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Termes descripteurs IGN > mathématiques > statistique mathématique > analyse de données > analyse multivariée > analyse factorielle > analyse de groupement
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A heuristic approach to the generalization of complex building groups in urban villages / Wenhao Yu in Geocarto international, vol 36 n° 2 ([01/02/2021])
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Titre : A heuristic approach to the generalization of complex building groups in urban villages Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Qi Zhou, Auteur ; Rong Zhao, Auteur Année de publication : 2021 Article en page(s) : pp 155 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] empreinte
[Termes descripteurs IGN] généralisation du bâti
[Termes descripteurs IGN] méthode heuristique
[Termes descripteurs IGN] représentation multiple
[Termes descripteurs IGN] triangulation de Delaunay
[Termes descripteurs IGN] zone urbaine
[Vedettes matières IGN] GénéralisationRésumé : (auteur) The generalization of building footprints acts as the basis of multi-scale mapping. Most of the previous studies focus on the generalization of regular building clusters within a wide neighbourhood, but only few has concerned about the generalization of cluttered building clusters within the narrow space such as urban village. The buildings in urban villages show special characteristics in terms of individual properties and group properties, and thus their map generalization processes are often limited. This study proposes a framework to generalize the cluttered building clusters that allows for multi-scale mapping. It first adopts a heuristic method to group adjacent buildings based on the Delaunay triangulation model and then aggregates and simplifies each building group separately. Given that the aggregated buildings in urban villages often show cluttered alignments, our method further trims the jagged boundaries of building footprints by extracting the gap space between neighbouring buildings from the Delaunay triangulation model. Numéro de notice : A2021-084 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.159046 date de publication en ligne : 25/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1590463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96843
in Geocarto international > vol 36 n° 2 [01/02/2021] . - pp 155 - 179[article]Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
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Titre : Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis Type de document : Article/Communication Auteurs : Marta Sapena, Auteur ; Luis Angel Ruiz, Auteur Année de publication : 2021 Article en page(s) : pp 375 - 396 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] analyse discriminante
[Termes descripteurs IGN] analyse spatio-temporelle
[Termes descripteurs IGN] carte d'occupation du sol
[Termes descripteurs IGN] carte d'utilisation du sol
[Termes descripteurs IGN] changement d'occupation du sol
[Termes descripteurs IGN] croissance urbaine
[Termes descripteurs IGN] distance euclidienne
[Termes descripteurs IGN] modèle de croissance
[Termes descripteurs IGN] pondérationRésumé : (auteur) The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas. Numéro de notice : A2021-040 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1817463 date de publication en ligne : 08/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1817463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96752
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 375 - 396[article]Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs / Yang Bai in Computers & geosciences, vol 146 (January 2021)
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Titre : Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs Type de document : Article/Communication Auteurs : Yang Bai, Auteur ; Maojin Tan, Auteur Année de publication : 2021 Article en page(s) : n° 104626 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] classification floue
[Termes descripteurs IGN] classification par réseau neuronal
[Termes descripteurs IGN] puits de carbone
[Termes descripteurs IGN] régression linéaire
[Termes descripteurs IGN] schisteRésumé : (auteur) The total organic carbon (TOC) content is of great significance to reflect the hydrocarbon-generation potential in shale reservoirs. The well logs were always used to predict the TOC content, but some linear regression methods do not match well with complex data. The neural network method can improve prediction accuracy, but it always generates unstable prediction models. A static committee machine can reduce errors and uncertainties by combining multiple learners, but the weight of integrating learners is difficult to determine. Therefore, a dynamic committee machine with fuzzy-c-means clustering (DCMF) was proposed to predict the TOC content. Experts in the DCMF include Elman neural network, extreme learning machine, and generalized regression neural network. The fuzzy-c-means clustering algorithm was used as the gate network to perform subtasks decomposition and weights calculation based on input data. The subtasks were used to train more adaptive TOC content prediction models, and the weights were transferred to the combiner to integrate all experts’ outputs into final results. The DCMF was applied in two wells located in the Jiumenchong formation in the Qiannan depression, China. The TOC prediction results using the DCMF method are more accurate than the linear regression method, three individual intelligent algorithms, and the static committee machine. The DCMF also provides a new method for weight calculation by mining potential information of input data. Numéro de notice : A2021-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2020.104626 date de publication en ligne : 17/10/2020 En ligne : https://doi.org/10.1016/j.cageo.2020.104626 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96512
in Computers & geosciences > vol 146 (January 2021) . - n° 104626[article]Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
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Titre : Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation Type de document : Article/Communication Auteurs : George Grekousis, Auteur Année de publication : 2021 Article en page(s) : pp 152 - 174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] classification floue
[Termes descripteurs IGN] données démographiques
[Termes descripteurs IGN] New York (Etats-Unis ; ville)
[Termes descripteurs IGN] optimisation par essaim de particules
[Termes descripteurs IGN] pondération
[Termes descripteurs IGN] régression géographiquement pondérée
[Termes descripteurs IGN] santé
[Termes descripteurs IGN] segmentation
[Termes descripteurs IGN] voisinage (topologie)Résumé : (auteur) Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings. Numéro de notice : A2021-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808221 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808221 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96525
in International journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 152 - 174[article]Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data / Yi Qiang in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
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Titre : Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data Type de document : Article/Communication Auteurs : Yi Qiang, Auteur ; Jinwen Xu, Auteur Année de publication : 2020 Article en page(s) : pp 2434 - 2450 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] étude empirique
[Termes descripteurs IGN] Google Maps
[Termes descripteurs IGN] Ohio (Etats-Unis)
[Termes descripteurs IGN] participation du public
[Termes descripteurs IGN] réseau routier
[Termes descripteurs IGN] résilience
[Termes descripteurs IGN] risque naturel
[Termes descripteurs IGN] trafic routierRésumé : (auteur) Climate change and natural hazards pose great threats to road transport systems which are ‘lifelines’ of human society. However, there is generally a lack of empirical data and approaches for assessing resilience of road networks in real hazard events. This study introduces an empirical approach to evaluate road network resilience using crowdsourced traffic data in Google Maps. Based on the conceptualization of resilience and the Hansen accessibility index, resilience of road network is measured from accumulated accessibility reduction over time during a hazard. The utility of this approach is demonstrated in a case study of the Cleveland metropolitan area (Ohio) in Winter Storm Harper. The results reveal strong spatial variations of the disturbance and recovery rate of road network performance during the hazard. The major findings of the case study are: (1) longer distance travels have higher increasing ratios of travel time during the hazard; (2) communities with low accessibility at the normal condition have lower road network resilience; (3) spatial clusters of low resilience are identified, including communities with low socio-economic capacities. The introduced approach provides ground-truth validation for existing quantitative models and supports disaster management and transportation planning to reduce hazard impacts on road network. Numéro de notice : A2020-691 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1694681 date de publication en ligne : 25/11/2020 En ligne : https://doi.org/10.1080/13658816.2019.1694681 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96229
in International journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2434 - 2450[article]Group diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
PermalinkSTME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)
PermalinkA comparison of neighbourhood relations based on ordinary Delaunay diagrams and area Delaunay diagrams: an application to define the neighbourhood relations of buildings / Hiroyuki Usui in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)
PermalinkA multi-scale representation model of polyline based on head/tail breaks / Pengcheng Liu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)
PermalinkCoupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)
PermalinkA framework for group converging pattern mining using spatiotemporal trajectories / Bin Zhao in Geoinformatica [en ligne], vol 24 n° 4 (October 2020)
PermalinkNetwork-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)
PermalinkAn overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
PermalinkComprehensive decision-strategy space exploration for efficient territorial planning strategies / Olivier Billaud in Computers, Environment and Urban Systems, vol 83 (September 2020)
PermalinkMining regional patterns of land use with adaptive adjacent criteria / Xinmeng Tu in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
PermalinkPrecise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkExploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkReestimating a minimum acceptable geocoding hit rate for conducting a spatial analysis / Alvaro Briz-Redon in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)
PermalinkUnsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)
PermalinkExtracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation / Shuhui Gong in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)
PermalinkHyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance / Bing Tu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
PermalinkUsing GIS for disease mapping and clustering in Jeddah, Saudi Arabia / Abdulkader Murad in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
PermalinkA framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)
PermalinkPotential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests / Sadeepa Jayathunga in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)
PermalinkUnsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)
PermalinkA reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n° 5 (October 2019)
PermalinkSpatially constrained regionalization with multilayer perceptron / Michael Govorov in Transactions in GIS, Vol 23 n° 5 (October 2019)
PermalinkGenetic diversity and structure of Silver fir (Abies alba Mill.) at the south-eastern limit of its distribution range / Maria Teodosiu in Annals of forest research, vol 62 n° 2 (June - December 2019)
PermalinkPiecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W5 (May 2019)
PermalinkExploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)
PermalinkA natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements / Yingjie Hu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)
PermalinkUsing LiDAR to develop high-resolution reference models of forest structure and spatial pattern / Haley L. Wiggins in Forest ecology and management, vol 434 (28 February 2019)
PermalinkDetecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization / Si Song in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)
PermalinkIntegration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)
PermalinkSimultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)
PermalinkOn the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)
PermalinkSpatial association between regionalizations using the information-theoretical V-measure / Jakub Nowosad in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)
PermalinkScalable individual tree delineation in 3D point clouds / Jinhu Wang in Photogrammetric record, vol 33 n° 163 (September 2018)
PermalinkUsing interactions and dynamics for mining groups of moving objects from trajectory data / Corrado Loglisci in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)
PermalinkA simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data / Biao He in ISPRS International journal of geo-information, vol 7 n° 6 (June 2018)
PermalinkA geometric correspondence feature based-mismatch removal in vision based-mapping and navigation / Zeyu Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)
PermalinkAutomatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning / Tim Ritter in Forests, vol 8 n° 8 (August 2017)
PermalinkA novel semisupervised active-learning algorithm for hyperspectral image classification / Zengmao Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
PermalinkConstrained clustering by constraint programming / Thi-Bich-Hanh Dao in Artificial intelligence, vol 244 (March 2017)
PermalinkPermalinkAirborne lidar estimation of aboveground forest biomass in the absence of field inventory / António Ferraz in Remote sensing, vol 8 n° 8 (August 2016)
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PermalinkAutomatic extraction of road networks from GPS traces / Jia Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)
PermalinkLand-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping / L. Drăguț in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)
PermalinkClassified and clustered data constellation: An efficient approach of 3D urban data management / Suhaibah Azri in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)
PermalinkUniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
PermalinkContributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques / Saadallah El Asmar (2016)
PermalinkEuropean handbook of crowdsourced geographic information, ch. 12. Gaining knowledge from georeferenced social media data with visual analytics / Gennady Andrienko (2016)
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PermalinkVegetation classification and biogeography of European floodplain forests and alder carrs / Jan Douda in Applied Vegetation Science, vol 19 n° 1 (January 2016)
PermalinkAPFiLoc: An Infrastructure-Free Indoor Localization method fusing smartphone inertial sensors, landmarks and map information / Jianga Shang in Sensors, vol 15 n° 10 (October 2015)
PermalinkPolygonal clustering analysis using multilevel graph-partition / Wanyi Wang in Transactions in GIS, vol 19 n° 5 (October 2015)
PermalinkCharacterizing the heterogeneity of the OpenStreetMap data and community / Ding Ma in ISPRS International journal of geo-information, vol 4 n°2 (June 2015)
PermalinkPoints of interest recommendation from GPS trajectories / Yaqiong Liu in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)
PermalinkRegionalization of youth and adolescent weight metrics for the continental United States using contiguity-constrained clustering and partitioning / Samuel Adu-Prah in Cartographica, vol 50 n° 2 (Summer 2015)
PermalinkCo-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data / Xiaojing Wu in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)
PermalinkMapping large spatial flow data with hierarchical clustering / Xi Zhu in Transactions in GIS, vol 18 n° 3 (June 2014)
PermalinkCombining Geo-SOM and hierarchical clustering to explore geospatial data / Chen-Chieh Feng in Transactions in GIS, vol 18 n° 1 (February 2014)
PermalinkAbstracting geographic information in a data rich world, ch. 3. Modelling geographic relationships in automated environments / Guillaume Touya (2014)
PermalinkScale-specific automated line simplification by vertex clustering on a hexagonal tessellation / Paulo Raposo in Cartography and Geographic Information Science, vol 40 n° 5 (November 2013)
PermalinkFootprint generation using fuzzy-neighborhood clustering / Jonathon K. Parker in Geoinformatica, vol 17 n° 2 (April 2013)
PermalinkSpatio-temporal polygonal clustering with space and time as first-class citizens / Deepti Joshi in Geoinformatica, vol 17 n° 2 (April 2013)
PermalinkTrajectories of moving objects on a network: detection of similarities, visualization of relations, and classification of trajectories / Yukio Sadahiro in Transactions in GIS, vol 17 n° 1 (February 2013)
PermalinkSemisupervised learning of hyperspectral data with unknown land-cover classes / G. Jun in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
PermalinkCluster recognition in spatial-temporal sequences: the case of forest fires / C. Vega Orozco in Geoinformatica, vol 15 n° 4 (October 2012)
PermalinkSemisupervised classification of remote sensing images with active queries / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)
PermalinkHyperspectral band clustering and band selection for urban land cover classification / H. Su in Geocarto international, vol 27 n° 5 (August 2012)
PermalinkMemory-based cluster sampling for remote sensing image classification / Michele Volpi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
PermalinkSatellite image time series analysis under time warping / F. Petitjean in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
PermalinkDiscovering spatial patterns in origin-destination mobility data / D. Guo in Transactions in GIS, vol 16 n° 3 (June 2012)
PermalinkEfficient parallel algorithm for pixel classification in remote sensing imagery / U. Maulik in Geoinformatica, vol 16 n° 2 (April 2012)
PermalinkFuzzy analysis for modeling regional delineation and development: The case of the Sardinian mining geopark / G. Manca in Transactions in GIS, vol 16 n° 1 (February 2012)
PermalinkClustering of detected changes in high-resolution satellite imagery using a stabilized competitive agglomeration algorithm / O. Sjahputera in IEEE Transactions on geoscience and remote sensing, vol 49 n° 12 Tome 1 (December 2011)
PermalinkComputational method for the point cluster analysis on networks / K. Sugihara in Geoinformatica, vol 15 n° 1 (January 2011)
PermalinkA framework for regional association rule mining and scoping in spatial datasets / W. Ding in Geoinformatica, vol 15 n° 1 (January 2011)
PermalinkUsing clustering methods in geospatial information systems / X. Wang in Geomatica, vol 64 n° 3 (September 2010)
PermalinkSegmentation and reconstruction of polyhedral building roofs from aerial lidar points clouds / A. Sampath in IEEE Transactions on geoscience and remote sensing, vol 48 n° 3 Tome 2 (March 2010)
PermalinkAutomatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm / T. Oyana in Geocarto international, vol 25 n° 1 (February 2010)
PermalinkUsing building permits to monitor disaster recovery: a spatio-temporal case study of coastal Mississipi following hurricane Katrina / J. Stevenson in Cartography and Geographic Information Science, vol 37 n° 1 (January 2010)
PermalinkResearch on urban influence domains in China / S. Liang in International journal of geographical information science IJGIS, vol 23 n°11-12 (november 2009)
PermalinkStylistic diversity in European state 1: 50 000 topographic maps / Alexander J. Kent in Cartographic journal (the), vol 46 n° 3 (August 2009)
PermalinkOptimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis / L. Su in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)
PermalinkPan-European forest/non forest mapping with Landsat ETM+ and Corine Land Cover 2000 data / A. Pekkarinen in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 2 (March - April 2009)
PermalinkAn automated method of scale selection and sheet configuration for multiple sheet census maps with Insets / W.G. Thompson in Cartography and Geographic Information Science, vol 36 n° 1 (January 2009)
PermalinkDetection of multi-scale clusters in network space / S. Shiode in International journal of geographical information science IJGIS, vol 23 n° 1-2 (january 2009)
PermalinkExtending marine GIS capabilities: 3D representation of fish aggregations using Delaunay tetrahedralisation and Alpha shapes / V. Carette in Geomatica, vol 62 n° 4 (December 2008)
PermalinkAn assessment of the effects of cell size on AGNPS modeling of watershed runoff / S.S. Wu in Cartography and Geographic Information Science, vol 35 n° 4 (October 2008)
PermalinkClassification fonctionnelle des Public Participation GIS / A. Turkucu in Revue internationale de géomatique, vol 18 n° 4 (septembre – novembre 2008)
PermalinkLand cover classification of the North China Plain using MODIS-EVI time series / Z. Xia in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 4 (July - August 2008)
PermalinkPermalinkSupporting the process of exploring and interpreting space-time multivariate patterns: the visual inquiry toolkit / J. Chen in Cartography and Geographic Information Science, vol 35 n° 1 (January 2008)
PermalinkSpatial aspects of MRSA epidemiology: a case study using stochastic simulation, kernel estimation and SaTScan / Lucy Bastin in International journal of geographical information science IJGIS, vol 21 n° 6-7 (july 2007)
PermalinkEvaluating the uncertainty caused by Post Office Box addresses in environmental health studies: A restricted Monte Carlo approach / X. Shi in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)
PermalinkNET-DBSCAN: clustering the nodes of a dynamic linear network / Emmanuel Stefanakis in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)
PermalinkDEM resolution dependencies of terrain attributes across a landscape / Y. Deng in International journal of geographical information science IJGIS, vol 21 n° 1-2 (january 2007)
PermalinkPermalinkFast cluster polygonization and its applications in data-rich environments / I. Lee in Geoinformatica, vol 10 n° 4 (December 2006)
PermalinkAgent-based modelling of shifting cultivation field patterns, Vietnam / M.R. Jepsen in International journal of geographical information science IJGIS, vol 20 n° 9 (october 2006)
PermalinkPopulation landscape: a geometric approach to studying spatial patterns of the US urban hierarchy / L. Mu in International journal of geographical information science IJGIS, vol 20 n° 6 (july 2006)
PermalinkSegmentation of airborne laser scanning data using a slope adaptative neighbourhood / S. Filin in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 2 (April 2006)
PermalinkCAp 2006, 8e conférence francophone sur l'apprentissage automatique, 22 - 24 mai 2006, Trégastel, France / Laurent Miclet (2006)
PermalinkPermalinkReconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)
PermalinkRemote sensing image thresholding methods for determining landslide activity / P.L. Rosin in International Journal of Remote Sensing IJRS, vol 26 n° 6 (March 2005)
PermalinkSatellite image classification using genetically guided fuzzy clustering with spatial information / S. Bandyopadhyay in International Journal of Remote Sensing IJRS, vol 26 n° 3 (February 2005)
PermalinkFiltering airborne Laser scanner data: a wavelet-based clustering method / T. Thuy in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 11 (November 2004)
PermalinkClustering with obstacles for geographical data mining / V. Estivill-Castro in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 1-2 (August 2004 - April 2005)
PermalinkA land cover classification product over France at 1 km resolution using Spot4-Vegetation data / K.S. Han in Remote sensing of environment, vol 92 n° 1 (15 July 2004)
PermalinkIntra-urban location and clustering of road accidents using GIS: a Belgian example / T. Steenberghen in International journal of geographical information science IJGIS, vol 18 n° 2 (march 2004)
PermalinkApproaches to fractional land cover and continuous field mapping: a comparative assessment over the BOREAS [BOReal Ecosystem Atmosphere Study] study region / R. Fernandes in Remote sensing of environment, vol 89 n° 2 (30/01/2004)
PermalinkEvaluation of speckle noise MAP filtering algorithms applied to SAR images / F.N.S. Medeiros in International Journal of Remote Sensing IJRS, vol 24 n° 24 (December 2003)
PermalinkA comparison of vector and raster GIS methods for calculating landscape metrics used in environmental assessments / T.G. Wade in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 12 (December 2003)
PermalinkPrincipal-components-based display strategy for spectral imagery / J.S. Tyo in IEEE Transactions on geoscience and remote sensing, vol 41 n° 3 (March 2003)
PermalinkLandscape dynamics of the spread of sudden oak death / M. Kelly in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 10 (October 2002)
PermalinkLarge-area land-cover mapping through scene-based classification compositing / B. Guindon in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 6 (June 2002)
PermalinkA synergic automatic clustering technique (syneract) for multispectral image analysis / K.Y. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 1 (January 2002)
PermalinkDetection of urban structures in SAR images by robust fuzzy clustering algorithms: the example of street tracking / F. Dell'acqua in IEEE Transactions on geoscience and remote sensing, vol 39 n° 10 (October 2001)
PermalinkClustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery / C.C. Funk in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)
PermalinkPermalinkSDH 98 Proceedings 8th international symposium on spatial data handling, Vancouver, July 11 - 15, 1998 / Thomas K. Poiker (1998)
PermalinkNo fuzzy creep! A clustering algorithm for controlling arbitrary node movement / Francis Harvey (07/04/1997)
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PermalinkPermalinkKlassifizierung von multispektralen Bildern unter Verwendung der Clusterformen im Merkmalsraum / M. Zahn (1996)
PermalinkMeasurement, characterization and classification for automated line feature generalization / Corinne Plazanet (27/02/1995)
PermalinkCluster analysis of pine crown foliage patterns aid identification of mountain pine beetle current-attack / P.A. Murtha in Photogrammetric Engineering & Remote Sensing, PERS, vol 55 n° 1 (january 1989)
PermalinkUsing cluster analysis to improve the selection of training statistics in classifying remotely sensed data / E. Chuvieco in Photogrammetric Engineering & Remote Sensing, PERS, vol 54 n° 9 (september 1988)
PermalinkAdaptive clustering algorithm / L. O'malley in Journal research and development, vol 29 n° 1 (01/01/1985)
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