthias Schlögl in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
PermalinkCorrentropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
PermalinkCrop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)
PermalinkCrowdsourcing without data bias: Building a quality assurance system for air pollution symptom mapping / Marta Samulowska in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)
PermalinkCurved buildings reconstruction from airborne LiDAR data by matching and deforming geometric primitives / Jingwei Song in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
Permalink