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Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])
[article]
Titre : Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data Type de document : Article/Communication Auteurs : Saeideh Sahebi Vayghan, Auteur ; Mohammad Salmani, Auteur ; Neda Ghasemkhanic, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2967 - 2995 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme génétique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'arbres
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] empreinte
[Termes IGN] image aérienne
[Termes IGN] image optique
[Termes IGN] Inférence floue
[Termes IGN] morphologie mathématiqueRésumé : (auteur) One of the most important considerations in urban environments is the extraction of urban objects, with a high automation level. This study aims to present a new method which uses aerial images and LiDAR data to extract buildings and trees footprint in urban areas. In this study, high-elevation objects were extracted from the LiDAR data using the developed scan labeling method, and then the classification methods of Neural Networks (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Based K-Means algorithm (GBKMs) were used to separate buildings and trees and with the purpose of evaluating their performance. The features used for the classification were extracted from aerial images and LiDAR data, and the training data for the classification were selected automatically. Mathematical morphology functions were also used to process the classification results. The results show that NN and the ANFIS are more effective than the genetic-based K-Means algorithm in detecting small and large buildings. Numéro de notice : A2022-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1844311 En ligne : https://doi.org/10.1080/10106049.2020.1844311 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101300
in Geocarto international > vol 37 n° 10 [01/06/2022] . - pp 2967 - 2995[article]Multi-objective optimization of urban environmental system design using machine learning / Peiyuan Li in Computers, Environment and Urban Systems, vol 94 (June 2022)
[article]
Titre : Multi-objective optimization of urban environmental system design using machine learning Type de document : Article/Communication Auteurs : Peiyuan Li, Auteur ; Tianfang Xu, Auteur ; Shiqi Wei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101796 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] dioxyde de carbone
[Termes IGN] ilot thermique urbain
[Termes IGN] indicateur environnemental
[Termes IGN] milieu urbain
[Termes IGN] optimisation (mathématiques)
[Termes IGN] planification urbaine
[Termes IGN] processus gaussien
[Termes IGN] régression
[Termes IGN] végétationRésumé : (auteur) The efficacy of urban mitigation strategies for heat and carbon emissions relies heavily on local urban characteristics. The continuous development and improvement of urban land surface models enable rather accurate assessment of the environmental impact on urban development strategies, whereas physically-based simulations remain computationally costly and time consuming, as a consequence of the increasing complexity of urban system dynamics. Hence it is imperative to develop fast, efficient, and economic operational toolkits for urban planners to foster the design, implementation, and evaluation of urban mitigation strategies, while retaining the accuracy and robustness of physical models. In this study, we adopt a machine learning (ML) algorithm, viz. Gaussian Process Regression, to emulate the physics of heat and biogenic carbon exchange in the built environment. The ML surrogate is trained and validated on the simulation results generated by a state-of-the-art single-layer urban canopy model over a wide range of urban characteristics, showing high accuracy in capturing heat and carbon dynamics. Using the validated surrogate model, we then conduct multi-objective optimization using the genetic algorithm to optimize urban design scenarios for desirable urban mitigation effects. While the use of urban greenery is found effective in mitigating both urban heat and carbon emissions, there is manifest trade-offs among ameliorating diverse urban environmental indicators. Numéro de notice : A2022-244 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101796 Date de publication en ligne : 18/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100184
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101796[article]Trade-offs between sustainable development goals in systems of cities / Juste Raimbault in Journal of Urban Management, vol 11 n° 2 (June 2022)
[article]
Titre : Trade-offs between sustainable development goals in systems of cities Type de document : Article/Communication Auteurs : Juste Raimbault, Auteur ; Denise Pumain, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 237 - 245 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme génétique
[Termes IGN] croissance urbaine
[Termes IGN] développement durable
[Termes IGN] modèle dynamique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] urbanisme
[Termes IGN] ville durableRésumé : (auteur) Sustainable Development Goals are intrinsically competing, and their embedding into urban systems furthermore emphasises such compromises. When observed at the scale of systems of cities, such concern is considered as a series of innovations that challenges the adaptive capacity of urban systems. The spatial complexity, the non-optimal nature of such systems, and the multi-objective aspects of their agents, are among the reasons that raise difficulties when trying to adjust local policies through promoting innovation in order to satisfy at least a couple of SDGs simultaneously. As we lack enough empirical evidence, we propose in this paper to use a stylised simulation model for systems of cities, focused on innovation diffusion and population dynamics, to show how trade-offs may operate at such a scale. We proceed in particular to a bi-objective optimisation of emissions and innovation utilities, and show that no single urban optimum exists, but a diversity of regimes forming a compromise between the two objectives. Numéro de notice : A2022-686 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jum.2022.05.008 En ligne : https://doi.org/10.1016/j.jum.2022.05.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101598
in Journal of Urban Management > vol 11 n° 2 (June 2022) . - pp 237 - 245[article]Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping / A'Kif Al-Fugara in Geocarto international, vol 37 n° 9 ([15/05/2022])
[article]
Titre : Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping Type de document : Article/Communication Auteurs : A'Kif Al-Fugara, Auteur ; Mohammad Ahmadlou, Auteur ; Rania Shatnawi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2627 - 2646 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] algorithme du recuit simulé
[Termes IGN] algorithme génétique
[Termes IGN] analyse comparative
[Termes IGN] carte hydrogéologique
[Termes IGN] eau souterraine
[Termes IGN] Jordanie
[Termes IGN] méthode heuristique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régressionRésumé : (auteur) This study aims to develop three novel GIS-based models combining Genetic Algorithm (GA), Biogeography-Based Optimization (BBO) and Simulated Annealing (SA) with Support Vector Regression (SVR) for groundwater potential (GP) mapping in the governorate of Tafillah, Jordan. Twelve topographical, hydrological and geological factors were considered. The mapping process was done with and without feature selection (FS) conducted by integration of SVR model with GA, BBO and SA algorithms. The accuracy of these models was evaluated using the area under receiver operating characteristic (AUROC) curve. Comparisons among the models uncovered that the SVR-RBF-GA and SVR-RBF-BBO models performed better than the SVR-RBF-SA. The AUROC for two mentioned models were 0.964 and 0.996 in training and testing runs, respectively, while this metric was 0.953 and 0.986 for SVR-RBF-SA model in training and testing runs, respectively. The results showed that after FS, the models are more accurate in test data than train data. Numéro de notice : A2022-567 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831622 Date de publication en ligne : 19/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831622 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101250
in Geocarto international > vol 37 n° 9 [15/05/2022] . - pp 2627 - 2646[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2022091 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of neural networks and k-nearest neighbors methods in forest stand variable estimation using airborne laser data / Andras Balazs in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)
[article]
Titre : Comparison of neural networks and k-nearest neighbors methods in forest stand variable estimation using airborne laser data Type de document : Article/Communication Auteurs : Andras Balazs, Auteur ; Eero Liski, Auteur ; Sakari Tuominen, Auteur Année de publication : 2022 Article en page(s) : n° 100012 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme génétique
[Termes IGN] bois sur pied
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] covariance
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] peuplement forestier
[Termes IGN] réseau neuronal artificiel
[Termes IGN] semis de points
[Termes IGN] volume en boisRésumé : (auteur) In the remote sensing of forests, point cloud data from airborne laser scanning contains high-value information for predicting the volume of growing stock and the size of trees. At the same time, laser scanning data allows a very high number of potential features that can be extracted from the point cloud data for predicting the forest variables. In some methods, the features are first extracted by user-defined algorithms and the best features are selected based on supervised learning, whereas both tasks can be carried out automatically by deep learning methods typically based on deep neural networks. In this study we tested k-nearest neighbor method combined with genetic algorithm (k-NN), artificial neural network (ANN), 2-dimensional convolutional neural network (2D-CNN) and 3-dimensional CNN (3D-CNN) for estimating the following forest variables: volume of growing stock, stand mean height and mean diameter. The results indicate that there were no major differences in the accuracy of the tested methods, but the ANN and 3D-CNN generally resulted in the lowest RMSE values for the predicted forest variables and the highest R2 values between the predicted and observed forest variables. The lowest RMSE scores were 20.3% (3D-CNN), 6.4% (3D-CNN) and 11.2% (ANN) and the highest R2 results 0.90 (3D-CNN), 0.95 (3D-CNN) and 0.85 (ANN) for volume of growing stock, stand mean height and mean diameter, respectively. Covariances of all response variable combinations and all predictions methods were lower than corresponding covariances of the field observations. ANN predictions had the highest covariances for mean height vs. mean diameter and total growing stock vs. mean diameter combinations and 3D-CNN for mean height vs. total growing stock. CNNs have distinct theoretical advantage over the other methods in complex recognition or classification tasks, but the utilization of their full potential may possibly require higher point density clouds than applied here. Thus, the relatively low density of the point clouds data may have been a contributing factor to the somewhat inconclusive ranking of the methods in this study. The input data and computer codes are available at: https://github.com/balazsan/ALS_NNs. Numéro de notice : A2022-265 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2022.100012 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100263
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 4 (April 2022) . - n° 100012[article]Deformation analysis: the modified GREDOD method / Mehmed Batilović in Geodetski vestnik, vol 66 n° 1 (March 2022)PermalinkSuspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms / Marzieh Fadaee in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkFlood susceptibility mapping using meta-heuristic algorithms / Alireza Arabameri in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkModeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)PermalinkRandom forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkIncreasing efficiency of the robust deformation analysis methods using genetic algorithm and generalised particle swarm optimisation / Mehmed Batilović in Survey review, Vol 53 n° 378 (May 2021)PermalinkA novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm / Sara Khanbani in Applied geomatics, vol 13 n° 1 (May 2021)PermalinkA Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm / Vorapong Suppakitpaisarn in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkPermalinkPermalinkAdvanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations / Wang Li in Remote sensing, vol 12 n° 5 (March 2020)PermalinkAnalysing performance of SLEUTH model calibration using brute force and genetic algorithm–based methods / Ankita Saxena in Geocarto international, vol 35 n° 3 ([01/03/2020])PermalinkPermalinkPermalinkEvolutionary approach for detection of buried remains using hyperspectral images / Leon Dozal in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 7 (juillet 2018)PermalinkA geometric-based approach for road matching on multi-scale datasets using a genetic algorithm / Alireza Chehreghan in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)PermalinkDétermination du géopotentiel à haute résolution spatiale : apport des horloges atomiques et des algorithmes génétiques / Guillaume Lion (2018)PermalinkHybrid image noise reduction algorithm based on genetic ant colony and PCNN / Chong Shen in The Visual Computer, vol 33 n° 11 (November 2017)PermalinkDenoising of natural images through robust wavelet thresholding and genetic programming / Asem Khmag in The Visual Computer, vol 33 n°9 (September 2017)PermalinkModeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkA hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model / Rachel Whitsed in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkPermalinkAn immune genetic algorithm to buildings displacement in cartographic generalization / Yageng Sun in Transactions in GIS, vol 20 n° 4 (August 2016)PermalinkAutonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm / Pramod Kumar Konugurthi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)PermalinkOptimisation d'un service d'autopartage de véhicules électriques / Amine Ait-Ouahmed in Revue internationale de géomatique, vol 26 n° 2 (avril - juin 2016)PermalinkPermalinkOptimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm / Nan Mi in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)PermalinkClassification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkGlobal optimization of GNSS station reference networks / David Coulot in GPS solutions, vol 19 n° 4 (october 2015)PermalinkApplication d'algorithmes génétiques à la détermination d'orbites optimales pour GRASP / Arnaud Pollet in XYZ, n° 144 (septembre - novembre 2015)PermalinkRegional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization, a case study in Caidian District, Wuhan, China / Man Yuan in Cartography and Geographic Information Science, vol 41 n° 4 (September 2014)PermalinkOrbit computation of the TELECOM-2D satellite with a genetic algorithm / Florent Deleflie in Proceedings of the International astronomical union, vol 9 S310 (Juillet 2014)PermalinkActive learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes / Christos Yiakoumettis in Geoinformatica, vol 18 n° 1 (January 2014)PermalinkPermalinkRecherche des sous-réseaux d’antennes VLBI et de radio‐sources extra‐galactiques par algorithmes génétiques / Serge Nyoka (2014)PermalinkPermalinkClassification automatique des images satellitaires optimisée par l'algorithme des chauves-souris / Soumia Benmostefa in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkUtility of the wavelet transform for LAI estimation using hyperspectral data / Asim Banskota in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 7 (July 2013)PermalinkOptimisation de transport à la demande dans des territoires polarisés / Rémy Chevrier in Cartes & Géomatique, n° 215 (mars 2013)Permalink2D arrangement-based hierarchical spatial partitioning: an application to pedestrian network generation / Murat Yirci (2013)PermalinkFirst attempt of orbit determination of SLR satellites and space debris using genetic algorithms / Florent Deleflie (2013)PermalinkPermalinkFusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes / J. Im in Geocarto international, vol 27 n° 5 (August 2012)PermalinkApplication des algorithmes génétiques à la recherche de sous-réseaux de stations de télémétrie laser / David Coulot in Bulletin d'information scientifique et technique de l'IGN, n° 77 (avril 2011)Permalink