Descripteur
Termes IGN > géomatique > base de données localisées > jeu de données localisées
jeu de données localiséesVoir aussi |
Documents disponibles dans cette catégorie (90)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
GIS-based modeling for selection of dam sites in the Kurdistan region, Iraq / Arsalan Ahmed Othman in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)
[article]
Titre : GIS-based modeling for selection of dam sites in the Kurdistan region, Iraq Type de document : Article/Communication Auteurs : Arsalan Ahmed Othman, Auteur ; Ahmed F. Al-Maamar, Auteur ; Diary Ali Mohammed Amin Al-Manmi, Auteur Année de publication : 2020 Article en page(s) : 34 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse multicritère
[Termes IGN] barrage
[Termes IGN] capacité de stockage
[Termes IGN] construction
[Termes IGN] gestion de l'eau
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] image Quickbird
[Termes IGN] Iraq
[Termes IGN] jeu de données localisées
[Termes IGN] processus de hiérarchisation analytique floue
[Termes IGN] régression géographiquement pondéréeRésumé : (auteur) Iraq, a country in the Middle East, has suffered severe drought events in the past two decades due to a significant decrease in annual precipitation. Water storage by building dams can mitigate drought impacts and assure water supply. This study was designed to identify suitable sites to build new dams within the Al-Khabur River Basin (KhRB). Both the fuzzy analytic hierarchy process (AHP) and the weighted sum method (WSM) were used and compared to select suitable dam sites. A total of 14 layers were used as input dataset (i.e., lithology, tectonic zones, distance to active faults, distance to lineaments, soil type, land cover, hypsometry, slope gradient, average precipitation, stream width, Curve Number Grid, distance to major roads, distance to towns and cities, and distance to villages). Landsat-8/Operational Land Imager (OLI) and QuickBird optical images were used in the study. Three types of accuracies were tested: overall, suitable pixels by number, and suitable pixels by weight. Based on these criteria, we determined that 11 sites are suitable for locating dams for runoff harvesting. Results were compared to the location of 21 preselected dams proposed by the Ministry of Agricultural and Water Resources (MAWR). Three of these dam sites coincide with those proposed by the MAWR. The overall accuracies of the 11 dams ranged between 76.2% and 91.8%. The two most suitable dam sites are located in the center of the study area, with favorable geology, adequate storage capacity, and in close proximity to the population centers. Of the two selection methods, the AHP method performed better as its overall accuracy is greater than that of the WSM. We argue that when stream discharge data are not available, use of high spatial resolution QuickBird imageries to determine stream width for discharge estimation is acceptable and can be used for preliminary dam site selection. The study offers a valuable and relatively inexpensive tool to decision-makers for eliminating sites having severe limitations (less suitable sites) and focusing on those with the least restriction (more suitable sites) for dam construction. Numéro de notice : A2020-265 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040244 Date de publication en ligne : 15/04/2020 En ligne : https://doi.org/10.3390/ijgi9040244 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95028
in ISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 34 p.[article]Deep learning for remote sensing images with open source software / Rémi Cresson (2020)
Titre : Deep learning for remote sensing images with open source software Type de document : Guide/Manuel Auteurs : Rémi Cresson, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2020 Importance : 164 p. Présentation : Nombreuses illustrations en couleur ISBN/ISSN/EAN : 978-0-367-85848-3 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image radar
[Termes IGN] image Sentinel
[Termes IGN] jeu de données localisées
[Termes IGN] Orfeo Tool Box
[Termes IGN] QGIS
[Termes IGN] restauration d'image
[Termes IGN] segmentation sémantiqueIndex. décimale : 35.20 Traitement d'image Résumé : (Editeur) In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource.This book is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data.
Specific Features of this Book:
- The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow)
- Presents approaches suited for real world images and data targeting large scale processing and GIS applications
- Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration)
- Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills.
- Includes deep learning techniques through many step by step remote sensing data processing exercises.Note de contenu :
Introduction
1. Backgrounds
2. Patch Based Classification
3. Semantic Segmentation
4. Image RestorationNuméro de notice : 26551 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Manuel DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97864 Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 26551-01 35.20 Livre Centre de documentation Télédétection Disponible Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n° 12 (December 2019)
[article]
Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction automatique
[Termes IGN] gestion des itinéraires
[Termes IGN] jeu de données localisées
[Termes IGN] point de repère
[Termes IGN] précision de la classification
[Termes IGN] représentation mentale spatiale
[Termes IGN] saillance
[Termes IGN] Shenzhen
[Termes IGN] villeRésumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559
in International journal of geographical information science IJGIS > vol 33 n° 12 (December 2019) . - pp 2406 - 2423[article]An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping / Qi Zhou in Transactions in GIS, Vol 23 n° 6 (November 2019)
[article]
Titre : An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping Type de document : Article/Communication Auteurs : Qi Zhou, Auteur ; Xuecan Jia, Auteur ; Hao Lin, Auteur Année de publication : 2019 Article en page(s) : pp 1420 - 1443 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse des correspondances
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie collaborative
[Termes IGN] données localisées des bénévoles
[Termes IGN] jeu de données localisées
[Termes IGN] Kappa de Cohen
[Termes IGN] OpenStreetMap
[Termes IGN] panel de référence
[Termes IGN] précision de la classification
[Termes IGN] zone tamponRésumé : (auteur) OpenStreetMap (OSM) provides free source data for land use and land cover (LULC) mapping of many regions globally. Earlier work has used just manual and subjective approaches to establish correspondence between paired OSM and reference datasets, an essential step for LULC mapping. This study proposes an approach to establish correspondence via three steps: (1) convert line feature(s) into polygon feature(s); (2) merge multiple polygon feature(s) into a single layer; and (3) establish correspondence and reclassify OSM and/or reference datasets. Study areas in Sheffield, London, Rome, and Paris were used for testing, and two measures (overall accuracy, OA and kappa index) were used for evaluation. Experiments were designed to verify this approach, with each pair of OSM and reference datasets initially compared after reclassification. Correspondence from one study area was then applied to another for further validation. Results show that OA was between 70 and 90% and the kappa index varied between 0.6 and 0.8. Evaluation also indicates that the correspondence obtained from one study area is applicable to another, and we illustrate the effectiveness of this approach. Numéro de notice : A2019-568 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12581 Date de publication en ligne : 03/10/2019 En ligne : https://doi.org/10.1111/tgis.12581 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94420
in Transactions in GIS > Vol 23 n° 6 (November 2019) . - pp 1420 - 1443[article]PPD: Pyramid Patch Descriptor via convolutional neural network / Jie Wan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)
[article]
Titre : PPD: Pyramid Patch Descriptor via convolutional neural network Type de document : Article/Communication Auteurs : Jie Wan, Auteur ; Alper Yilmaz, Auteur ; Lei Yan, Auteur Année de publication : 2019 Article en page(s) : pp 673 - 686 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] appariement d'images
[Termes IGN] benchmark spatial
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données localisées de référence
[Termes IGN] échantillonnage d'image
[Termes IGN] état de l'art
[Termes IGN] extraction de données
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] jeu de données localiséesRésumé : (Auteur) Local features play an important role in remote sensing image matching, and handcrafted features have been excessively used in this area for a long time. This article proposes a pyramid convolutional neural triplet network that extracts a 128-dimensional deep descriptor that significantly improves the matching performance. The proposed approach first extracts deep descriptors of the anchor patches and corresponding positive patches in a batch using the proposed pyramid convolutional neural network. Following this step, the approaches chooses the closest negative patch for each anchor patch and corresponding positive patch pair to form the triplet sample based on the descriptor distances among all other image patches in the batch. These triplets are used to optimize the parameters of the network using a new loss function. We evaluated the proposed deep descriptors on two benchmark data sets (Brown and HPatches) as well as real image data sets. The results reveal that the proposed descriptor achieves the state-of-the-art performance on the Brown data set and a comparatively very high performance on the HPatches data set. The proposed approach finds more correct matches than the classical handcrafted feature descriptors on aerial image pairs and is observed to be robust to variations in the viewpoint and illumination. Numéro de notice : A2019-416 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.9.673 Date de publication en ligne : 01/09/2019 En ligne : https://doi.org/10.14358/PERS.85.9.673 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93543
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 9 (September 2019) . - pp 673 - 686[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2019091 SL Revue Centre de documentation Revues en salle Disponible Semantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkLes systèmes d'information géographique / Christina Aschan-Leygonie (2019)PermalinkSurface reconstruction of incomplete datasets: A novel Poisson surface approach based on CSRBF / Jules Morel in Computers and graphics, vol 74 (August 2018)PermalinkAssessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets / Ming Li in Geoinformatica, vol 22 n° 3 (July 2018)PermalinkUn modèle pour l’intégration spatiale et temporelle de données géolocalisées / Helbert Arenas in Revue internationale de géomatique, vol 28 n° 2 (avril - juin 2018)PermalinkA 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)PermalinkGéomatique et enseignement secondaire / Cyrille Chopin in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 22 n° 5 (septembre - octobre 2017)PermalinkA GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)PermalinkA viewpoint based approach to the visual exploration of trajectory / Jie Li in Journal of Visual Languages and Computing, vol 41 (August 2017)PermalinkEfficient maximal reverse skyline query processing / Farnoush Banaei-Kashani in Geoinformatica, vol 21 n° 3 (July - September 2017)Permalink