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Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 1836 - 1847 Titre : Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery Type de document : Article/Communication Auteurs : Ju Zhang, Auteur ; Qingwu Hu, Auteur ; Jiayuan Li, Auteur ; Mingyao Ai, Auteur Année de publication : 2021 Article en page(s) : pp 1836 - 1847 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] extraction du réseau routier [Termes IGN] image à haute résolution [Termes IGN] rastérisation [Termes IGN] segmentation d'image [Termes IGN] trace GPS [Termes IGN] trace numérique [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] Wuhan (Chine) [Termes IGN] zone urbaine Résumé : (Auteur) Deep learning has achieved great success in recent years, among which the convolutional neural network (CNN) method is outstanding in image segmentation and image recognition. It is also widely used in satellite imagery road extraction and, generally, can obtain accurate and extraction results. However, at present, the extraction of roads based on CNN still requires a lot of manual preparation work, and a large number of samples can be marked to achieve extraction, which has to take long drawing cycle and high production cost. In this article, a new CNN sample set production method is proposed, which uses the GPS trajectories of floating car as training set (GPSTasST), for the multilevel urban roads extraction from high-resolution remote sensing imagery. This method rasterizes the GPS trajectories of floating car into a raster map and uses the processed raster map to label the satellite image to obtain a road extraction sample set. CNN can extract roads from remote sensing imagery by learning the training set. The results show that the method achieves a harmonic mean of precision and recall higher than road extraction method from single data source while eliminating the manual labeling work, which shows the effectiveness of this work. Numéro de notice : A2021-211 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003425 Date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003425 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97196 [article]