Dense prediction of label noise for learning building extraction from aerial drone imagery-Reference-Cited by-同舟云学术

Dense prediction of label noise for learning building extraction from aerial drone imagery

Published:2021-10-01 Issue:23 Volume:42 Page:8906-8929
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Ahmed Nahian¹^ORCID,Rahman Rashedur M.¹^ORCID,Adnan Mohammed Sarfaraz Gani²^ORCID,Ahmed Bayes³

Affiliation:

1. Department of Electrical and Computer Engineering, School of Engineering and Physical Sciences, North South University, Bashundhara, Dhaka, Bangladesh

2. Department of Urban and Regional Planning, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh

3. Institute for Risk and Disaster Reduction (IRDR), University College London (UCL), London, UK

Funder

Bashundhara, Dhaka 1229, Bangladesh

Faculty Research Grant, North South University

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2021.1973685

Reference33 articles.

1. Learning to extract buildings from ultra-high-resolution drone images and noisy labels

2. Learning from noisy examples

3. Deep learning-based multi-feature semantic segmentation in building extraction from images of UAV photogrammetry

4. Building extraction from remote sensing images with deep learning in a supervised manner

5. Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage

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