Centre dedocumentation
scientifique

Prediction of suspended sediment concentration using hybrid SVM-WOA approaches / Sandeep Samantaray in Geocarto international, vol 37 n° 19 ([15/09/2022])  

Public [article]  inGeocarto international > vol 37 n° 19 [15/09/2022] . - pp 5609 - 5635 Titre : Prediction of suspended sediment concentration using hybrid SVM-WOA approaches Type de document : Article/Communication Auteurs : Sandeep Samantaray, Auteur ; Abinash Sahoo, Auteur Année de publication : 2022 Article en page(s) : pp 5609 - 5635 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] alluvion [Termes IGN] bassin hydrographique [Termes IGN] fonction de base radiale [Termes IGN] Inde [Termes IGN] modèle de simulation [Termes IGN] optimisation (mathématiques) [Termes IGN] optimisation par essaim de particules [Termes IGN] régression [Termes IGN] sédiment [Termes IGN] séparateur à vaste marge Résumé : (auteur) Suspended sediment concentration (SSC) is one of the primary reasons with respect to watersheds or river basins, which must be assessed in a correct manner so that it will help decision makers to make right decisions regarding hydraulic structure, flash-flood, flood-mitigation of the basin. The present research evaluated efficacy of a hybrid model integrating Support Vector Machine with Whale optimization algorithm (SVM-WOA) for predicting SSC at Sundargarh and Salebhata stations in Mahanadi River, India. Various quantitative statistical evaluation constrains are applied to evacuate the model performance. Also, model performance of SVM-WOA is compared with SVM-PSO (Particle Swarm Optimization) and conventional SVM and RBFN (Radial Basis Function Network) models. The results reveal that, SVM-WOA performed superiorly in comparison to SVM-PSO, SVM and RBFN models for five different input scenarios during both training and testing phases. Hence, it is recommended to apply SVM-WOA as an appropriate technique for hydrological simulation at the basin. Numéro de notice : A2022-707 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1920638 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1920638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101577 [article]

A general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101850 Titre : A general model for creating robust choropleth maps Type de document : Article/Communication Auteurs : Wangshu Mu, Auteur ; Daoqin Tong, Auteur Année de publication : 2022 Article en page(s) : n° 101850 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] carte choroplèthe [Termes IGN] incertitude des données [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] méthode robuste [Termes IGN] optimisation par essaim de particules [Termes IGN] programmation dynamique Résumé : (auteur) Choropleth maps visualize areal geographical data by grouping data into a few map classes and assigning different colors, shades, or patterns. Recent studies show that data uncertainty, commonly observed in real-life applications, should also be accounted for when determining the best classification scheme. Due to data uncertainty, a few studies note that map units might be placed in a wrong class, and the concept of map robustness has been introduced to minimize such misplacement. Recently, an algorithm has been developed to integrate robustness into the design of the optimal map classification scheme. However, the existing algorithm has two limitations: first, it is only suitable for certain robustness metrics. Second, when identifying the optimal class breaks, the existing algorithm requires predefined candidate class break values, which might lead to sub-optimal solutions. This paper resolves these issues by proposing a new model, namely, the Continuous Robust Map Classification Problem (CRMCP), and the associated solution approach. The CRMCP allows mapmakers to customize robustness metrics according to their data and applications. In addition, a particle swarm optimization algorithm is developed to solve the CRMCP. The model and algorithm are tested using American Community Survey data. Test results suggest that the new approach can find better solutions than the existing algorithm. The study improves the usability of choropleth maps when uncertain geographical attributes are involved and allows spatial analysts and decision-makers to incorporate robustness into the mapmaking process more flexibly. Numéro de notice : A2022-514 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101850 Date de publication en ligne : 28/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101850 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101055 [article]

Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol inconnu n° inconnu ([01/08/2022])  

Public [article]  inGeocarto international > vol inconnu n° inconnu [01/08/2022] Titre : Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data Type de document : Article/Communication Auteurs : Zhuomei Huang, Auteur ; Yichao Tian, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] Chine [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] mangrove [Termes IGN] optimisation par essaim de particules Résumé : (auteur) Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R2) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R2 = 0.7807, RMSE = 24.6864 Mg·ha−1), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha−1. Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves. Numéro de notice : A2022-621 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102226 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101356 [article]

Full-waveform classification and segmentation-based signal detection of single-wavelength bathymetric LiDAR / Xue Ji in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 4208714 Titre : Full-waveform classification and segmentation-based signal detection of single-wavelength bathymetric LiDAR Type de document : Article/Communication Auteurs : Xue Ji, Auteur ; Bisheng Yang, Auteur ; Yuan Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4208714 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme de Levenberg-Marquardt [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du signal [Termes IGN] forme d'onde pleine [Termes IGN] Hainan (Chine) [Termes IGN] lidar bathymétrique [Termes IGN] optimisation par essaim de particules Résumé : (auteur) Single-wavelength bathymetric light detection and ranging (LiDAR) (532 nm) can provide seamless meter- and submeter-scale digital elevation model (DEMs) of both the terrestrial surface and seafloor. However, mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and nonterrestrial waveforms from the original waveforms without auxiliary information and flexible detection of peaks based on a new FW theoretical model. A novel FW signal detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FWs are divided into five categories for subsequent signal detection using a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition, and a flexible signal detection framework. To search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than Levenberg–Marquardt algorithm optimized generalized Gaussian model (LM-GGM) and RLD algorithm. Numéro de notice : A2022-661 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3198168 Date de publication en ligne : 11/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3198168 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101517 [article]

Generating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes / Christian Kruse in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) Titre : Generating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes Type de document : Article/Communication Auteurs : Christian Kruse, Auteur ; Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme du recuit simulé [Termes IGN] chevauchement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] échantillonnage de données [Termes IGN] Europe centrale [Termes IGN] guerre [Termes IGN] image aérienne [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] méthode fondée sur le noyau [Termes IGN] processus ponctuel marqué [Termes IGN] processus stochastique Résumé : (auteur) Even more than 75 years after the Second World War, numerous unexploded bombs (duds) linger in the ground and pose a considerable hazard to society. The areas containing these duds are documented in so-called impact maps, which are based on locations of exploded bombs; these locations can be found in aerial images taken shortly after bombing. To generate impact maps, in this paper we present a novel approach based on marked point processes (MPPs) for the automatic detection of bomb craters in such images, some of which are overlapping. The object model for the craters is represented by circles and is embedded in the MPP-framework. By means of stochastic sampling, the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function that describes the consistency with a predefined object model. High gradient magnitudes along the object borders and homogeneous grey values inside the objects are favoured, while overlaps between objects are penalized. Reversible Jump Markov Chain Monte Carlo sampling, in combination with simulated annealing, provides the global optimum of the energy function. Our procedure allows the combination of individual detection results covering the same location. Afterwards, a probability map for duds is generated from the detections via kernel density estimation and areas around the detections are classified as contaminated, resulting in an impact map. Our results, based on 74 aerial wartime images taken over different areas in Central Europe, show the potential of the method; among other findings, a clear improvement is achieved by using redundant image information. We also compared the MPP method for bomb crater detection with a state-of-of-the-art convolutional neural network (CNN) for generating region proposals; it turned out that the CNN outperforms the MPPs if a sufficient amount of representative training data is available and a threshold for a region to be considered as crater is properly tuned prior to running the experiments. If this is not the case, the MPP approach achieves better results. Numéro de notice : A2022-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100017 Date de publication en ligne : 02/06/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101057 [article]

GNSSseg, a statistical method for the segmentation of daily GNSS IWV time series / Annarosa Quarello in Remote sensing, vol 14 n° 14 (July-2 2022)  

Permalink

Encoder-decoder structure with multiscale receptive field block for unsupervised depth estimation from monocular video / Songnan Chen in Remote sensing, Vol 14 n° 12 (June-2 2022)  

Permalink

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

Permalink

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

Permalink

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])  

Permalink

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

Permalink

A search step optimization in an ambiguity function-based GNSS precise positioning / Sławomir Cellmer in Survey review, vol 54 n° 383 (March 2022)  

Permalink

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])  

Permalink

A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)  

Permalink

GCN-Denoiser: mesh denoising with graph convolutional networks / Yuefan Shen in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)  

Permalink