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Raster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])
[article]
Titre : Raster-based method for building selection in the multi-scale representation of two-dimensional maps Type de document : Article/Communication Auteurs : Yilang Shen, Auteur ; Tinghua Ai, Auteur ; Rong Zhao, Auteur Année de publication : 2022 Article en page(s) : pp 6494 - 6518 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] bâtiment
[Termes IGN] densité du bâti
[Termes IGN] distribution spatiale
[Termes IGN] données matricielles
[Termes IGN] représentation cartographique 2D
[Termes IGN] représentation multiple
[Termes IGN] segmentation
[Termes IGN] superpixel
[Termes IGN] triangulation de Delaunay
[Vedettes matières IGN] GénéralisationRésumé : (auteur) In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method for raster building selection is proposed. In this method, to preserve the distribution range, a new homogeneous linear spectral clustering (HLSC) superpixel segmentation method is developed for the relatively homogeneous spatial division of building groups. Then, to preserve the relative distribution density, multi-level spatial division is performed according to the local number of buildings. Finally, to preserve the local geometric, attributive and geographical characteristics, four selection strategies, namely, the minimum centroid distance, minimum boundary distance, maximum area and considering geographical element strategies, are designed to generate selection results. To evaluate the proposed method, dispersed buildings in a suburban area are utilized to perform selection tasks. The experimental results indicate that the proposed method can effectively select dispersed irregular buildings at different levels of detail while maintaining the original distribution range and relative distribution density. In addition, the use of multiple selection strategies considering various geometric, attributive and geographical characteristics provides multiple options for cartography. Numéro de notice : A2022-727 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1943007 Date de publication en ligne : 29/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1943007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101673
in Geocarto international > vol 37 n° 22 [10/10/2022] . - pp 6494 - 6518[article]Novel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)
[article]
Titre : Novel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud Type de document : Article/Communication Auteurs : Jie Yang, Auteur ; Xiaorong Wen, Auteur ; Qiulai Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1534 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] branche (arbre)
[Termes IGN] C++
[Termes IGN] Chine
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] itération
[Termes IGN] modélisation de la forêt
[Termes IGN] semis de points
[Termes IGN] squelettisationRésumé : (auteur) More accurate tree models, such as branch skeleton, are needed to acquire forest inventory data. Currently available algorithms for constructing a branch skeleton from a LiDAR point cloud have low accuracy with problems such as irrational connection near trunk bifurcation, excessive central deviation and topological errors. Using the C++ and PCL library, a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN), based on geometric characteristics for tree branch skeleton extraction, was developed in this research. The algorithm is an incomplete simulation of tree transmitting water and nutrients. Improvements were made to improve the time and memory consumption. The result show that the ISTTWN algorithm without any improvements is quite time consuming but has consecutive output. After improvement with iteration, the process is faster and has more detailed output. Breakpoint connection is added to recover continuity. The ISTTWN algorithm with improvements can produce a more accurate skeleton and cost less time than a previous algorithm. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of tree modeling and a prospect of application in other fields, such as virtual reality, computer games and movie scenes. Numéro de notice : A2022-835 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13101534 Date de publication en ligne : 17/09/2022 En ligne : https://doi.org/10.3390/f13101534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102032
in Forests > vol 13 n° 10 (October 2022) . - n° 1534[article]A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers / Qasim Khan in Geocarto international, vol 37 n° 20 ([20/09/2022])
[article]
Titre : A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers Type de document : Article/Communication Auteurs : Qasim Khan, Auteur ; Muhammad Usman Liaqat, Auteur ; Mohamed Mostafa Mohamed, Auteur Année de publication : 2022 Article en page(s) : pp 5832 - 5850 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] aquifère
[Termes IGN] ArcGIS
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] eau souterraine
[Termes IGN] Emirats Arabes Unis
[Termes IGN] estimation par noyau
[Termes IGN] nitrate
[Termes IGN] vulnérabilitéRésumé : (auteur) Groundwater is more prone to contamination due to its extensive usage. Different methods are applied to study vulnerability of groundwater including widely used DRASTIC method, SI and GOD. This study proposes a novel method of mapping groundwater vulnerability using machine learning algorithms. In this study, point extraction method was used to extract point values from a grid of 646 points of seven raster layer in the Al Khatim study area of United Arab Emirates. These extracted values were classified based on nitrate concentration threshold of 50 mg/L into two classes. Machine learning models were developed, using depth to water (D), recharge (R), aquifer media (A), soil media (S), topography (T), vadose zone (I) and hydraulic conductivity (C), on the basis of nitrate class. Classified ‘groundwater vulnerability class values’ were trained using 10-fold cross-validation, using four machine learning models which were Random Forest, Support Vector Machine, Naïve Bayes and C4. 5. Accuracy showed the model developed by Random Forest gained highest accuracy of 93%. Four groundwater vulnerability maps were developed from machine learning classifiers and was compared with base method of DRASTIC index. The efficiency, accuracy and validity of machine learning based models were evaluated based on Receiver Operating Characteristics (ROC) curve and Precision-Recall curve (PRC). The results proved that machine learning is an efficient tool to access, analyze and map groundwater vulnerability. Numéro de notice : A2022-716 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1923833 Date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1923833 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101641
in Geocarto international > vol 37 n° 20 [20/09/2022] . - pp 5832 - 5850[article]Regional climate moderately influences species-mixing effect on tree growth-climate relationships and drought resistance for beech and pine across Europe / Géraud de Streel in Forest ecology and management, vol 520 (September-15 2022)
[article]
Titre : Regional climate moderately influences species-mixing effect on tree growth-climate relationships and drought resistance for beech and pine across Europe Type de document : Article/Communication Auteurs : Géraud de Streel, Auteur ; François Lebourgeois, Auteur ; Christian Ammer, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120317 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] Bootstrap (statistique)
[Termes IGN] climat
[Termes IGN] coefficient de corrélation
[Termes IGN] dendrochronologie
[Termes IGN] échantillonnage
[Termes IGN] Europe (géographie politique)
[Termes IGN] évapotranspiration
[Termes IGN] Fagus sylvatica
[Termes IGN] peuplement mélangé
[Termes IGN] Pinus sylvestris
[Termes IGN] région
[Termes IGN] sécheresse
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Increasing species diversity is considered a promising strategy to mitigate the negative impacts of global change on forests. However, the interactions between regional climate conditions and species-mixing effects on climate-growth relationships and drought resistance remain poorly documented. In this study, we investigated the patterns of species-mixing effects over a large gradient of environmental conditions throughout Europe for European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.), two species with contrasted ecological traits. We hypothesized that across large geographical scales, the difference of climate-growth relationships and drought resistance between pure and mixed stands would be dependent on regional climate. We used tree ring chronologies derived from 1143 beech and 1164 pine trees sampled in 30 study sites, each composed of one mixed stand of beech and pine and of the two corresponding pure stands located in similar site conditions. For each site and stand, we used Bootstrapped Correlation Coefficients (BCCs) on standardized chronologies and growth reduction during drought years on raw chronologies to analyze the difference in climate-tree growth relationships and resistance to drought between pure and mixed stands. We found consistent large-scale spatial patterns of climate-growth relationships. Those patterns were similar for both species. With the exception of the driest climates where pure and mixed beech stands tended to display differences in growth correlation with the main climatic drivers, the mixing effects on the BCCs were highly variable, resulting in the lack of a coherent response to mixing. No consistent species-mixing effect on drought resistance was found within and across climate zones. On average, mixing had no significant effect on drought resistance for neither species, yet it increased pine resistance in sites with higher climatic water balance in autumn. Also, beech and pine most often differed in the timing of their drought response within similar sites, irrespective of the regional climate, which might increase the temporal stability of growth in mixed compared to pure stands. Our results showed that the impact of species mixing on tree response to climate did not strongly differ between groups of sites with distinct climate characteristics and climate-growth relationships, indicating the interacting influences of species identity, stand characteristics, drought events characteristics as well as local site conditions. Numéro de notice : A2022-557 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120317 Date de publication en ligne : 17/06/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101172
in Forest ecology and management > vol 520 (September-15 2022) . - n° 120317[article]Adaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)
[article]
Titre : Adaptive block modeling of time dependent variations of datum reference points in a tectonically active area Type de document : Article/Communication Auteurs : Chun-Yun Chou, Auteur ; Jen-Yu Han, Auteur Année de publication : 2022 Article en page(s) : pp 404 - 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] analyse de groupement
[Termes IGN] angle d'Euler
[Termes IGN] champ de vitesse
[Termes IGN] Cinématique
[Termes IGN] collocation par moindres carrés
[Termes IGN] données GNSS
[Termes IGN] formule d'Euler
[Termes IGN] matrice de covariance
[Termes IGN] rotation
[Termes IGN] série temporelle
[Termes IGN] station GNSS
[Termes IGN] système de référence local
[Termes IGN] Taïwan
[Termes IGN] tectonique des plaques
[Termes IGN] variation temporelleRésumé : (auteur) Although a dynamic or semi-dynamic datum has been adopted in some countries, it remains a challenge if a long-term stable datum is to be established in a tectonic active area. This study presents an approach to realistically reflect the time dependent behaviors of ground reference points while maintaining the long-term stability of a datum. An adaptive approach coupled with the Euler motion model is proposed for dividing an area into blocks. A least-squares collocation is then applied for modeling the residual velocities in each block. A case study using the data from 375 continuously operated GNSS stations in Taiwan is presented. It is illustrated that the complex surface kinematics in this region can be divided into three blocks. Significant reductions up to 64% of residual velocities were obtained. This shows that a stable datum can be established in a region with active and complicated surface kinematics by implementing the proposed. Numéro de notice : A2022-658 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1949194 Date de publication en ligne : 12/07/2021 En ligne : https://doi.org/10.1080/00396265.2021.1949194 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101509
in Survey review > vol 54 n° 386 (September 2022) . - pp 404 - 419[article]An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)PermalinkAssessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)PermalinkMeasuring COVID-19 vulnerability for Northeast Brazilian municipalities: Social, economic, and demographic factors based on multiple criteria and spatial analysis / Ciro José Jardim De Figueiredo in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)PermalinkSTICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity / Yuhao Kang in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)PermalinkCan machine learning improve small area population forecasts? A forecast combination approach / Irina Grossman in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkEstimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkGeographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis / Chuan Yin in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)PermalinkDetecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkDetecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkGIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey / Saffet Erdoğan in Applied geomatics, vol 14 n° 2 (June 2022)Permalink