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Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation / Haowei Zeng in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)
[article]
Titre : Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation Type de document : Article/Communication Auteurs : Haowei Zeng, Auteur ; Qing Zhu, Auteur ; Yulin Ding, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aléa
[Termes IGN] cartographie des risques
[Termes IGN] cohérence des données
[Termes IGN] effondrement de terrain
[Termes IGN] prédiction
[Termes IGN] programmation par contraintes
[Termes IGN] réseau neuronal de graphes
[Termes IGN] vulnérabilitéRésumé : (auteur) In complex and heterogeneous geoenvironments, landslides exhibit varying features in different environments, and data in landslide inventories are imbalanced. Existing data-driven landslide susceptibility evaluation (LSE) methods overlook environmental heterogeneity and cannot reliably predict regions with few samples. Alternatively, global random negative sampling strategies may produce imbalanced positive and negative samples in some environments, contributing to inaccurate predictions. This article proposes a graph neural network (GNN) constrained by environmental consistency (GNN-EC) to overcome these problems. The GNN-EC consists of graphs with nodes, and edges. A graph represents the environmental relationships in the study area. Nodes are geographic units delineated from terrain polygon approximation. Edges capture the relationships between node-pairs. Additionally, the weights of edges reflect the similarity between two node environments. A GNN aggregates node information in the graph for LSE. Our experiment showed that the proposed method outperformed the common machine learning methods: increasing prediction accuracy by approximately 7, 5–6 and 3–4% compared to the artificial neural network (ANN), the support vector machine (SVM) and the random forest (RF), respectively. Moreover, our method can maintain high prediction accuracy, even with a small training set. Numéro de notice : A2022-626 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2103819 Date de publication en ligne : 28/07/2022 En ligne : https://doi.org/10.1080/13658816.2022.2103819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101396
in International journal of geographical information science IJGIS > vol 36 n° 11 (November 2022)[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2022111 SL Revue Centre de documentation Revues en salle Disponible An estimation method to reduce complete and partial nonresponse bias in forest inventory / James A. Westfall in European Journal of Forest Research, vol 141 n° 5 (October 2022)
[article]
Titre : An estimation method to reduce complete and partial nonresponse bias in forest inventory Type de document : Article/Communication Auteurs : James A. Westfall, Auteur Année de publication : 2022 Article en page(s) : pp 901 - 907 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] enquête
[Termes IGN] erreur systématique
[Termes IGN] estimateur
[Termes IGN] estimation statistique
[Termes IGN] Etats-Unis
[Termes IGN] incertitude des données
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de simulation
[Termes IGN] placette d'échantillonnage
[Termes IGN] post-stratification de données
[Termes IGN] propriété foncière
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Survey practitioners commonly encounter various types of nonresponse and strive to implement methods that mitigate any resulting bias when reporting results. In national forest inventories (NFI), complete or partial nonresponse usually results from hazardous conditions or lack of plot access permission. While many factors may be related to nonresponse, the two primary factors in the NFI of the USA are public/private land ownership and office/field plot status. To ameliorate potential nonresponse bias, these factors should be accounted for in the estimation process. An estimation method is presented where response homogeneity groups (RHGs) account for differential nonresponse rates between forest/nonforest plots. In a post-stratified estimation context, ratio-to-size estimators are used in RHGs within post-strata to avoid potential bias in variance estimates arising from partial plot nonresponse. Combining RHGs within post-strata requires a complex variance estimator that includes four sources of uncertainty. Testing of the estimation method on a synthetic population showed the approach is essentially unbiased. Application to NFI data from 10 states in the USA consistently showed the RHG method produced state-level estimates of forestland area that were 0.1%–3.6% larger than the current post-stratified estimation procedure. It is suggested that these differences are indicative of the nonresponse bias present when plots having differential nonresponse rates are not accounted for. Numéro de notice : A2022-759 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1007/s10342-022-01480-6 Date de publication en ligne : 14/07/2022 En ligne : https://doi.org/10.1007/s10342-022-01480-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101770
in European Journal of Forest Research > vol 141 n° 5 (October 2022) . - pp 901 - 907[article]3D LiDAR aided GNSS/INS integration fault detection, localization and integrity assessment in urban canyons / Zhipeng Wang in Remote sensing, vol 14 n° 18 (September-2 2022)
[article]
Titre : 3D LiDAR aided GNSS/INS integration fault detection, localization and integrity assessment in urban canyons Type de document : Article/Communication Auteurs : Zhipeng Wang, Auteur ; Bo Li, Auteur ; Zhiqiang Dan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4641 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canyon urbain
[Termes IGN] couplage GNSS-INS
[Termes IGN] détection de cible
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur de positionnement
[Termes IGN] filtre adaptatif
[Termes IGN] intégration de données
[Termes IGN] intégrité des données
[Termes IGN] khi carré
[Termes IGN] semis de pointsRésumé : (auteur) The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced. Numéro de notice : A2022-769 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.3390/rs14184641 Date de publication en ligne : 16/09/2022 En ligne : https://doi.org/10.3390/rs14184641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101795
in Remote sensing > vol 14 n° 18 (September-2 2022) . - n° 4641[article]A general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)
[article]
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 dynamiqueRésumé : (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
in Computers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101850[article]A geographical and content-based approach to prioritize relevant and reliable tweets for emergency management / A. Marcela Suarez in Cartography and Geographic Information Science, Vol 49 n° 5 (September 2022)
[article]
Titre : A geographical and content-based approach to prioritize relevant and reliable tweets for emergency management Type de document : Article/Communication Auteurs : A. Marcela Suarez, Auteur ; Keith C. Clarke, Auteur Année de publication : 2022 Article en page(s) : pp 443 - 463 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] catastrophe naturelle
[Termes IGN] classement
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] Etats-Unis
[Termes IGN] fiabilité des données
[Termes IGN] filtrage d'information
[Termes IGN] gestion de crise
[Termes IGN] pertinence
[Termes IGN] qualité des données
[Termes IGN] secours d'urgence
[Termes IGN] tempête
[Termes IGN] TwitterRésumé : (auteur) Tweets posted by the general public during disaster events represent timely, up-to-date, and on-site data that may be useful for emergency responders. However, since Twitter data has been deemed to be unverifiable and untrustworthy, it is challenging to identify those reliable and relevant tweets that can inform emergency response operations. Although computational methods exist both to classify overwhelming amounts of tweets and to filter those relevant to emergency response, using contextual geographic information regarding the disaster event to filter tweets has been overlooked. We review the existing research on the quality of data contributed by the general public from a geographical perspective, and then propose an approach to prioritize tweets for emergency response based on their relevance and reliability. The novelty of the approach is twofold: a) the use of both authoritative data such as hazard-related information and on-the-ground reports provided by weather spotters and validated by the National Weather Service; and b) the fact that it leverages tweets content as well as their geographical context and location. Using Hurricane Harvey in 2017 as a case study, results show that by following the proposed approach 79% of tweets sent from post-identified flooded areas were classified as of high or medium relevance and reliability. This suggests that the proposed approach can provide an accurate prioritization of tweets to be used for real time emergency management. Numéro de notice : A2022-633 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2022.2081257 En ligne : https://doi.org/10.1080/15230406.2022.2081257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101399
in Cartography and Geographic Information Science > Vol 49 n° 5 (September 2022) . - pp 443 - 463[article]Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil / Adrián Pascual in Ecological Informatics, vol 70 (September 2022)PermalinkModeling and propagating inventory-based sampling uncertainty in the large-scale forest demographic model “MARGOT” / Timothée Audinot in Natural Resource Modelling, vol 35 n° 3 (August 2022)PermalinkUncertainty interval estimates for computing slope and aspect from a gridded digital elevation model / Carlos López-Vázquez in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)PermalinkEvaluation of the GSRM2.1 and the NUVEL1-A values in Europe using SLR and VLBI based geodetic velocity fields / Mina Rahmani in Survey review, vol 54 n° 385 (July 2022)PermalinkOutliers and uncertainties in GNSS ZTD estimates from double-difference processing and precise point positioning / Katarzyna Stępniak in GPS solutions, vol 26 n° 3 (July 2022)PermalinkA participatory trail web map based on open source technologies / Joshua Gore in International journal of cartography, vol 8 n° 2 (July 2022)PermalinkQuantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest / Thangavelu Mayamanikandan in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkConstraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)PermalinkMultipurpose temporal GIS model for cadastral data management / Joseph Mango in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkOn the consistency of coastal sea-level measurements in the Mediterranean Sea from tide gauges and satellite radar altimetry / Sara Bruni in Journal of geodesy, vol 96 n° 6 (June 2022)Permalink