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Integration of airborne gravimetry data filtering into residual least-squares collocation: example from the 1 cm geoid experiment / Martin Willberg in Journal of geodesy, vol 94 n° 8 (August 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 8 (August 2020) . - n° 75 Titre : Integration of airborne gravimetry data filtering into residual least-squares collocation: example from the 1 cm geoid experiment Type de document : Article/Communication Auteurs : Martin Willberg, Auteur ; Philipp Zingerle, Auteur ; Roland Pail, Auteur Année de publication : 2020 Article en page(s) : n° 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] collocation par moindres carrés [Termes descripteurs IGN] Colorado (Etats-Unis) [Termes descripteurs IGN] filtre passe-bas [Termes descripteurs IGN] géoïde gravimétrique [Termes descripteurs IGN] gravimétrie aérienne [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] modélisation [Termes descripteurs IGN] pondération [Termes descripteurs IGN] processus gaussien Résumé : (auteur) Low-pass filters are commonly used for the processing of airborne gravity observations. In this paper, for the first time, we include the resulting correlations consistently in the functional and stochastic model of residual least-squares collocation. We demonstrate the necessity of removing high-frequency noise from airborne gravity observations, and derive corresponding parameters for a Gaussian low-pass filter. Thereby, we intend an optimal combination of terrestrial and airborne gravity observations in the mountainous area of Colorado. We validate the combination in the frame of our participation in ‘the 1 cm geoid experiment’. This regional geoid modeling inter-comparison exercise allows the calculation of a reference solution, which is defined as the mean value of 13 independent height anomaly results in this area. Our result performs among the best and with 7.5 mm shows the lowest standard deviation to the reference. From internal validation we furthermore conclude that the input from airborne and terrestrial gravity observations is consistent in large parts of the target area, but not necessarily in the highly mountainous areas. Therefore, the relative weighting between these two data sets turns out to be a main driver for the final result, and is an important factor in explaining the remaining differences between various height anomaly results in this experiment. Numéro de notice : A2020-536 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01396-2 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.1007/s00190-020-01396-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95729 [article]

Hyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance / Bing Tu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 4116 - 4131 Titre : Hyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance Type de document : Article/Communication Auteurs : Bing Tu, Auteur ; Chengle Zhou, Auteur ; Danbing He, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4116 - 4131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] erreur d'échantillon [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] pondération [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] superpixel Résumé : (auteur) Classification is an important technique for remotely sensed hyperspectral image (HSI) exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate supervised classification. In this article, we introduce a new framework for noisy label detection that combines a superpixel-to-pixel weighting distance (SPWD) and density peak clustering. The proposed method is able to accurately detect and remove noisy labels in the training set before HSI classification. It considers two weak assumptions when exploiting the spectral–spatial information contained in the HSI: 1) all the pixels in a superpixel belong to the same class and 2) close pixels in spectral space have the same label. The proposed method consists of the following steps. First, a superpixel segmentation step is used to obtain self-adaptive spatial information for each training sample. Then, a metric is utilized to measure the spectral distance information between each superpixel and pixel. Meanwhile, in order to overcome the first weak assumption, we use K nearest neighbors to obtain the closest neighborhoods of pixels around each superpixel, and a Gaussian weight is employed to mitigate the second weak assumption by adapting the original distance information. Next, the noisy labels in the original training set are removed by a density threshold-based decision function. Finally, the support vector machine (SVM) classifier is employed to evaluate the effectiveness of the proposed SPWD detection method in terms of classification accuracy. Experiments performed on several real HSI data sets demonstrate that the method can effectively improve the performance of classifiers trained with noisy training sets in terms of classification accuracy. Numéro de notice : A2020-283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2961141 date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2961141 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95105 [article]

Prediction of traffic accidents hot spots using fuzzy logic and GIS / Aslam Al-Omari in Applied geomatics, vol 12 n° 2 (June 2020)  

Public [article]  inApplied geomatics > vol 12 n° 2 (June 2020) . - pp 149 – 161 Titre : Prediction of traffic accidents hot spots using fuzzy logic and GIS Type de document : Article/Communication Auteurs : Aslam Al-Omari, Auteur ; Nawras Shatnawi, Auteur ; Taisir Khedaywi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 149 – 161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] accident de la route [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] Jordanie [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] pondération [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] superposition [Termes descripteurs IGN] zone à risque Résumé : (auteur) The purpose of the current study is to predict accident hot spots in different locations using Geographic Information System (GIS) and fuzzy logic. The data used contained accident types and occurrence time. Fatality and injury were also studied with spatial-temporal analysis. Moreover, accident hot spots were predicted performing Weighted Overlay Method (WOM) and Fuzzy Overlay Method (FOM), which are widely used in decision making and alternatives analysis based on the results obtained from Analytic Hierarchy Process (AHP). Point Density (PD) method was used to verify hot spots in urban region that resulted from the mentioned two methods. Traffic accidents’ hot spots were predicted for Irbid City in Jordan using the data of the accidents that occurred between 2013 and 2015. Both WOM and FOM proved to be successful in identifying hot spots in parts of study area when verified to PD surface. Final results showed that eight hot spots were pointed out; three are road sections and five are major intersections, which were analyzed to get accident-contributing factors and suggest the proper remedies. Numéro de notice : A2020-559 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00290-7 date de publication en ligne : 03/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00290-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95869 [article]

Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS / Dogus Guler in Annals of GIS, vol 26 n° 2 (April 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 2 (April 2020) . - pp 169 - 189 Titre : Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS Type de document : Article/Communication Auteurs : Dogus Guler, Auteur ; Tahsin Yomralioglu, Auteur Année de publication : 2020 Article en page(s) : pp 169 - 189 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] approche holistique [Termes descripteurs IGN] électricité [Termes descripteurs IGN] outil d'aide à la décision [Termes descripteurs IGN] pondération [Termes descripteurs IGN] processus d'analyse hiérarchisée floue [Termes descripteurs IGN] station [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] véhicule automobile [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Electric vehicles arouse interest since they not only contribute economies of countries in the context of dependency to oil but also support to more livable and sustainable urban areas. The location selection of electric vehicle charging stations is one of the most vital topics in order to enhance the use of electric vehicles. In this sense, the aim of this paper is to propose an approach that integrates Geographic Information System (GIS) techniques and Multi-Criteria Decision Making (MCDM) methods for finding suitable locations of the electric vehicle charging stations. In this regard, the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP) methods are used to calculate the weights of criteria. While the two different weights for each criterion are obtained by means of AHP in terms of environmental impact and accessibility, another weight for each criterion is obtained as a means of applying the FAHP. The intersection of three different suitability indexes is determined so as to achieve a holistic, credible result. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the alternative locations. The results show that the proposed approach offers a notable solution to be selected suitable charging station locations. Moreover, policymakers and administrators could benefit from these results in order to make efficient decisions for forward planning and strategies. Numéro de notice : A2020-322 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1737226 date de publication en ligne : 09/03/2020 En ligne : https://doi.org/10.1080/19475683.2020.1737226 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95189 [article]

Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)  

Public [article]  inSurvey review > vol 52 n° 371 (March 2020) . - pp 150 - 158 Titre : Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps Type de document : Article/Communication Auteurs : Alper Sen, Auteur ; Baris Suleymanoglu, Auteur ; Metin Soycan, Auteur Année de publication : 2020 Article en page(s) : pp 150 - 158 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] extraction de points [Termes descripteurs IGN] filtre adaptatif [Termes descripteurs IGN] khi carré [Termes descripteurs IGN] pondération [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] zone urbaine Résumé : (auteur) The extraction of artificial and natural features using light detection and ranging (Lidar) data is a fundamental task in many fields of research for environmental science. In this study, the possibility of using self-organising maps (SOM), which is an unsupervised artificial neural network classification method to extract the bare earth surface and features from airborne Lidar data, was investigated for two different urban areas. The effect of the enlargement of the study area was analysed using the proposed approach. The appropriate weights of SOM inputs, which are 3D coordinates and intensity, obtained from a Lidar point cloud were determined by using Pearson's chi-squared independence test. The weighted SOM feature extraction performance was better than that of the unweighted SOM. The filtering results of SOM to separate ground and non-ground data were also compared with those obtained by the adaptive TIN filtering algorithm. Most of the non-ground features could be removed by the weighted SOM. Numéro de notice : A2020-079 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1532704 date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1532704 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94642 [article]

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n°1 (2020)  

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Impact of GPS processing on the estimation of snow water equivalent using refracted GPS signals / Ladina Steiner in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)  

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Helmert-VCE-aided fast-WTLS approach for global ionospheric VTEC modelling using data from GNSS, satellite altimetry and radio occultation / Andong Hu in Journal of geodesy, vol 93 n°6 (June 2019)  

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A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)  

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Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology / Stéphane Guinard in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)

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A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures / Gangothri Rajaram in Geoinformatica [en ligne], vol 22 n° 2 (April 2018)  

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A novel orthoimage mosaic method using a weighted A∗ algorithm : Implementation and evaluation / Maoteng Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data / Giles M. Foody in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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