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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])  

Public [article]  inGeocarto international > vol 37 n° 10 [01/06/2022] . - pp 2967 - 2995 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 mathématique Résumé : (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 [article]

Multi-objective optimization of urban environmental system design using machine learning / Peiyuan Li in Computers, Environment and Urban Systems, vol 94 (June 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101796 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] végétation Résumé : (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 [article]

Trade-offs between sustainable development goals in systems of cities / Juste Raimbault in Journal of Urban Management, vol 11 n° 2 (June 2022)  

Public [article]  inJournal of Urban Management > vol 11 n° 2 (June 2022) . - pp 237 - 245 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 durable Résumé : (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 [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])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2627 - 2646 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] régression Résumé : (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 [article]

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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)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 4 (April 2022) . - n° 100012 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 bois Résumé : (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 [article]

Deformation analysis: the modified GREDOD method / Mehmed Batilović in Geodetski vestnik, vol 66 n° 1 (March 2022)  

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Suspended 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])  

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Flood susceptibility mapping using meta-heuristic algorithms / Alireza Arabameri in Geomatics, Natural Hazards and Risk, vol 13 (2022)  

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Modeling 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)  

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Random 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)  

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Increasing 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)  

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A 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)  

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A 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)  

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Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)  

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Advances in modeling and management of urban water networks / Enrico Creaco (2021)  

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