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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])  

Public [article]  inGeocarto international > vol 37 n° 19 [15/09/2022] . - pp 5716 - 5741 Titre : Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood Type de document : Article/Communication Auteurs : Amid Darabi, Auteur ; Omid Rahmati, Auteur ; Seyed Amir Naghibi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 5716 - 5741 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] aléa [Termes IGN] apprentissage automatique [Termes IGN] cartographie des risques [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par Perceptron multicouche [Termes IGN] écoulement des eaux [Termes IGN] inondation [Termes IGN] Iran [Termes IGN] simulation spatiale [Termes IGN] zone urbaine Résumé : (auteur) In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models. Numéro de notice : A2022-708 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1920629 Date de publication en ligne : 13/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1920629 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101578 [article]

Deep learning method for Chinese multisource point of interest matching / Pengpeng Li in Computers, Environment and Urban Systems, vol 96 (September 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101821 Titre : Deep learning method for Chinese multisource point of interest matching Type de document : Article/Communication Auteurs : Pengpeng Li, Auteur ; Jiping Liu, Auteur ; An Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] appariement sémantique [Termes IGN] apprentissage profond [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] inférence sémantique [Termes IGN] information sémantique [Termes IGN] point d'intérêt [Termes IGN] représentation vectorielle [Termes IGN] traitement du langage naturel Résumé : (auteur) Multisource point of interest (POI) matching refers to the pairing of POIs that refer to the same geographic entity in different data sources. This also constitutes the core issue in geospatial data fusion and update. The existing methods cannot effectively capture the complex semantic information from a text, and the manually defined rules largely affect matching results. This study developed a multisource POI matching method based on deep learning that transforms the POI pair matching problem into a binary classification problem. First, we used three different Chinese word segmentation methods to segment the POI text attributes and used the segmentation results to train the Word2Vec model to generate the corresponding word vector representation. Then, we used the text convolutional neural network (Text-CNN) and multilayer perceptron (MLP) to extract the POI attributes' features and generate the corresponding feature vector representation. Finally, we used the enhanced sequential inference model (ESIM) to perform local inference and inference combination on each attribute to realize the classification of POI pairs. We used the POI dataset containing Baidu Map, Tencent Map, and Gaode Map from Chengdu to train, verify, and test the model. The experimental results show that the matching precision, recall rate, and F1 score of the proposed method exceed 98% on the test set, and it is significantly better than the existing matching methods. Numéro de notice : A2022-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101821 Date de publication en ligne : 18/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101053 [article]

Flood 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)  

Public [article]  inNatural Hazards > vol 113 n° 2 (September 2022) . - pp 1043 - 1081 Titre : Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach Type de document : Article/Communication Auteurs : Quoc Bao Pham, Auteur ; Sk Ajim Ali, Auteur ; Elzbieta Bielecka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1043 - 1081 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] apprentissage profond [Termes IGN] cartographie des risques [Termes IGN] classification par Perceptron multicouche [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] prévention des risques [Termes IGN] processus de hiérarchisation analytique [Termes IGN] régression logistique [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] Varsovie (Pologne) [Termes IGN] vulnérabilité [Termes IGN] zone urbaine dense Résumé : (auteur) Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events and risk. Therefore, this study was undertaken to develop a flood vulnerability map and to assess the exposure of buildings to flood risk in Warsaw, the capital of Poland. This goal was pursued in four research phases. The thirteen flood predictors were evaluated using information gain ratio (IGR), and finally reduced to eight of the most causative ones and used for flood vulnerability mapping with three machine learning algorithms, Artificial Neural Network Multi-Layer Perceptron (ANN/MLP), Deep Learning Neural Network based approach—DL4j (DLNN-DL4j) and Bayesian Logistic Regression (BLR). These algorithms show a good predictive performance with the receiver operating curve (ROC) value of 0.851, 0.877 and 0.697, respectively. The buildings’ exposure to flood was assessed in line with criteria established in European and national legal regulations. The introduced new buildings' flood hazard index (BFH) revealed a significant similarity of potential flood risk for both models, highlighting the greatest risk in zones with high vulnerability to flooding. Depending on the method used, the BFH value was 0.54 (ANN), 0.52 (DLNNs) or 0.64 (BLR). The holistic approach proposed in this study could assist local authorities in improving flood management. Numéro de notice : A2022-705 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s11069-022-05336-5 Date de publication en ligne : 05/04/2022 En ligne : https://doi.org/10.1007/s11069-022-05336-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101569 [article]

A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])  

Public [article]  inGeocarto international > vol 37 n° 14 [20/07/2022] . - pp 4141 - 4157 Titre : A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway Type de document : Article/Communication Auteurs : Reza Sanayeia, Auteur ; Alireza Vafaeinejad, Auteur ; Jalal Karami, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4141 - 4157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] accident de la route [Termes IGN] autocorrélation [Termes IGN] autoroute [Termes IGN] classification par Perceptron multicouche [Termes IGN] modèle de simulation [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] Téhéran [Termes IGN] transformation en ondelettes Résumé : (auteur) The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method, initially, the raw collision data were analyzed, normalized, and classified via Geographical Information System (GIS). Partial Autocorrelation Function (PACF) was also utilized to evaluate the temporal autocorrelation for consecutive existing daily data. The results of this study showed that the proposed integrated DWT-ANN method provided higher predictive accuracy in daily traffic collision than ANN model by increasing coefficient of determination (R2) from 0.66 to 0.82. Numéro de notice : A2022-650 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1871669 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.1080/10106049.2021.1871669 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101472 [article]

A graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102735 Titre : A graph attention network for road marking classification from mobile LiDAR point clouds Type de document : Article/Communication Auteurs : Lina Fang, Auteur ; Tongtong Sun, Auteur ; Shuang Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par Perceptron multicouche [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] noeud [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] signalisation routière Résumé : (auteur) The category of road marking is a crucial element in Mobile laser scanning systems’ (MLSs) applications such as intelligent traffic systems, high-definition maps, location and navigation services. Due to the complexity of road scenes, considerable and various categories, occlusion and uneven intensities in MLS point clouds, finely road marking classification is considered as the challenging work. This paper proposes a graph attention network named GAT_SCNet to simultaneously group the road markings into 11 categories from MLS point clouds. Concretely, the proposed GAT_SCNet model constructs serial computable subgraphs and fulfills a multi-head attention mechanism to encode the geometric, topological, and spatial relationships between the node and neighbors to generate the distinguishable descriptor of road marking. To assess the effectiveness and generalization of the GAT_SCNet model, we conduct extensive experiments on five test datasets of about 100 km in total captured by different MLS systems. Three accuracy evaluation metrics: average Precision, Recall, and of 11 categories on the test datasets exceed 91%, respectively. Accuracy evaluations and comparative studies show that our method has achieved a new state-of-the-art work on road marking classification, especially on similar linear road markings like stop lines, zebra crossings, and dotted lines. Numéro de notice : A2022-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.jag.2022.102735 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100124 [article]

Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)  

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Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image / Efosa Gbenga Adagbasa in Geocarto international, vol 37 n° 1 ([01/01/2022])  

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Classification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)  

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Monitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)  

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Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)  

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Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])  

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Cloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)  

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Amélioration des systèmes de suivi des cultures à l’aide de la télédétection multi-source et des techniques d’apprentissage profond / Yawogan Gbodjo (2021)  

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