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GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 556 - 572 Titre : GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2020 Article en page(s) : pp 556 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] cartographie topographique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] collecte de données [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] géobalise [Termes IGN] toponyme [Termes IGN] United States Geological Survey Résumé : (Auteur) Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain. Numéro de notice : A2020-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12633 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12633 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95307 [article]

Validating the use of object-based image analysis to map commonly recognized landform features in the United States / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 46 n° 5 (September 2019)  

Public [article]  inCartography and Geographic Information Science > Vol 46 n° 5 (September 2019) . - pp 441 - 455 Titre : Validating the use of object-based image analysis to map commonly recognized landform features in the United States Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Gaurav Sinha, Auteur Année de publication : 2019 Article en page(s) : pp 441 - 455 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] accident géographique [Termes IGN] analyse d'image orientée objet [Termes IGN] base de données localisées [Termes IGN] chaîne de traitement [Termes IGN] données localisées 3D [Termes IGN] Etats-Unis [Termes IGN] relation spatiale [Termes IGN] relief [Termes IGN] toponymie locale Résumé : (Auteur) The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods. Numéro de notice : A2019-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1526652 Date de publication en ligne : 07/11/2018 En ligne : https://doi.org/10.1080/15230406.2018.1526652 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93173 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2019051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automated extraction of hydrographically corrected contours for the conterminous United States: the US Geological Survey US Topo product / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 45 n° 1 (January 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 1 (January 2018) . - pp 31 - 55 Titre : Automated extraction of hydrographically corrected contours for the conterminous United States: the US Geological Survey US Topo product Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Philip T. Thiem, Auteur ; Eric W. Constance, Auteur Année de publication : 2018 Article en page(s) : pp 31 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] base de données topographiques [Termes IGN] carte topographique [Termes IGN] courbe de niveau [Termes IGN] détection de contours [Termes IGN] données localisées 3D [Termes IGN] Etats-Unis [Termes IGN] hydrographie [Termes IGN] modèle numérique de surface [Termes IGN] nivellement [Termes IGN] précision cartographique Résumé : (auteur) The US Topo is a new generation of digital topographic maps delivered by the US Geological Survey (USGS). These maps include contours in the traditional 7.5-min quadrangle format. The process for producing digital elevation contours has evolved over several years, but automated production of contours for the US Topo product began in 2010. This process, which is quite complex yet fairly elegant, automatically chooses the proper USGS quadrangle, captures the corresponding 1/3 as grid points from the national elevation data set (3D Elevation Program), and adjusts elevation data to better fit water features from the National Hydrography Dataset. After additional processing, such as identifying and tagging depressions, constructing proper contours across double-line streams, and omitting contours from water bodies, contours are automatically produced for the quadrangle. The resulting contours are then compared to the contours on the original (legacy) topographic map sheets, or to the 10-m contours from the original map sheets. Where the elevation surface used to generate the contours has been derived from the previously published contours for a quadrangle, the generated contours match the legacy contours quite well. Where newer elevation sources, such as lidar, originate the elevation surface, generated contours may vary significantly from the previous cartographically produced contours due to more accurate representations of the surface, and less reliance on cartographic interpretation. Numéro de notice : A2018-003 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2016.1230027 En ligne : https://doi.org/10.1080/15230406.2016.1230027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88379 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2018011	RAB	Revue	Centre de documentation	En réserve L003	Disponible