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Analysis of factors affecting adoption of volunteered geographic information in the context of national spatial data infrastructure / Munir Ahmad in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)
[article]
Titre : Analysis of factors affecting adoption of volunteered geographic information in the context of national spatial data infrastructure Type de document : Article/Communication Auteurs : Munir Ahmad, Auteur ; Malik Sikandar Hayat Khayal, Auteur ; Ali Tahir, Auteur Année de publication : 2022 Article en page(s) : n° 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] données localisées des bénévoles
[Termes IGN] fiabilité des données
[Termes IGN] infrastructure nationale des données localisées
[Termes IGN] INSPIRE
[Termes IGN] modèle empirique
[Termes IGN] Pakistan
[Termes IGN] qualité des données
[Termes IGN] régression des moindres carrés partielsRésumé : (auteur) Spatial data infrastructures (SDIs) have been implemented for the last four decades in most countries. One of the key objectives of SDIs is to ensure the quick availability and accessibility of spatial data. The success of SDI depends on the underlying spatial datasets. Many developing countries such as Pakistan are facing problems in implementing SDI because of the unavailability of spatial data. Volunteered Geographic Information (VGI) is an alternate source for obtaining spatial data. Therefore, the question is what factors hamper the adoption of VGI for making it part of SDI in Pakistan. The intention behind this paper is to explore such factors as the key research question. To do so, we make use of the Technology–Organization–Environment (TOE) framework along with the partial least square structural equation model (PLS-SEM) to empirically analyze the factors impeding VGI from becoming part of SDI in the country. The study concludes that many technical, organizational, and environmental factors affect the adoption of VGI to be part of SDI in Pakistan. Numéro de notice : A2022-169 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020120 En ligne : https://doi.org/10.3390/ijgi11020120 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99798
in ISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 120[article]Analysis of spatio-temporal changes in forest biomass in China / Weiyi Xu in Journal of Forestry Research, vol 33 n° 1 (February 2022)
[article]
Titre : Analysis of spatio-temporal changes in forest biomass in China Type de document : Article/Communication Auteurs : Weiyi Xu, Auteur ; Xiaobin Jin, Auteur ; Jing Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 261 - 278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle
[Termes IGN] biomasse
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] puits de carbone
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Forests play a central role in the global carbon cycle. China's forests have a high carbon sequestration potential owing to their wide distribution, young age and relatively low carbon density. Forest biomass is an essential variable for assessing carbon sequestration capacity, thus determining the spatio-temporal changes of forest biomass is critical to the national carbon budget and to contribute to sustainable forest management. Based on Chinese forest inventory data (1999–2013), this study explored spatial patterns of forest biomass at a grid resolution of 1 km by applying a downscaling method and further analyzed spatio-temporal changes of biomass at different spatial scales. The main findings are: (1) the regression relationship between forest biomass and the associated influencing factors at a provincial scale can be applied to estimate biomass at a pixel scale by employing a downscaling method; (2) forest biomass had a distinct spatial pattern with the greatest biomass occurring in the major mountain ranges; (3) forest biomass changes had a notable spatial distribution pattern; increase (i.e., carbon sinks) occurred in east and southeast China, decreases (i.e., carbon sources) were observed in the northeast to southwest, with the largest biomass losses in the Hengduan Mountains, Southern Hainan and Northern Da Hinggan Mountains; and, (4) forest vegetation functioned as a carbon sink during 1999–2013 with a net increase in biomass of 3.71 Pg. Numéro de notice : A2022-336 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s11676-021-01299-8 Date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1007/s11676-021-01299-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100678
in Journal of Forestry Research > vol 33 n° 1 (February 2022) . - pp 261 - 278[article]Application of catastrophe theory to spatial analysis of groundwater potential in a sub-humid tropical region: a hybrid approach / Laishram Kanta Singh in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Application of catastrophe theory to spatial analysis of groundwater potential in a sub-humid tropical region: a hybrid approach Type de document : Article/Communication Auteurs : Laishram Kanta Singh, Auteur ; Madan K. Jha, Auteur ; V.M. Chowdary, Auteur Année de publication : 2022 Article en page(s) : pp 700 - 719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse multicritère
[Termes IGN] analyse spatiale
[Termes IGN] couche thématique
[Termes IGN] drainage
[Termes IGN] eau souterraine
[Termes IGN] gestion de l'eau
[Termes IGN] Inde
[Termes IGN] pondération
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] zone tropicale humideRésumé : (auteur) Geospatial techniques and Multi-Criteria Decision Analysis (MCDA) play a crucial role in the planning and management of land and water resources. GIS-based MCDA technique "Catastrophe theory" has been recently proposed for evaluating groundwater potential. However, the major limitation of "Catastrophe theory" is that only quantitative factors/thematic layers can be used for assessing groundwater potential, though qualitative factors are equally important. To overcome this inherent limitation, a novel GIS-based MCDA approach named "Hybrid Catastrophe" technique is proposed in this study. The "Hybrid Catastrophe" technique integrates the original "Catastrophe theory" with the "Analytic Hierarchy Process (AHP)" to take into account both qualitative and quantitative thematic layers for assessing groundwater potential, thereby improving the reliability and versatility of the original Catastrophe technique. The applicability of "Hybrid Catastrophe" technique is demonstrated through a case study wherein 8 influential thematic layers (both quantitative and qualitative) were considered for assessing groundwater potential. The four quantitative layers were assigned weights based on the "Catastrophe theory" and the remaining four qualitative layers were assigned weights based on the "AHP theory". These thematic layers were integrated in GIS to delineate groundwater potential zones. The "Hybrid Catastrophe" technique yields four groundwater potential zones in the study area: (i) "very good" (covering 16% of the study area), (ii) "good" (54%), (iii) "moderate" (29%) and (iv) "poor" (1%) and its accuracy was found to be 77% that is reasonably high. The proposed "Hybrid Catastrophe" technique is versatile and it can be successfully applied to other parts of the world for evaluating groundwater potential at diverse spatial scales irrespective of agro-climatic, hydrologic and hydrogeologic conditions. Numéro de notice : A2022-343 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1737970 Date de publication en ligne : 11/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1737970 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100524
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 700 - 719[article]Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Building footprint extraction in Yangon city from monocular optical satellite image using deep learning Type de document : Article/Communication Auteurs : Hein Thura Aung, Auteur ; Sao Hone Pha, Auteur ; Wataru Takeuchi, Auteur Année de publication : 2022 Article en page(s) : pp 792 - 812 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Birmanie
[Termes IGN] détection du bâti
[Termes IGN] empreinte
[Termes IGN] image Geoeye
[Termes IGN] image isolée
[Termes IGN] réseau antagoniste génératif
[Termes IGN] vision monoculaireRésumé : (auteur) In this research, building footprints in Yangon City, Myanmar are extracted only from monocular optical satellite image by using conditional generative adversarial network (CGAN). Both training dataset and validating dataset are created from GeoEYE image of Dagon Township in Yangon City. Eight training models are created according to the change of values in three training parameters; learning rate, β1 term of Adam, and number of filters in the first convolution layer of the generator and the discriminator. The images of the validating dataset are divided into four image groups; trees, buildings, mixed trees and buildings, and pagodas. The output images of eight trained models are transformed to the vector images and then evaluated by comparing with manually digitized polygons using completeness, correctness and F1 measure. According to the results, by using CGAN, building footprints can be extracted up to 71% of completeness, 81% of correctness and 69% of F1 score from only monocular optical satellite image. Numéro de notice : A2022-345 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1740949 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1740949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100526
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 792 - 812[article]A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)
[article]
Titre : A combination of convolutional and graph neural networks for regularized road surface extraction Type de document : Article/Communication Auteurs : Jingjing Yan, Auteur ; Shunping Ji, Auteur ; Yao Wei, Auteur Année de publication : 2022 Article en page(s) : n° 4409113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] jeu de données
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] réseau neuronal de graphes
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Road surface extraction from high-resolution remote sensing images has many engineering applications; however, extracting regularized and smooth road surface maps that reach the human delineation level is a very challenging task, and substantial and time-consuming manual work is usually unavoidable. In this article, to solve this problem, we propose a novel regularized road surface extraction framework by introducing a graph neural network (GNN) for processing the road graph that is preconstructed from the easily accessible road centerlines. The proposed framework formulates the road surface extraction problem as two-sided width inference of the road graph and consists of a convolutional neural network (CNN)-based feature extractor and a GNN model for vertex attribute adjustment. The CNN extracts the high-level abstract features of each vertex in the graph as the input of the GNN and also the road boundary features that allow us to distinguish roads from the background. The GNN propagates and aggregates the features of the vertices in the graph to achieve global optimization of the regression of the regularized widths of the vertices. At the same time, a biased centerline map can also be corrected based on the width prediction result. To the best of the authors’ knowledge, this is the first study to have introduced a GNN to regularized human-level road surface extraction. The proposed method was evaluated on four diverse datasets, and the results show that the proposed method comprehensively outperforms the recent CNN-based segmentation methods and other regularization methods in the intersection over union (IoU) and smoothness score, and a visual check shows that a majority of the prediction results of the proposed method approach the human delineation level. Numéro de notice : A2022-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3151688 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3151688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100355
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 2 (February 2022) . - n° 4409113[article]Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan / Eunbeen Park in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkDynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])PermalinkExploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods / Bin Zhang in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkGenerating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network / Da He in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)PermalinkIntegrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan / Katsuto Shimizu in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)PermalinkNovel model for predicting individuals’ movements in dynamic regions of interest / Xiaoqi Shen in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkQuantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data / Tianyu Hu in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)PermalinkRecurrent origin–destination network for exploration of human periodic collective dynamics / Xiaojian Chen in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkSNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkSpatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)Permalink