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DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques / Ali H. Ahmed Suliman in Geocarto international, vol 36 n° 7 ([15/04/2021])
[article]
Titre : DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques Type de document : Article/Communication Auteurs : Ali H. Ahmed Suliman, Auteur ; W. Gumindoga, Auteur ; Taymoor A. Awchi, Auteur ; Ayob Katimon, Auteur Année de publication : 2021 Article en page(s) : pp 803 - 819 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Advanced Spaceborne Thermal Emission and Reflection Radiometer
[Termes IGN] analyse comparative
[Termes IGN] bassin hydrographique
[Termes IGN] carte topographique
[Termes IGN] Iran
[Termes IGN] limite de résolution géométrique
[Termes IGN] MNS ASTER
[Termes IGN] modèle numérique de surface
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] ruissellementRésumé : (Auteur) The accurate estimation of terrain characteristics is central in rainfall runoff modelling. In this study, influences of Digital Elevation Models (DEMs) obtained from different sources, resolutions and rescaling techniques are compared for Peak flow prediction in a large-scale watershed by the Topographic driven model (TOPMODEL). The comparison includes graphical representation and statistical assessments using daily time series data. As a result, DEM extracted from contour map (DEM-Con) showed better performance when DEM resolutions increased, but the Advanced Space-borne Thermal Emission and Reflection Radiometer (DEM-Aster) continued to achieve less Relative Error (RE) at low resolution. Moreover, better RE values were found at cubic convolution technique to predict the peaks followed by nearest neighbor and bilinear. In addition, this study indicated that DEM resolution is more sensitive factor for TOPMODEL simulation compared to DEM sources and rescaling techniques for streamflow and peaks prediction. Numéro de notice : A2021-295 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622599 Date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1622599 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97355
in Geocarto international > vol 36 n° 7 [15/04/2021] . - pp 803 - 819[article]Detecting archaeological features with airborne laser scanning in the alpine tundra of Sápmi, Northern Finland / Oula Seitsonen in Remote sensing, vol 13 n° 8 (April-2 2021)
[article]
Titre : Detecting archaeological features with airborne laser scanning in the alpine tundra of Sápmi, Northern Finland Type de document : Article/Communication Auteurs : Oula Seitsonen, Auteur ; Janne Ikäheimo, Auteur Année de publication : 2021 Article en page(s) : n° 1599 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte archéologique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] Finlande
[Termes IGN] fouille archéologique
[Termes IGN] lasergrammétrie
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] toundraRésumé : (auteur) Open access airborne laser scanning (ALS) data have been available in Finland for over a decade and have been actively applied by the Finnish archaeologists in that time. The low resolution of this laser scanning 2008–2019 dataset (0.5 points/m2), however, has hindered its usability for archaeological prospection. In the summer of 2020, the situation changed markedly, when the Finnish National Land Survey started a new countrywide ALS survey with a higher resolution of 5 points/m2. In this paper we present the first results of applying this newly available ALS material for archaeological studies. Finnish LIDARK consortium has initiated the development of semi-automated approaches for visualizing, detecting, and analyzing archaeological features with this new dataset. Our first case studies are situated in the Alpine tundra environment of Sápmi in northern Finland, and the assessed archaeological features range from prehistoric sites to indigenous Sámi reindeer herding features and Second Word War-era German military structures. Already the initial analyses of the new ALS-5p data show their huge potential for locating, mapping, and assessing archaeological material. These results also suggest an imminent burst in the number of known archaeological sites, especially in the poorly accessible and little studied northern wilderness areas, when more data become available. Numéro de notice : A2021-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081599 Date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97629
in Remote sensing > vol 13 n° 8 (April-2 2021) . - n° 1599[article]The delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([15/04/2021])
[article]
Titre : The delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods Type de document : Article/Communication Auteurs : Akhtar Jamil, Auteur ; Bulent Bayram, Auteur Année de publication : 2021 Article en page(s) : pp 758 - 772 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de décalage moyen
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] Camellia sinensis
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] exploitation agricole
[Termes IGN] extraction de la végétation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] orthoimage
[Termes IGN] segmentation hiérarchique
[Termes IGN] TurquieRésumé : (Auteur) Rize district is an important tea production site in Turkey, which is known for high quality tea. Determining the temporal changes is very crucial from the viewpoint of agricultural management and protection of tea areas. In addition, delineation of tea gardens using photogrammetric evaluation techniques for a single orthoimage takes approximately 8 h of labour work, which is both costly and time-consuming process. To overcome these issues, a method is proposed for demarcation of tea gardens from high-resolution orthoimages. In this article, a hierarchical object-based segmentation using mean-shift (MS) and supervised machine learning (ML) methods are investigated for delineation of tea gardens. First, the MS algorithm was applied to partition the images into homogeneous segments (objects) and then from each segment, various spectral, spatial and textural features were extracted. Finally, four most widely used supervised ML classifiers, support vector machine (SVM), artificial neural network (ANN), random forest (RF), and decision trees (DTs), were selected for classification of objects into tea gardens and other types of trees. Photogrammetrically evaluated tea garden borders were taken as reference data to evaluate the performance of the proposed methods. The experiments showed that all selected supervised classifiers were effective for delineation of the tea gardens from high-resolution images. Numéro de notice : A2021-293 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622597 Date de publication en ligne : 19/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1622597 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97349
in Geocarto international > vol 36 n° 7 [15/04/2021] . - pp 758 - 772[article]Scene classification of remotely sensed images via densely connected convolutional neural networks and an ensemble classifier / Qimin Cheng in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)
[article]
Titre : Scene classification of remotely sensed images via densely connected convolutional neural networks and an ensemble classifier Type de document : Article/Communication Auteurs : Qimin Cheng, Auteur ; Yuan Xu, Auteur ; Peng Fu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 295-308 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] scèneRésumé : (Auteur) Deep learning techniques, especially convolutional neural networks, have boosted performance in analyzing and understanding remotely sensed images to a great extent. However, existing scene-classification methods generally neglect local and spatial information that is vital to scene classification of remotely sensed images. In this study, a method of scene classification for remotely sensed images based on pretrained densely connected convolutional neural networks combined with an ensemble classifier is proposed to tackle the under-utilization of local and spatial information for image classification. Specifically, we first exploit the pretrained DenseNet and fine-tuned it to release its potential in remote-sensing image feature representation. Second, a spatial-pyramid structure and an improved Fisher-vector coding strategy are leveraged to further strengthen representation capability and the robustness of the feature map captured from convolutional layers. Then we integrate an ensemble classifier in our network architecture considering that lower attention to feature descriptors. Extensive experiments are conducted, and the proposed method achieves superior performance on UC Merced, AID, and NWPU-RESISC45 data sets. Numéro de notice : A2021-334 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.3.295 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.3.295 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97533
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 295-308[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021041 SL Revue Centre de documentation Revues en salle Disponible Spatial analysis of subway passenger traffic in Saint-Petersburg / Tatiana Baltyzhakova in Geodesy and cartography, vol 47 n° 1 (January 2021)
[article]
Titre : Spatial analysis of subway passenger traffic in Saint-Petersburg Type de document : Article/Communication Auteurs : Tatiana Baltyzhakova, Auteur ; Aleksei Romanchicov, Auteur Année de publication : 2021 Article en page(s) : pp 10 - 20 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Blender
[Termes IGN] diagramme de Voronoï
[Termes IGN] flux
[Termes IGN] Mapbox
[Termes IGN] modèle numérique de surface
[Termes IGN] planification urbaine
[Termes IGN] QGIS
[Termes IGN] R (langage)
[Termes IGN] Saint-Petersbourg
[Termes IGN] trafic
[Termes IGN] transport publicRésumé : (auteur) The purpose of the paper is to create clear visualization of passenger traffic for Saint Petersburg subway system. This visualization can be used to better understand the passenger flow and to make more informed decisions in future planning. Research was based on officially published information about passenger traffic on subway station for years 2016 and 2018. Visualization was created with the variety of methods and software: Voronoi diagrams (QGIS software), social gravitation potential (R programming language), presentation of gravitation potential as a relief (Blender software), service zones of ground transport accessibility (2GIS, QGIS and Mapbox mapping platform). In this research, authors propose the use of intersection between the service zones and social gravitation potential isolines as an instrument for spatial analysis of traffic data. Analysis shown that current development of subway system does not correspond to passenger distribution. All stations were classified according to their accessibility and propositions about future directions of development were made. Numéro de notice : A2021-451 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3846/gac.2021.11980 Date de publication en ligne : 12/03/2021 En ligne : https://doi.org/10.3846/gac.2021.11980 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97869
in Geodesy and cartography > vol 47 n° 1 (January 2021) . - pp 10 - 20[article]Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery / Zifeng Wang in Remote sensing of environment, Vol 255 (March 2021)PermalinkAutomated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images / Luigi Parente in Photogrammetric record, vol 36 n° 173 (March 2021)PermalinkDigital surface model refinement based on projected images / Jiali Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)PermalinkFamous charts and forgotten fragments: exploring correlations in early Portuguese nautical cartography / Bruno Almeida in International journal of cartography, vol 7 n° 1 (March 2021)PermalinkGIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan / Shah Naseer in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkHorizontal calibration of vessels with UASs / Casey O'Heran in Marine geodesy, vol 44 n° 2 (March 2021)PermalinkAn integrated method for DEM simplification with terrain structural features and smooth morphology preserved / Wenhao Yu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkA feature-preserving point cloud denoising algorithm for LiDAR-derived DEM construction / Chuanfa Chen in Survey review, Vol 53 n° 377 (February 2021)PermalinkForest height estimation using a single-pass airborne L-band polarimetric and interferometric SAR system and tomographic techniques / Yue Huang in Remote sensing, Vol 13 n° 3 (February 2021)PermalinkInfluence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle / Lucas Santos Santana in European journal of remote sensing, vol 54 n° 1 (2021)PermalinkUncertainties and errors in algorithms for elevation gradients / Dong Shi in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkUsing Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkPermalinkAmélioration et adaptation du protocole de mesure d’empreintes d’abrasion par photogrammétrie / Hiba Sayeh (2021)PermalinkAn improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images / Behrooz Moradi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)PermalinkAnalyse de la dynamique d’embroussaillement des pelouses calcaires par traitement d’images / Théo Mesure (2021)PermalinkPermalinkApport des méthodes : imagerie drone, LiDAR et imagerie hyperspectrale pour l’étude du littoral vendéen / Mathis Baudis (2021)PermalinkApport de la photogrammétrie satellite pour la modélisation du manteau neigeux / César Deschamps-Berger (2021)PermalinkAutomated detection of lineaments express geological linear features of a tropical region using topographic fabric grain algorithm and the SRTM DEM / Samy Ismail Elmahdy in Geocarto international, vol 36 n° 1 ([01/01/2021])Permalink