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Automating the external placement of symbols for point features in situation maps for emergency response / Sven Gedicke in Cartography and Geographic Information Science, Vol 50 n° 4 (June 2023)
[article]
Titre : Automating the external placement of symbols for point features in situation maps for emergency response Type de document : Article/Communication Auteurs : Sven Gedicke, Auteur ; Lukas Arzoumanidis, Auteur ; Jan‐Henrik Haunert, Auteur Année de publication : 2023 Article en page(s) : pp 385 - 402 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme du recuit simulé
[Termes IGN] cartographie d'urgence
[Termes IGN] optimisation (mathématiques)
[Termes IGN] placement automatique des signes conventionnels
[Termes IGN] programmation linéaireRésumé : (auteur) In this article, we address the time-critical work of emergency services in the field of disaster and emergency response. Aiming at saving valuable human and time resources during emergency operations, we present one exact and one heuristic approach for the automatic placement of tactical symbols in situation maps. Such maps are used to establish situational awareness and to convey mission-relevant information to emergency personnel. Usually, the information is communicated through the visualization of descriptive symbols which are predominantly placed in a manual process. We automate this process based on an established map layout used by emergency services in Germany that distributes the symbols to the map boundaries. Following general principles and observations from existing literature, we formalize the symbol placement as an optimization problem. We take into account the relevance of tactical symbols as well as short and crossing-free leaders and allow the grouped representation of symbols of similar semantics and spatially close map locations. In experiments with real-world data, we determine a balance between the optimization criteria and show that our heuristic generates high-quality results in less than a second. In an assessment by an expert, we get confirmation that our maps are suitable for use in emergency scenarios. Numéro de notice : A2023-234 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2023.2213446 Date de publication en ligne : 20/06/2023 En ligne : https://doi.org/10.1080/15230406.2023.2213446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103592
in Cartography and Geographic Information Science > Vol 50 n° 4 (June 2023) . - pp 385 - 402[article]Global-aware siamese network for change detection on remote sensing images / Ruiqian Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 199 (May 2023)
[article]
Titre : Global-aware siamese network for change detection on remote sensing images Type de document : Article/Communication Auteurs : Ruiqian Zhang, Auteur ; Hanchao Zhang, Auteur ; Xiaogang Ning, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de sensibilité
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] détection de changement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Change detection (CD) in remote sensing images is one of the most important technical options to identify changes in observations in an efficient manner. CD has a wide range of applications, such as land use investigation, urban planning, environmental monitoring and disaster mapping. However, the frequently occurring class imbalance problem brings huge challenges to the change detection applications. To address this issue, we develop a novel global-aware siamese network (GAS-Net), aiming to generate global-aware features for efficient change detection by incorporating the relationships between scenes and foregrounds. The proposed GAS-Net, consisting of the global-attention module (GAM) and foreground-awareness module (FAM) that both learns contextual relationships and enhances symbiotic relation learning between scene and foreground. The experimental results demonstrate the effectiveness and robustness of the proposed GAS-Net, achieving up to 91.21% and 95.84% F1 score on two widely used public datasets, i.e., Levir-CD and Lebedev-CD dataset. The source code is available at https://github.com/xiaoxiangAQ/GAS-Net. Numéro de notice : 2023-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.04.001 Date de publication en ligne : 05/04/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103106
in ISPRS Journal of photogrammetry and remote sensing > vol 199 (May 2023) . - pp 61 - 72[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 38 n° inconnu ([01/01/2023])
[article]
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 : 2023 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 particulesRésumé : (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
in Geocarto international > vol 38 n° inconnu [01/01/2023][article]How to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data / Rongfang Lyu in Sustainable Cities and Society, vol 88 (January 2023)
[article]
Titre : How to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data Type de document : Article/Communication Auteurs : Rongfang Lyu, Auteur ; Jili Pang, Auteur ; Xiaolei Tian, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104287 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Chine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espace vert
[Termes IGN] hauteur du bâti
[Termes IGN] ilot thermique urbain
[Termes IGN] image captée par drone
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] Leaf Area Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] optimisation (mathématiques)
[Termes IGN] paysage urbain
[Termes IGN] plan d'eau
[Termes IGN] planification urbaine
[Termes IGN] réseau bayesien
[Termes IGN] semis de points
[Termes IGN] température au solRésumé : (auteur) The systematical exploration of how two-dimensional (2D) and three-dimensional (3D) features of urban landscapes influence land surface temperature (LST) is still limited. Therefore, we investigated the influence of three main urban landscapes—urban green space, impervious land, and water bodies on LST, with a particular focus on the 3D vegetation metrics of green volume (GV) and leaf area index (LAI). We used Yinchuan City, China, as a case study. We quantified the impacts of various 2D/3D metrics of the three landscape types on LST using a random forest analysis with multiple sources, including Unmanned Aerial Vehicle (UAV) and remote sensing images. We then generated a Bayesian Network (BN) model to identify the optimal configurations for each landscape type. We found that using 11 of the 31 metrics considered, our model could explain 81.8% of the observed variance in LST of Yinchuan City. Among those, water body metrics were the most important, followed by vegetation abundance, impervious land metrics, and landscape pattern of urban green space. The mean classification error of the BN model was only 22.9%. We suggest that this makes the BN model a promising support tool for urban planning with a view to urban heat island mitigation. Our findings also stress the importance of considering both 2D and 3D features when considering urban cooling strategies. Numéro de notice : A2023-007 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104287 Date de publication en ligne : 02/11/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104287 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102095
in Sustainable Cities and Society > vol 88 (January 2023) . - n° 104287[article]Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution / Estera Trzcina in Journal of geodesy, vol 97 n° 1 (January 2023)
[article]
Titre : Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution Type de document : Article/Communication Auteurs : Estera Trzcina, Auteur ; Witold Rohm, Auteur ; Kamil Smolak, Auteur Année de publication : 2023 Article en page(s) : n° 2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] données GNSS
[Termes IGN] interpolation bilinéaire
[Termes IGN] modèle météorologique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] radiosondage
[Termes IGN] récepteur GNSS
[Termes IGN] retard troposphérique
[Termes IGN] retard troposphérique zénithal
[Termes IGN] système de grille globale discrète
[Termes IGN] teneur en vapeur d'eau
[Termes IGN] tomographie
[Termes IGN] troposphèreRésumé : (auteur) Water vapour is a highly variable constituent of the troposphere; thus, its high-resolution measurements are of great importance to weather prediction systems. The Global Navigation Satellite Systems (GNSS) are operationally used in the estimation of the tropospheric state and assimilation of the results into the weather models. One of the GNSS techniques of troposphere sensing is tomography which provides 3-D fields of wet refractivity. The tomographic results have been successfully assimilated into the numerical weather models, showing the great potential of this technique. The GNSS tomography can be based on two different approaches to the parameterisation of the model’s domain, i.e. block (voxel-based) or grid (node-based) approach. Regardless of the parameterisation approach, the tomographic domain should be discretised, which is usually performed in a regular manner, with a grid resolution depending on the mean distance between the GNSS receivers. In this work, we propose a new parameterisation approach based on the optimisation of the tomographic nodes’ location, taking into account the non-uniform distribution of the GNSS information in the troposphere. The experiment was performed using a dense network of 16 low-cost multi-GNSS receivers located in Wrocław and its suburbs, with a mean distance of 3 km. Cross-validation of four different parameterisation approaches is presented. The validation is performed based on the Weather Research and Forecasting model as well as radiosonde observations. The new approach improves the results of wet refractivity estimation by 0.5–2 ppm in terms of RMSE, especially for altitudes of 0.5–2.0 km. Numéro de notice : A2023-044 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1007/s00190-022-01691-0 Date de publication en ligne : 30/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01691-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102343
in Journal of geodesy > vol 97 n° 1 (January 2023) . - n° 2[article]Automatic registration of point cloud and panoramic images in urban scenes based on pole matching / Yuan Wang in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkHybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkReconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)PermalinkSea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkA whale optimization algorithm–based cellular automata model for urban expansion simulation / Yuan Ding in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkAn improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao in Transactions in GIS, vol 26 n° 7 (November 2022)PermalinkA fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation / Xiaoguo Guan in Survey review, vol 54 n° 387 (November 2022)PermalinkMulti-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)PermalinkFlash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkPrediction of suspended sediment concentration using hybrid SVM-WOA approaches / Sandeep Samantaray in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkA general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)PermalinkFull-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)PermalinkGenerating 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)PermalinkGNSSseg, a statistical method for the segmentation of daily GNSS IWV time series / Annarosa Quarello in Remote sensing, vol 14 n° 14 (July-2 2022)PermalinkEncoder-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)PermalinkMulti-objective optimization of urban environmental system design using machine learning / Peiyuan Li in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkTrade-offs between sustainable development goals in systems of cities / Juste Raimbault in Journal of Urban Management, vol 11 n° 2 (June 2022)PermalinkNovel 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])PermalinkDeformation analysis: the modified GREDOD method / Mehmed Batilović in Geodetski vestnik, vol 66 n° 1 (March 2022)PermalinkA search step optimization in an ambiguity function-based GNSS precise positioning / Sławomir Cellmer in Survey review, vol 54 n° 383 (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])PermalinkA 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)PermalinkGCN-Denoiser: mesh denoising with graph convolutional networks / Yuefan Shen in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)PermalinkObject recognition algorithm based on optimized nonlinear activation function-global convolutional neural network / Feng-Ping An in The Visual Computer, vol 38 n° 2 (February 2022)PermalinkSynergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])PermalinkVariable selection for estimating individual tree height using genetic algorithm and random forest / Evandro Nunes Miranda in Forest ecology and management, vol 504 (January-15 2022)PermalinkPermalinkÉléments pour l'analyse et le traitement d'images : application à l'estimation de la qualité du bois / Rémy Decelle (2022)PermalinkFlood susceptibility mapping using meta-heuristic algorithms / Alireza Arabameri in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkFusion de données hyperspectrales et panchromatiques dans le domaine réflectif / Yohann Constans (2022)PermalinkGenetic diversity of sessile oak populations in the Czech Republic / Jakub Dvořák in Journal of forest science, vol 68 n° 1 (January 2022)PermalinkPermalinkOptimization of deep neural networks: A functional perspective with applications in image classification / Simon Roburin (2022)PermalinkRoad traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods / Sulaiman Yunus in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkPermalinkStudying informativeness of satellite image texture for sea ice state retrieval using deep learning methods / Clément Fougerouse (2022)PermalinkPermalinkFast estimation for robust supervised classification with mixture models / Erwan Giry Fouquet in Pattern recognition letters, vol 152 (December 2021)PermalinkImproving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network / Yajie Shi in International Journal of Remote Sensing IJRS, vol 42 n° 23 (1-10 December 2021)PermalinkParticle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkA topology-based graph data model for indoor spatial-social networking / Mahdi Rahimi in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)PermalinkSuperpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)PermalinkAdaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkAn internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images / Sihang Zhang in Geo-spatial Information Science, vol 24 n° 4 (October 2021)PermalinkEndmember bundle extraction based on multiobjective optimization / Rong Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)Permalink