Semi-Supervised Building Footprint Generation With Feature and Output Consistency Training
Author:
Affiliation:
1. Chair of Data Science in Earth Observation, Technische Universität München (TUM), Munich, Germany
2. Remote Sensing Technology, Technische Universität München (TUM), Munich, Germany
Funder
Bundesministerium fr Wirtschaft und Energie
Helmholtz Association
Bundesministerium fr Bildung und Forschung
Bavarian State Office for Digitizing Broadband and Survey
H2020 European Research Council
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09773314.pdf?arnumber=9773314
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1. Representation Learning: A Review and New Perspectives
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3. Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
4. Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
5. HA U-Net: Improved Model for Building Extraction From High Resolution Remote Sensing Imagery
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