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Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)
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Titre : Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods Type de document : Article/Communication Auteurs : Rocio Nahime Torres, Auteur Année de publication : 2020 Article en page(s) : pp 225 – 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] base de données altimétriques
[Termes IGN] classification floue
[Termes IGN] collecte de données
[Termes IGN] données localisées des bénévoles
[Termes IGN] figuré du terrain
[Termes IGN] méthode heuristique
[Termes IGN] modèle numérique de surface
[Termes IGN] montagne
[Termes IGN] OpenStreetMap
[Termes IGN] sommet (relief)
[Termes IGN] système d'information géographiqueRésumé : (auteur) Landform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets. Numéro de notice : A2020-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00295-2 Date de publication en ligne : 24/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00295-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95870
in Applied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246[article]NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages / Jimin Wang in Transactions in GIS, Vol 24 n° 3 (June 2020)
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Titre : NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages Type de document : Article/Communication Auteurs : Jimin Wang, Auteur ; Yingjie Hu, Auteur ; Kenneth Joseph, Auteur Année de publication : 2020 Article en page(s) : pp 719 - 735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] catastrophe naturelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] flux de travaux
[Termes IGN] géolocalisation
[Termes IGN] précision sémantique
[Termes IGN] reconnaissance de noms
[Termes IGN] réseau neuronal récurrent
[Termes IGN] réseau social
[Termes IGN] toponymeRésumé : (auteur) Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models. Numéro de notice : A2020-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12627 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1111/tgis.12627 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95508
in Transactions in GIS > Vol 24 n° 3 (June 2020) . - pp 719 - 735[article]Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)
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Titre : Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Arnaud Le Guilcher
, Auteur ; Guillaume Saint Pierre, Auteur ; Mohammad Ghasemi Hamed, Auteur ; Sébastien Mustière
, Auteur ; Olivier Orfila, Auteur
Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 101 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse fonctionnelle (mathématiques)
[Termes IGN] apprentissage profond
[Termes IGN] carte routière
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] détection d'objet
[Termes IGN] données routières
[Termes IGN] feu de circulation
[Termes IGN] inférence
[Termes IGN] reconnaissance de formes
[Termes IGN] signalisation routière
[Termes IGN] trace GPS
[Termes IGN] trafic routier
[Termes IGN] transformation en ondelettes
[Termes IGN] vitesseRésumé : (auteur) The increasing availability of large-scale global positioning system data stemming from in-vehicle-embedded terminal devices enables the design of methods deriving road network cartographic information from drivers’ recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-m-long road span. We proposed three different ways of deriving features: The first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms, and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that random forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs. Numéro de notice : A2020-336 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41060-019-00197-x Date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1007/s41060-019-00197-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93755
in International Journal of Data Science and Analytics JDSA > vol 10 n° 1 (June 2020) . - pp 101 - 119[article]Documents numériques
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Traffic signal detection ... - preprintAdobe Acrobat PDFUnder-canopy UAV laser scanning for accurate forest field measurements / Eric Hyyppä in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
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Titre : Under-canopy UAV laser scanning for accurate forest field measurements Type de document : Article/Communication Auteurs : Eric Hyyppä, Auteur ; Juha Hyyppä, Auteur ; Teemu Hakala, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 41 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] balayage laser
[Termes IGN] canopée
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] densité du bois
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] hauteur à la base du houppier
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] inventaire forestier local
[Termes IGN] modèle de croissance végétale
[Termes IGN] semis de points
[Termes IGN] télédétection aérienne
[Termes IGN] télémètre laser terrestre
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] troncRésumé : (auteur) Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m 32 m test sites that were characterized as sparse ( = 42 trees) and obstructed ( = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories. Numéro de notice : A2020-240 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.021 Date de publication en ligne : 11/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94994
in ISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 41 - 60[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])
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Titre : Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data Type de document : Article/Communication Auteurs : Thota Sivasankar, Auteur ; Dheeraj Kumar, Auteur ; Hari Shanker Srivastava, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 905 - 915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] coefficient de corrélation
[Termes IGN] image radar moirée
[Termes IGN] image Risat-1
[Termes IGN] indice foliaire
[Termes IGN] polarisation
[Termes IGN] régression non linéaire
[Termes IGN] rétrodiffusion
[Termes IGN] séparateur à vaste marge
[Termes IGN] surveillance de la végétationRésumé : (auteur) Leaf Area Index (LAI) is a key parameter to characterize the canopy–atmosphere interface, where most of the energy fluxes exchange. Space-borne satellite images have shown their relevance for various applications including LAI retrieval over large areas. Although optical data have been used for this purpose in previous studies, the constraints to acquire optical data during extreme weather conditions due to the presence of clouds, haze, smoke etc. hinders its use for uninterrupted monitoring. This study aims to analyze the relationships of C-band RISAT-1 hybrid polarized SAR data (σ˚RH and σ˚RV) with wheat LAI. The results have shown the correlation coefficient (|r|) of 0.57 and 0.73 for RH and RV backscatter, respectively, using non-linear regression approach. It is also observed that the accuracy of LAI retrieval has been significantly improved with |r| and RMSE of 0.81 and 0.54 (m2/m2), respectively, by considering both RH and RV backscatter as inputs for support vector machine-based model. Numéro de notice : A2020-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10106049.2019.1566404 Date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566404 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95219
in Geocarto international > Vol 35 n° 8 [01/06/2020] . - pp 905 - 915[article]Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network / Clara Tallieu in Forest ecology and management, Vol 465 (1st June 2020)
PermalinkMethodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland / Izabela Karsznia in Geocarto international, vol 35 n° 7 ([15/05/2020])
PermalinkAutomatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
PermalinkComparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding / Weihua Dong in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)
PermalinkA convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
PermalinkDeep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, TOSAS, vol 6 n° 3 (May 2020)
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PermalinkExploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
PermalinkImproved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
PermalinkImproved wavelet neural network based on change rate to predict satellite clock bias / Xu Wang in Survey review, vol 52 n° 372 (May 2020)
PermalinkA review of assessment methods for cellular automata models of land-use change and urban growth / Xiaohua Tong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
PermalinkA review of techniques for 3D reconstruction of indoor environments / Zhizhong Kang in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
PermalinkSaliency-guided single shot multibox detector for target detection in SAR images / Lan Du in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
PermalinkAdaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
PermalinkAutomated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)
PermalinkDirectionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
PermalinkGeocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
PermalinkPredictive mapping with small field sample data using semi‐supervised machine learning / Fei Du in Transactions in GIS, Vol 24 n° 2 (April 2020)
PermalinkA Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
PermalinkStreet-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)
PermalinkUsing multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)
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