Deep Learning for Cross-Domain Building Change Detection from Multi-Source Very High-Resolution Satellite Imagery
Author:
Affiliation:
1. Paris Lodron University of Salzburg (PLUS),Christian Doppler Laboratory for Geospatial and EO-Based Humanitarian Technologies (GEOHUM)
Funder
Borders
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641261.pdf?arnumber=10641261
Reference22 articles.
1. BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING
2. Developing a Framework for Rapid Collapsed Building Mapping Using Satellite Imagery and Deep Learning Models
3. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters
4. AFDE-Net: Building Change Detection Using Attention-Based Feature Differential Enhancement for Satellite Imagery
5. BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING
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