Centre de documentation
scientifique

Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 10 (May-2 2022) . - n° 2446 Titre : Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning Type de document : Article/Communication Auteurs : Mingge Yu, Auteur ; Xiaoping Rui, Auteur ; Weiyi Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2446 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] échantillonnage [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] image panchromatique [Termes IGN] image Worldview [Termes IGN] modèle de simulation [Termes IGN] surface cultivée [Termes IGN] terrasse Résumé : (auteur) Rapid, accurate extraction of terraces from high-resolution images is of great significance for promoting the application of remote-sensing information in soil and water conservation planning and monitoring. To solve the problem of how deep learning requires a large number of labeled samples to achieve good accuracy, this article proposes an automatic identification method for terraces that can obtain high precision through small sample datasets. Firstly, a terrace identification source model adapted to multiple data sources is trained based on the WorldView-1 dataset. The model can be migrated to other types of images for terracing extraction as a pre-trained model. Secondly, to solve the small sample problem, a deep transfer learning method for accurate pixel-level extraction of high-resolution remote-sensing image terraces is proposed. Finally, to solve the problem of insufficient boundary information and splicing traces during prediction, a strategy of ignoring edges is proposed, and a prediction model is constructed to further improve the accuracy of terrace identification. In this paper, three regions outside the sample area are randomly selected, and the OA, F1 score, and MIoU averages reach 93.12%, 91.40%, and 89.90%, respectively. The experimental results show that this method, based on deep transfer learning, can accurately extract terraced field surfaces and segment terraced field boundaries. Numéro de notice : A2022-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14102446 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.3390/rs14102446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100705 [article]

A novel approach for generating routable road maps from vehicle GPS traces / Jing Wang in International journal of geographical information science IJGIS, vol 29 n° 1 (January 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 1 (January 2015) . - pp 69 - 91 Titre : A novel approach for generating routable road maps from vehicle GPS traces Type de document : Article/Communication Auteurs : Jing Wang, Auteur ; Xiaoping Rui, Auteur ; Xianfeng Song, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 69 - 91 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données GPS [Termes IGN] PostGIS [Termes IGN] PostgreSQL [Termes IGN] Python (langage de programmation) [Termes IGN] récupération de données [Termes IGN] réseau routier [Termes IGN] trafic routier Résumé : (Auteur) Public vehicles and personal navigation assistants have become increasingly equipped with single-frequency global positioning system (GPS) receivers or loggers. These commonly used terminals offer an inexpensive way for acquiring large volumes of GPS traces, which contain information pertaining to road position and traffic rules. Using this new type of spatial data resource, we propose a novel approach for generating high-quality routable road maps. In this approach, a simplified road network graph model uses circular boundaries to separate all GPS traces into road intersections and road segments and builds road networks that maintain their identical geometric topologies through the entry/exit points at the original boundaries. One difficulty inherent to this type of approach is how to best determine the appropriate spatial coverage for road intersections. Conflict points among GPS traces that have large intersection angles usually occur within the physical areas of road intersections, particularly those involving left turns. Therefore, we determined a proper circle boundary for individual road intersections by conducting a spatial analysis of such feature points. This approach was implemented using Python and PostgreSQL/PostGIS and was tested in Huaibei City, China. Based on a comparison with human-interpreted results, the automatically generated routable road map was demonstrated to be of high quality and displayed detailed road networks with turning at various at-grade intersections, interchanges and U-turns. Numéro de notice : A2015-575 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.944527 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.944527 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77837 [article]