Centre de documentation
scientifique

A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)  

Public [article]  inRemote sensing of environment > vol 282 (December 2022) . - n° 113280 Titre : A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds Type de document : Article/Communication Auteurs : Xiaoqiang Liu, Auteur ; Qin Ma, Auteur ; Xiaoyong wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] Chine [Termes IGN] couvert forestier [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage [Termes IGN] écosystème forestier [Termes IGN] entropie [Termes IGN] estimation par noyau [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] semis de points Résumé : (auteur) Forest canopy structural complexity (CSC) describes the three-dimensional (3D) arrangement of canopy elements, and has become an emergent forest attribute mediating forest ecosystem functioning along with species diversity. Light detection and ranging (lidar), especially the emerging near-surface lidar platforms (e.g., terrestrial laser scanning/TLS, backpack laser scanning/BLS, unmanned aerial vehicle laser scanning/ULS), can depict 3D canopy information with high efficiency and accuracy, providing an ideal data source for forest CSC quantification. However, current existing lidar-based CSC quantification indices may share common limitations of getting saturated in structurally complex forest stands and not fully capturing within-canopy structural variations. In this study, we introduced the concept of entropy into forest CSC quantification, and proposed a new forest CSC index, namely canopy entropy (CE). Two major bottlenecks were addressed in the CE calculation procedure, including (1) using a Mann-Kendall (MK) test-based resampling strategy to address the issue of incongruent sampling chances of canopy elements at different locations from different lidar systems, and (2) using a kernel density estimation (KDE)-based method to reduce its dependence on point density. The effectiveness and generality of CE were evaluated by simulating TLS and ULS point clouds from nine forest stands and collecting TLS, BLS, and ULS point clouds from 110 field plots distributed in five forest sites, covering a large variety of forest types and forest CSC conditions. The results showed that CE was an effective forest CSC quantification index that successfully captured CSC variations caused by both tree density and the number of vertical canopy layers. It had significant positive correlations with four widely used CSC indices (i.e., canopy cover, foliage height diversity, canopy top rugosity, and fractal dimension; R2: 0.32 to 0.67), but outperformed them by overcoming their common limitations. CE estimates from multiplatform lidar point clouds agreed well with each other (R2 ≥ 0.70, RMSE ≤0.10), indicating it has generality in cross-platform forest CSC quantification practices. We believe the proposed CE index has great potential to help us unravel the correlations among forest CSC, species diversity, and forest ecosystem functions, and therefore improve our understanding on forest ecosystem processes. Numéro de notice : A2022-795 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113280 Date de publication en ligne : 26/09/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101930 [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])  

Public [article]  inGeocarto international > vol 37 n° 20 [20/09/2022] . - pp 5832 - 5850 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] vulnérabilité Résumé : (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 [article]

Assessing 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)  

Public [article]  inGeodetski vestnik > vol 66 n° 3 (September - November 2022) . - pp 412 - 431 Titre : Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS Type de document : Article/Communication Auteurs : Yegane Khosravi, Auteur ; Farhad Hosseinali, Auteur ; Mostafa Adresi, Auteur Année de publication : 2022 Article en page(s) : pp 412 - 431 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accident de la route [Termes IGN] analyse de groupement [Termes IGN] autocorrélation spatiale [Termes IGN] classification par nuées dynamiques [Termes IGN] corrélation automatique de points homologues [Termes IGN] distance de Manhattan [Termes IGN] estimation par noyau [Termes IGN] Iran [Termes IGN] méthode statistique [Termes IGN] pente [Termes IGN] processus de hiérarchisation analytique [Termes IGN] regroupement de données [Termes IGN] système d'information géographique Résumé : (auteur) Road accidents are among the most critical causes of fatality, personal injuries, and financial damage worldwide. Identifying accident hotspots and the causes of accidents and improving the condition of these hotspots is an economical way to improve road traffic safety. In this study, to identify the accident hotspots of “Dehbala” road located in Yazd province-Iran, statistical and non-statistical clustering methods were used. First, the weighting of the criteria was performed by an expert using the AHP method. Hence, the spatial correlation of slope and curvature was calculated by Global Moran’I. Anselin Local Moran index and Getis-Ord Gi* and Kernel Density Estimation were used to identify accident hotspots based on accident location due to the density of points. As a result, four accident hotspots were obtained by the Anselin Local Moran index, three accident hotspots by Getis-Ord Gi*and one accident-prone area were obtained by Kernel Density Estimation method. Three algorithms, k-means, k-medoids, and DBSCAN, were used to identify accident-prone areas or points using non-statistical methods. The dense cluster of each method was considered as an accident-prone cluster. Then the results of statistical and non- statistical methods were intersected with each other and the final accident-prone area was obtained. This study revealed the effect of geometric charcateristics of the road (slope and curvature) on the occurance of accidents. Numéro de notice : A2022-781 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.15292/geodetski-vestnik.2022.03.412-431 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.15292/geodetski-vestnik.2022.03.412-431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101864 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2022031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Deep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 9 (September 2022) . - pp 2103 - 2130 Titre : Deep image deblurring: A survey Type de document : Article/Communication Auteurs : Kaihao Zhang, Auteur ; Wenqi Ren, Auteur ; Wenhan Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2103 - 2130 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déconvolution [Termes IGN] estimation par noyau [Termes IGN] filtrage du bruit [Termes IGN] image floue [Termes IGN] qualité d'image [Termes IGN] réseau antagoniste génératif [Termes IGN] taxinomie [Termes IGN] vision par ordinateur Résumé : (auteur) Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions. Numéro de notice : A2022-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-022-01633-5 Date de publication en ligne : 25/06/2022 En ligne : https://doi.org/10.1007/s11263-022-01633-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101444 [article]

A constraint-based approach for identifying the urban–rural fringe of polycentric cities using multi-sourced data / Jing Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 1 (January 2022) . - pp 114 - 136 Titre : A constraint-based approach for identifying the urban–rural fringe of polycentric cities using multi-sourced data Type de document : Article/Communication Auteurs : Jing Yang, Auteur ; Jingwen Dong, Auteur ; Yizhong Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 114 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] délimitation de frontière [Termes IGN] données multisources [Termes IGN] entropie de Shannon [Termes IGN] espace rural [Termes IGN] estimation par noyau [Termes IGN] Kiangsou (Chine) [Termes IGN] programmation par contraintes [Termes IGN] transformation en ondelettes [Termes IGN] urbanisation [Termes IGN] zonage (urbanisme) [Termes IGN] zone rurale [Termes IGN] zone urbaine Résumé : (auteur) Studies on urban–rural fringes, which represent regions facing various urbanization problems caused by rapid expansion, have steadily increased in recent years. However, problems persist in the quantitative delimitation of such regions. Based on the characteristics of abrupt urbanization-level changes in urban–rural fringe areas, we propose a constraint-based method in this study to detect the urban–rural fringes of cities with a spatial polycentric structure of ‘Main center–Subcenter’ based on data from multiple sources. We used the proposed approach to delimitate the fringe areas of Jiangyin and Zhangjiagang and identify their urban main center and subcenter pre-defined by their city master plans, towns, and rural hinterlands. Comparison of the identified results of different single urbanization indices, a single detection center, kernel density estimation, and a single constraint revealed that the patch density and Shannon’s diversity index of the proposed method were higher in urban–rural fringes and smaller in city centers and rural hinterlands. This suggests that the landscape of urban–rural fringes delimitated by the proposed method is more fragmented, diverse, and complicated, thereby performing better. This study is significant for future urban spatial analysis, planning, and management. Numéro de notice : A2022-045 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/13658816.2021.1876236 Date de publication en ligne : 05/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1876236 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99404 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

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Modeling in forestry using mixture models fitted to grouped and ungrouped data / Eric K. Zenner in Forests, vol 12 n° 9 (September 2021)  

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Understanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)  

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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)  

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A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December-1 2020)  

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Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam / Khanh Giang Le in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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Pedestrian network generation based on crowdsourced tracking data / Xue Yang in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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Using GIS for disease mapping and clustering in Jeddah, Saudi Arabia / Abdulkader Murad in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

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Interactive display of surnames distributions in historic and contemporary Great Britain / Justin Van Dijk in Journal of maps, vol 16 n° 1 ([02/01/2020])  

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