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An adaptive filtering algorithm of multilevel resolution point cloud / Youyuan Li in Survey review, Vol 53 n° 379 (July 2021)
[article]
Titre : An adaptive filtering algorithm of multilevel resolution point cloud Type de document : Article/Communication Auteurs : Youyuan Li, Auteur ; Jian Wang, Auteur ; Bin Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 300 - 311 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de filtrage
[Termes IGN] analyse multirésolution
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] données lidar
[Termes IGN] filtrage de points
[Termes IGN] filtre adaptatif
[Termes IGN] interpolation spatiale
[Termes IGN] Kappa de Cohen
[Termes IGN] octree
[Termes IGN] pente
[Termes IGN] semis de points
[Termes IGN] seuillage de pointsRésumé : (auteur) The existing filtering methods for airborne LiDAR point cloud have low accuracy. An adaptive filtering algorithm is proposed which is improved based on multilevel resolution algorithm. First double index structure of Octree and KDtree is established. Then the initial reference surface is constructed by ground seed points. According to the slope fluctuation situation, the grid resolution of the ground referential surface is adjusted in an adaptive way. Finally, the refined surface is formed gradually by multilevel renewing resolution to provide filtered point cloud with high accuracy. Experimental results show that the error of Type II can be effectively reduced, the average Kappa coefficient increases by 0.53% and the average total error decreases by 0.44% compared with multiresolution hierarchical classification algorithm. The result tested by practically measured data shows that Kappa coefficient can reach 90%. Especially, it maintains advantages of high accuracy under complex topographic environment. Numéro de notice : A2021-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1755163 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1080/00396265.2020.1755163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98042
in Survey review > Vol 53 n° 379 (July 2021) . - pp 300 - 311[article]Geographical and temporal huff model calibration using taxi trajectory data / Shuhui Gong in Geoinformatica, vol 25 n° 3 (July 2021)
[article]
Titre : Geographical and temporal huff model calibration using taxi trajectory data Type de document : Article/Communication Auteurs : Shuhui Gong, Auteur ; John Cartlidge, Auteur ; Ruibin Bai, Auteur ; Yang Yue, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 485 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] attractivité (aménagement)
[Termes IGN] étalonnage de modèle
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] régression géographiquement pondérée
[Termes IGN] Shenzhen
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) The Huff model is designed to estimate the probability of shopping centre patronage based on a shopping centre’s attractiveness and the cost of a customer’s travel. In this paper, we attempt to discover some general shopping trends by calibrating the Huff model in Shenzhen, China, and New York, USA, using taxi trajectory GPS data and sharing bikes GPS data. Geographical and Temporal Weighted Regression (GTWR) is used to fit the model, and calibration results are compared with Ordinary Least Squares (OLS) regression, Geographical Weighted Regression (GWR), and Temporal Weighted Regression (TWR). Results show that GTWR gives the highest performance due to significant geographical and temporal variation in the Huff model parameters of attractiveness and travel cost. To explain the geographical variation, we use residential sales’ and rental prices in Shenzhen and New York as a proxy for customers’ wealth in each region. Pearson product-moment correlation results show a medium relationship between localised sales’ and rental prices and the Huff model parameter of attractiveness: that is, customer wealth explains geographic sensitivity to shopping area attractiveness. To explain temporal variation, we use census data in both Shenzhen and New York to provide job profile distributions for each region as a proxy to estimate customers’ spare leisure time. Regression results demonstrate that there is a significant linear relationship between the length of spare time and the parameter of shopping area attractiveness. In particular, we demonstrate that wealthy customers with less spare time are more sensitive to a shopping centre’s attractiveness. We also discover customers’ sensitivities to travel distance are related to their travel mode. In particular, people riding bikes to shopping areas care much more about trip distance compared with people who take taxi. Finally, results show a divergence in behaviours between customers in New York and Shenzhen at weekends. While customers in New York prefer to shop more locally at weekends, customers in Shenzhen care less about trip distance. We provide the GTWR calibration of the Huff model as our theoretical contribution. GTWR extends the Huff model to two dimensions (time and space), so as to analyse the differences of residents’ travel behaviours in different time and locations. We also provide the discoveries of factors affecting urban travel behaviours (wealth and employment) as practical contributions that may help optimise urban transportation design. In particular, the sensitivity of residents to the attraction of shopping areas has a significant positive linear relationship with the housing price and a significant negative linear relationship with the residents’ length of spare time. Numéro de notice : A2021-973 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10707-019-00390-x Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1007/s10707-019-00390-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100392
in Geoinformatica > vol 25 n° 3 (July 2021) . - pp 485 - 512[article]Parallel computing for fast spatiotemporal weighted regression / Xiang Que in Computers & geosciences, vol 150 (May 2021)
[article]
Titre : Parallel computing for fast spatiotemporal weighted regression Type de document : Article/Communication Auteurs : Xiang Que, Auteur ; Chao Ma, Auteur ; Xiaogang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104723 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] calcul matriciel
[Termes IGN] étalonnage de modèle
[Termes IGN] modèle de régression
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] régression géographiquement pondérée
[Termes IGN] traitement parallèleRésumé : (auteur) The Spatiotemporal Weighted Regression (STWR) model is an extension of the Geographically Weighted Regression (GWR) model for exploring the heterogeneity of spatiotemporal processes. A key feature of STWR is that it utilizes the data points observed at previous time stages to make better fit and prediction at the latest time stage. Because the temporal bandwidths and a few other parameters need to be optimized in STWR, the model calibration is computationally intensive. In particular, when the data amount is large, the calibration of STWR becomes heavily time-consuming. For example, with 10,000 points in 10 time stages, it takes about 2307 s for a single-core PC to process the calibration of STWR. Both the distance and the weighted matrix in STWR are memory intensive, which may easily cause memory insufficiency as data amount increases. To improve the efficiency of computing, we developed a parallel computing method for STWR by employing the Message Passing Interface (MPI). A cache in the MPI processing approach was proposed for the calibration routine. Also, a matrix splitting strategy was designed to address the problem of memory insufficiency. We named the overall design as Fast STWR (F-STWR). In the experiment, we tested F-STWR in a High-Performance Computing (HPC) environment with a total number of 204,611 observations in 19 years. The results show that F-STWR can significantly improve STWR's capability of processing large-scale spatiotemporal data. Numéro de notice : A2021-300 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104723 Date de publication en ligne : 05/03/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104723 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97413
in Computers & geosciences > vol 150 (May 2021) . - n° 104723[article]Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
[article]
Titre : Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network Type de document : Article/Communication Auteurs : Nantheera Anantrasirichai, Auteur ; Juliet Biggs, Auteur ; Krisztina Kelevitz, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2940 - 2950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] covariance
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] effet atmosphérique
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] interpolation spatiale
[Termes IGN] matrice
[Termes IGN] optimisation (mathématiques)
[Termes IGN] représentation parcimonieuse
[Termes IGN] Royaume-Uni
[Termes IGN] zone urbaineRésumé : (auteur) The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here, we explore the applicability of deep learning approaches by adapting a pretrained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the U.K. where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides, and tunneling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exist to construct a balanced training data set, and the deformation signals are slower and more localized than in previous applications. We propose three enhancement methods to tackle these problems: 1) spatial interpolation with modified matrix completion; 2) a synthetic training data set based on the characteristics of the real U.K. velocity map; and 3) enhanced overwrapping techniques. Using velocity maps spanning 2015–2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides, and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems. Numéro de notice : A2021-283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-020-00323-6 Date de publication en ligne : 31/08/2020 En ligne : https://doi.org/10.1007/s12518-020-00323-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97391
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2940 - 2950[article]Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)
[article]
Titre : Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam Type de document : Article/Communication Auteurs : Sevim Sezi Karayazi, Auteur ; Gamze Dane, Auteur ; Bauke de Vries, Auteur Année de publication : 2021 Article en page(s) : n° 198 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Amsterdam (Pays-Bas)
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatiale
[Termes IGN] attractivité (aménagement)
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] gestion durable
[Termes IGN] image Flickr
[Termes IGN] musée
[Termes IGN] patrimoine
[Termes IGN] point d'intérêt
[Termes IGN] régression géographiquement pondérée
[Termes IGN] tourismeRésumé : (auteur) Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam. Numéro de notice : A2021-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10040198 Date de publication en ligne : 25/03/2021 En ligne : https://doi.org/10.3390/ijgi10040198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97424
in ISPRS International journal of geo-information > vol 10 n° 4 (April 2021) . - n° 198[article]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)PermalinkMinimum-error world map projections defined by polydimensional meshes / Justin H. Kunimune in International journal of cartography, vol 7 n° 1 (March 2021)PermalinkEstimating the impacts of proximity to public transportation on residential property values: An empirical analysis for Hartford and Stamford areas, Connecticut / Bo Zhang in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)PermalinkA feature-preserving point cloud denoising algorithm for LiDAR-derived DEM construction / Chuanfa Chen in Survey review, Vol 53 n° 377 (February 2021)PermalinkGeo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])PermalinkImproving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkUne généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue / Philippe Brion (2021)PermalinkLearning-based representations and methods for 3D shape analysis, manipulation and reconstruction / Marie-Julie Rakotosaona (2021)PermalinkLocal 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)PermalinkMéthodes et outils pour l’analyse spatiale exploratoire en géolinguistique : contributions aux humanités numériques spatialisées / Clément Chagnaud (2021)Permalink