Strategies in training deep learning models to extract building from multisource images with small training sample sizes-Reference-Cited by-同舟云学术

Strategies in training deep learning models to extract building from multisource images with small training sample sizes

Published:2023-05-09 Issue:1 Volume:16 Page:1707-1724
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Abriha Dávid¹^ORCID,Szabó Szilárd¹^ORCID

Affiliation:

1. Department of Physical Geography and Geoinformatics, Faculty of Science and Technology, University of Debrecen, Debrecen, Hungary

Funder

Thematic Excellence Programme

Ministry for Innovation and Technology in Hungary

National Research, Development and Innovation Fund

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2210312

Reference100 articles.

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2. Integrating semantic edges and segmentation information for building extraction from aerial images using UNet

3. An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images

4. Moving towards a sustainable environment: The dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China

5. Automatic Building Extraction on Satellite Images Using Unet and ResNet50

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