Centre de documentation
scientifique

Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])  

Public [article]  inGeocarto international > vol 37 n° 3 [01/02/2022] . - pp 792 - 812 Titre : Building footprint extraction in Yangon city from monocular optical satellite image using deep learning Type de document : Article/Communication Auteurs : Hein Thura Aung, Auteur ; Sao Hone Pha, Auteur ; Wataru Takeuchi, Auteur Année de publication : 2022 Article en page(s) : pp 792 - 812 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Birmanie [Termes IGN] détection du bâti [Termes IGN] empreinte [Termes IGN] image Geoeye [Termes IGN] image isolée [Termes IGN] réseau antagoniste génératif [Termes IGN] vision monoculaire Résumé : (auteur) In this research, building footprints in Yangon City, Myanmar are extracted only from monocular optical satellite image by using conditional generative adversarial network (CGAN). Both training dataset and validating dataset are created from GeoEYE image of Dagon Township in Yangon City. Eight training models are created according to the change of values in three training parameters; learning rate, β1 term of Adam, and number of filters in the first convolution layer of the generator and the discriminator. The images of the validating dataset are divided into four image groups; trees, buildings, mixed trees and buildings, and pagodas. The output images of eight trained models are transformed to the vector images and then evaluated by comparing with manually digitized polygons using completeness, correctness and F1 measure. According to the results, by using CGAN, building footprints can be extracted up to 71% of completeness, 81% of correctness and 69% of F1 score from only monocular optical satellite image. Numéro de notice : A2022-345 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1740949 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1740949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100526 [article]