Building footprint extraction in Yangon city from monocular optical satellite image using deep learning. Issue 3 (1st February 2022)

• Record Type: Journal Article
• Authors: Aung, Hein Thura; Pha, Sao Hone; Takeuchi, Wataru
• Is Part Of: Geocarto international. Volume 37:Issue 3(2022)
• Page Start: 792
• Page End: 812
• Publication Date: 2022-02-01
• Subjects: GeoEYE monocular RGB image -- learning rate -- momentum -- pix2pix -- pixel-based evaluation; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2020.1740949
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: 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, β 1 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.