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Spatial autoregressive model for population estimation at the census block level using lidar-derived building volume information / F. Qiu in Cartography and Geographic Information Science, vol 37 n° 3 (July 2010)  

Public [article]  inCartography and Geographic Information Science > vol 37 n° 3 (July 2010) . - pp 239 - 257 Titre : Spatial autoregressive model for population estimation at the census block level using lidar-derived building volume information Type de document : Article/Communication Auteurs : F. Qiu, Auteur ; H. Sridharan, Auteur ; Y. Chun, Auteur Année de publication : 2010 Article en page(s) : pp 239 - 257 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] auto-régression [Termes IGN] bati [Termes IGN] densité de population [Termes IGN] données lidar [Termes IGN] recensement démographique Résumé : (Auteur) The collection of population by census is laborious, time consuming and expensive, and often only available at limited temporal and spatial scales. Remote sensing based population estimation has been employed as a viable alternative for providing population estimates based on indicators that make use of two-dimensional areal information of buildings or one-dimensional length information of roads The recent advancement of LIDAR remote sensing provides the opportunity to add the third dimension of height information into the modeling of population distribution. This study explores the use of building volumes derived from LIDAR as a population indicator. Our study shows the volume-based model consistently outperforms area and length-based models at the census block level. Additionally, the study examines the impact of spatial autocorrelation, the presence of which violates the independence assumption of the traditional OLS models. To address this problem, a spatial autoregressive model is employed to account for the spatial autocorrelation in the regression residuals. By incorporating the spatial pattern, the volume-based spatial error model achieves a goodness of fit (R2) of 85 percent, with a significant improvement in model performance and estimation accuracies in comparison with its OLS counterpart. The study confirms building volume as a more valuable indicator and estimator for block level population distribution, especially if an appropriate spatial autoregressive model is adopted. Numéro de notice : A2010-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1559/152304010792194949 En ligne : https://doi.org/10.1559/152304010792194949 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30552 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2010031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Neuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 10 (October 2008) . - pp 1235 - 1247 Titre : Neuro-fuzzy based analysis of hyperspectral imagery Type de document : Article/Communication Auteurs : F. Qiu, Auteur Année de publication : 2008 Article en page(s) : pp 1235 - 1247 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] classification floue [Termes IGN] classification hybride [Termes IGN] classification par réseau neuronal [Termes IGN] découverte de connaissances [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale Résumé : (Auteur) A neuro-fuzzy system, namely Gaussian Fuzzy Learning Vector Quantization (GFLVQ), was developed based on the synergy of a neural network and a fuzzy system. GFLVQ is both a fuzzy neural network and a neural fuzzy system with supervised learning and unsupervised self-organizing capabilities. In this paper, GFLVQ was further improved to efficiently and effectively process hyperspectral data through training data informed initialization and a simplified fuzzy learning algorithm. A geovisualization tool was developed to facilitate knowledge discovery and understanding of the hyperspectral image. A case study was conducted using a Hyperion image. The results obtained from the improved neuro-fuzzy system were found to be significantly better than those from conventional statistics-based and endmember-based classifiers. The fuzzy spectral profiles produced from the geovisualization tool provided an extra insight into the neuro-fuzzy learning process, further opening up the black box of the neural network. Copyright ASPRS Numéro de notice : A2008-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.10.1235 En ligne : https://doi.org/10.14358/PERS.74.10.1235 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29368 [article]

Modeling urban population growth from remotely sensed imagery and TIGER GIS road data / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 9 (September 2003) . - pp 1031 - 1042 Titre : Modeling urban population growth from remotely sensed imagery and TIGER GIS road data Type de document : Article/Communication Auteurs : F. Qiu, Auteur ; K.L. Woller, Auteur ; R. Briggs, Auteur Année de publication : 2003 Article en page(s) : pp 1031 - 1042 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] croissance urbaine [Termes IGN] démographie [Termes IGN] détection de changement [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] modèle mathématique [Termes IGN] population urbaine [Termes IGN] recensement démographique [Termes IGN] réseau routier [Termes IGN] Texas (Etats-Unis) Résumé : (Auteur) We modeled population growth from 1990 to 2000 in the north Dallas-Fort Worth Metroplex using two different methods: a conventional model based on remote sensing land-use change detection, and a newly devised approach using GIS-derived road development measurements. These methods were applied at both city and census-tract levels and were evaluated against the actual population growth. It was found that accurate population growth estimates are achieved by both methods. At the census-tract level, our models yielded a comparable result with that obtained from a more complex commercial demographics model. At both city and census-tract levels, models using road development were better than those using land-use change detection. In addition to being efficient in cost and time, our models provide direct visualization of the distribution of the actual population growth within cities and census tracts when compared to commercial demographic models. Numéro de notice : A2003-233 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.9.1031 En ligne : https://doi.org/10.14358/PERS.69.9.1031 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22528 [article]

A neural network image interpretation system to extract rural and urban land use and land cover information from remote sensor data / J.R. Jensen in Geocarto international, vol 16 n° 1 (March - May 2001)  

Public [article]  inGeocarto international > vol 16 n° 1 (March - May 2001) . - pp 19 - 28 Titre : A neural network image interpretation system to extract rural and urban land use and land cover information from remote sensor data Type de document : Article/Communication Auteurs : J.R. Jensen, Auteur ; F. Qiu, Auteur ; K. Patterson, Auteur Année de publication : 2001 Article en page(s) : pp 19 - 28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photo-interprétation [Termes IGN] image Ikonos [Termes IGN] occupation du sol [Termes IGN] photo-interprétation [Termes IGN] réseau neuronal artificiel [Termes IGN] utilisation du sol Résumé : (Auteur) This paper describes the characteristics of a neural network image interpretation system that is designed to extract both rural land cover and urban land use from high spatial resolution imagery (e.g., digitized aerial photography, IKONOS imagery) and/or from relatively coarse spatial and spectral resolution remote sensor data (e.g., Landsat Thematic Mapper). The system consists of modules that a) classify remote sensing imagery into different land use/land cover types, b) segment the rural land cover information into relatively homogeneous polygons in a standard GISformat, and/or c) digitize and interpret urban/suburban land use cover polygons based on their feature attribute information with the aid of a neural network. Numéro de notice : A2001-044 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040108542179 En ligne : https://doi.org/10.1080/10106040108542179 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21746 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-01011	RAB	Revue	Centre de documentation	En réserve L003	Disponible