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Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction / Shuo Shi in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 90 Titre : Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction Type de document : Article/Communication Auteurs : Shuo Shi, Auteur ; Xingtao Tang, Auteur ; Bowen Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse spectrale [Termes IGN] arbre urbain [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Houston (Texas) [Termes IGN] interpolation [Termes IGN] réflectance spectrale [Termes IGN] segmentation [Termes IGN] semis de points Résumé : (auteur) Lidar can effectively obtain three-dimensional information on ground objects. In recent years, lidar has developed rapidly from single-wavelength to multispectral hyperspectral imaging. The multispectral airborne lidar Optech Titan is the first commercial system that can collect point cloud data on 1550, 1064, and 532 nm channels. This study proposes a method of point cloud segmentation in the preprocessed intensity interpolation process to solve the problem of inaccurate intensity at the boundary during point cloud interpolation. The entire experiment consists of three steps. First, a multispectral lidar point cloud is obtained using point cloud segmentation and intensity interpolation; the spatial dimension advantage of the multispectral point cloud is used to improve the accuracy of spectral information interpolation. Second, point clouds are divided into eight categories by constructing geometric information, spectral reflectance information, and spectral characteristics. Accuracy evaluation and contribution analysis are also conducted through point cloud truth value and classification results. Lastly, the spatial dimension information is enhanced by point cloud drop sampling, the method is used to solve the error caused by airborne scanning and single-tree extraction of urban trees. Classification results showed that point cloud segmentation before intensity interpolation can effectively improve the interpolation and classification accuracies. The total classification accuracy of the data is improved by 3.7%. Compared with the extraction result (377) of single wood without subsampling treatment, the result of the urban tree extraction proved the effectiveness of the proposed method with a subsampling algorithm in improving the accuracy. Accordingly, the problem of over-segmentation is solved, and the final single-wood extraction result (329) is markedly consistent with the real situation of the region. Numéro de notice : A2023-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030090 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.3390/ijgi12030090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102852 [article]

A comparative assessment of the statistical methods based on urban population density estimation / Merve Yılmaz in Geocarto international, vol 38 n° 1 ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° 1 [01/01/2023] . - n° 2152494 Titre : A comparative assessment of the statistical methods based on urban population density estimation Type de document : Article/Communication Auteurs : Merve Yılmaz, Auteur Année de publication : 2023 Article en page(s) : n° 2152494 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de population [Termes IGN] planification urbaine [Termes IGN] population urbaine [Termes IGN] régression géographiquement pondérée [Termes IGN] régression multiple [Termes IGN] Turquie Résumé : (auteur) Population density is important spatial information for addressing the use and access to land resources in cities under the Sustainable Development Goals. This is because the spatial data support appropriate spatial policies at the spatial scale and predicts how much land will be consumed in the future. The study aims to compare and evaluate the regression tools in the context of estimating the population density difference. The three analysis tools used are Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The sampling area covers cities around Türkiye. Comparative results showed that the two most important descriptive variables in the Random Forest-Based Classification model are the density difference of the new developed area and the connectivity. The three main explanatory variables of the Multiple Linear Regression model are centrality, vehicle ownership, and accessibility. The results of the Multiple Linear Regression model (a non-spatial model) and the Geographically Weighted Regression model (a spatial model), were found to be quite similar. The importance of accessibility and connectivity is more evident in the Multiple Linear Regression model when the Random Forest-Based Classification model highlights the density values in the new development areas. Numéro de notice : A2023-055 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2152494 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.1080/10106049.2022.2152494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102388 [article]

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density / Luyen K. Bui in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 7 (January 2023)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 7 (January 2023) . - n° 100028 Titre : Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density Type de document : Article/Communication Auteurs : Luyen K. Bui, Auteur ; Craig L. Glennie, Auteur Année de publication : 2023 Article en page(s) : n° 100028 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Alaska (Etats-Unis) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Hawaii (Etats-Unis) [Termes IGN] incertitude des données [Termes IGN] interpolation [Termes IGN] modèle numérique de surface [Termes IGN] semis de points [Termes IGN] Triangulated Irregular Network Résumé : (auteur) Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset. Numéro de notice : A2023-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100028 Date de publication en ligne : 17/12/2023 En ligne : https://doi.org/10.1016/j.ophoto.2022.100028 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102494 [article]

Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population / Heng Wan in Computers, Environment and Urban Systems, vol 99 (January 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 99 (January 2023) . - n° 101899 Titre : Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population Type de document : Article/Communication Auteurs : Heng Wan, Auteur ; Jim Yoon, Auteur ; Vivek Srikrishnan, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101899 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] carte thématique [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] Etats-Unis [Termes IGN] indicateur paysager [Termes IGN] interpolation [Termes IGN] occupation du sol [Termes IGN] paysage [Termes IGN] planification urbaine [Termes IGN] réduction d'échelle Résumé : (auteur) Population downscaling and interpolation methods are required to produce data which correspond to spatial units used in urban planning, demography, and environmental modeling. Population data are typically aggregated at census enumeration units, which can have arbitrary, temporally-evolving boundaries. Previous approaches to imperviousness-based dasymetric mapping ignore cell-level patterning of imperviousness within a spatial unit of prediction, which potentially serve as a strong indicator of population. Landscape metrics derived from imperviousness data offer a promising approach to capture these patterns. In this study, we incorporate landscape metrics derived from impervious cover percentage maps into intelligent dasymetric mapping to downscale population from census tracts to block groups in four states with varying population densities: Connecticut, South Carolina, West Virginia, and New Mexico. We compare the performance of the landscape metrics-based models against two baseline models in all four states across three different time periods. The results show that intelligent dasymetric mapping using landscape metrics generally outperforms the two baseline models. We further compare the performance of landscape metrics as an ancillary source of information for dasymetric mapping against other traditionally-used datasets (e.g., land use, roads, nighttime lights data) in three states (Connecticut, South Carolina, and New Mexico) in 2000. We find that class area, landscape shape index, and number of patches consistently achieve lower error rates than other ancillary datasets in all the three states. Numéro de notice : A2023-013 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101899 Date de publication en ligne : 02/11/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102130 [article]

Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution / Estera Trzcina in Journal of geodesy, vol 97 n° 1 (January 2023)  

Public [article]  inJournal of geodesy > vol 97 n° 1 (January 2023) . - n° 2 Titre : Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution Type de document : Article/Communication Auteurs : Estera Trzcina, Auteur ; Witold Rohm, Auteur ; Kamil Smolak, Auteur Année de publication : 2023 Article en page(s) : n° 2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] interpolation bilinéaire [Termes IGN] modèle météorologique [Termes IGN] optimisation (mathématiques) [Termes IGN] radiosondage [Termes IGN] récepteur GNSS [Termes IGN] retard troposphérique [Termes IGN] retard troposphérique zénithal [Termes IGN] système de grille globale discrète [Termes IGN] teneur en vapeur d'eau [Termes IGN] tomographie [Termes IGN] troposphère Résumé : (auteur) Water vapour is a highly variable constituent of the troposphere; thus, its high-resolution measurements are of great importance to weather prediction systems. The Global Navigation Satellite Systems (GNSS) are operationally used in the estimation of the tropospheric state and assimilation of the results into the weather models. One of the GNSS techniques of troposphere sensing is tomography which provides 3-D fields of wet refractivity. The tomographic results have been successfully assimilated into the numerical weather models, showing the great potential of this technique. The GNSS tomography can be based on two different approaches to the parameterisation of the model’s domain, i.e. block (voxel-based) or grid (node-based) approach. Regardless of the parameterisation approach, the tomographic domain should be discretised, which is usually performed in a regular manner, with a grid resolution depending on the mean distance between the GNSS receivers. In this work, we propose a new parameterisation approach based on the optimisation of the tomographic nodes’ location, taking into account the non-uniform distribution of the GNSS information in the troposphere. The experiment was performed using a dense network of 16 low-cost multi-GNSS receivers located in Wrocław and its suburbs, with a mean distance of 3 km. Cross-validation of four different parameterisation approaches is presented. The validation is performed based on the Weather Research and Forecasting model as well as radiosonde observations. The new approach improves the results of wet refractivity estimation by 0.5–2 ppm in terms of RMSE, especially for altitudes of 0.5–2.0 km. Numéro de notice : A2023-044 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1007/s00190-022-01691-0 Date de publication en ligne : 30/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01691-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102343 [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)  

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Geographically 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)  

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Analysis 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])  

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Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions / Di Zhu in Geoinformatica, vol 26 n° 4 (October 2022)  

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The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)  

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The use of gravity data to determine orthometric heights at the Hong Kong territories / Albertini Nsiah Ababio in Journal of applied geodesy, vol 16 n° 4 (October 2022)  

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Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand / Kiatkulchai Jitt-Aer in Natural Hazards, vol 113 n° 1 (August 2022)  

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Mixed geographically and temporally weighted regression for spatio-temporal deformation modelling / Zhijia Yang in Survey review, vol 54 n° 385 (July 2022)  

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Temporal transitions of demographic dot maps / Jeff Allen in International journal of cartography, vol 8 n° 2 (July 2022)  

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Validation of regional and global ionosphere maps from GNSS measurements versus IRI2016 during different magnetic activity / Ahmed Sedeek in Journal of applied geodesy, vol 16 n° 3 (July 2022)  

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