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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)
[article]
Titre : Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions Type de document : Article/Communication Auteurs : Di Zhu, Auteur ; Yu Liu, Auteur ; Xin Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 645 - 676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse multivariée
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] distribution spatiale
[Termes IGN] échantillonnage
[Termes IGN] intelligence artificielle
[Termes IGN] régression
[Termes IGN] régression géographiquement pondérée
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal de graphesMots-clés libres : Geospatial artificial intelligence (GeoAI) Résumé : (auteur) Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. Numéro de notice : A2022-865 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-021-00454-x Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.1007/s10707-021-00454-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102158
in Geoinformatica > vol 26 n° 4 (October 2022) . - pp 645 - 676[article]Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)
[article]
Titre : Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding Type de document : Article/Communication Auteurs : Faxi Yuan, Auteur ; Yuanchang Xu, Auteur ; Qingchun Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101870 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] catastrophe naturelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] graphe
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] polynôme de Chebysheff
[Termes IGN] prévention des risques
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] zone urbaineRésumé : (auteur) The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans. Numéro de notice : A2022-656 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101870 Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101870 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101506
in Computers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101870[article]The iterative convolution–thresholding method (ICTM) for image segmentation / Dong Wang in Pattern recognition, vol 130 (October 2022)
[article]
Titre : The iterative convolution–thresholding method (ICTM) for image segmentation Type de document : Article/Communication Auteurs : Dong Wang, Auteur ; Xiaoping Wang, Auteur Année de publication : 2022 Article en page(s) : n° 108794 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] contour
[Termes IGN] convergence
[Termes IGN] filtrage numérique d'image
[Termes IGN] image à haute résolution
[Termes IGN] itération
[Termes IGN] segmentation d'image
[Termes IGN] seuillageRésumé : (auteur) Variational methods, which have been tremendously successful in image segmentation, work by minimizing a given objective functional. The objective functional usually consists of a fidelity term and a regularization term. Because objective functionals may vary from different types of images, developing an efficient, simple, and general numerical method to minimize them has become increasingly vital. However, many existing methods are model-based, converge relatively slowly, or involve complicated techniques. In this paper, we develop a novel iterative convolution–thresholding method (ICTM) that is simple, efficient, and applicable to a wide range of variational models for image segmentation. In ICTM, the interface between two different segment domains is implicitly represented by the characteristic functions of domains. The fidelity term is usually written into a linear functional of the characteristic functions, and the regularization term is approximated by a functional of characteristic functions in terms of heat kernel convolution. This allows us to design an iterative convolution–thresholding method to minimize the approximate energy. The method has the energy-decaying property, and thus the unconditional stability is theoretically guaranteed. Numerical experiments show that the method is simple, easy to implement, robust, and applicable to various image segmentation models. Numéro de notice : A2022-779 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2022.108794 Date de publication en ligne : 14/05/2022 En ligne : https://doi.org/10.1016/j.patcog.2022.108794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101857
in Pattern recognition > vol 130 (October 2022) . - n° 108794[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]Comparison of deep neural networks in detecting field grapevine diseases using transfer learning / Antonios Morellos in Remote sensing, vol 14 n° 18 (September-2 2022)
[article]
Titre : Comparison of deep neural networks in detecting field grapevine diseases using transfer learning Type de document : Article/Communication Auteurs : Antonios Morellos, Auteur ; Xanthoula Eirini Pantazi, Auteur ; Charalampos Paraskevas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4648 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Grèce
[Termes IGN] jeu de données
[Termes IGN] maladie cryptogamique
[Termes IGN] maladie phytosanitaire
[Termes IGN] viticultureRésumé : (auteur) Plants diseases constitute a substantial threat for farmers given the high economic and environmental impact of their treatment. Detecting possible pathogen threats in plants based on non-destructive remote sensing and computer vision methods offers an alternative to existing laboratory methods and leads to improved crop management. Vine is an important crop that is mainly affected by fungal diseases. In this study, photos from healthy leaves and leaves infected by a fungal disease of vine are used to create disease identification classifiers. The transfer learning technique was employed in this study and was used to train three different deep convolutional neural network (DCNN) approaches that were compared according to their classification accuracy, namely AlexNet, VGG-19, and Inception v3. The above-mentioned models were trained on the open-source PlantVillage dataset using two training approaches: feature extraction, where the weights of the base deep neural network model were frozen and only the ones on the newly added layers were updated, and fine tuning, where the weights of the base model were also updated during training. Then, the created models were validated on the PlantVillage dataset and retrained using a custom field-grown vine photo dataset. The results showed that the fine-tuning approach showed better validation and testing accuracy, for all DCNNs, compared to the feature extraction approach. As far as the results of DCNNs are concerned, the Inception v3 algorithm outperformed VGG-19 and AlexNet in almost all the cases, demonstrating a validation performance of 100% for the fine-tuned strategy on the PlantVillage dataset and an accuracy of 83.3% for the respective strategy on a custom vine disease use case dataset, while AlexNet achieved 87.5% validation and 66.7% accuracy for the respective scenarios. Regarding VGG-19, the validation performance reached 100%, with an accuracy of 76.7%. Numéro de notice : A2022-768 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14184648 Date de publication en ligne : 17/09/2022 En ligne : https://doi.org/10.3390/rs14184648 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101794
in Remote sensing > vol 14 n° 18 (September-2 2022) . - n° 4648[article]Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkForest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (September-15 2022)PermalinkRegional 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)PermalinkAdaptive 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)PermalinkAn 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)PermalinkAnalytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (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)PermalinkClassification of pine wilt disease at different infection stages by diagnostic hyperspectral bands / Niwen Li in Ecological indicators, vol 142 (September 2022)PermalinkCrowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])PermalinkDeep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)PermalinkDeep learning method for Chinese multisource point of interest matching / Pengpeng Li in Computers, Environment and Urban Systems, vol 96 (September 2022)PermalinkFlood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)PermalinkForest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)PermalinkHuman perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms / Yunhao Li in Transactions in GIS, vol 26 n° 6 (September 2022)PermalinkIdentification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators / Luis Izquierdo-Horna in Computers, Environment and Urban Systems, vol 96 (September 2022)PermalinkLearning indoor point cloud semantic segmentation from image-level labels / Youcheng Song in The Visual Computer, vol 38 n° 9 (September 2022)PermalinkMapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)PermalinkMapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)PermalinkPoint-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)PermalinkStructured binary neural networks for image recognition / Bohan Zhuang in International journal of computer vision, vol 130 n° 9 (September 2022)Permalink