Self-Supervised Pretraining With Monocular Height Estimation for Semantic Segmentation-Reference-Cited by-同舟云学术

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Self-Supervised Pretraining With Monocular Height Estimation for Semantic Segmentation

Published:2024 Issue: Volume:62 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Xiong Zhitong¹^ORCID,Chen Sining¹,Shi Yilei²,Zhu Xiao Xiang³^ORCID

Affiliation:

1. Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany

2. School of Engineering and Design, Technical University of Munich (TUM), Munich, Germany

3. Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany

Funder

German Federal Ministry for Economic Affairs and Climate Action

German Federal Ministry of Education and Research

Bundesministerium f?r Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10604292.pdf?arnumber=10604292

Reference56 articles.

1. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

2. EarthNets: Empowering AI in Earth observation;Xiong;arXiv:2210.04936,2022

3. THE Benchmark: Transferable Representation Learning for Monocular Height Estimation

4. 2019 Data Fusion Contest [Technical Committees]

5. Depth map prediction from a single image using a multi-scale deep network;Eigen

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