On the Deployment of Post-Disaster Building Damage Assessment Tools using Satellite Imagery: A Deep Learning Approach-Reference-Cited by-同舟云学术

On the Deployment of Post-Disaster Building Damage Assessment Tools using Satellite Imagery: A Deep Learning Approach

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Data Mining Workshops (ICDMW)
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Author:

Gholami Shahrzad¹,Robinson Caleb¹,Ortiz Anthony¹,Yang Siyu¹,Margutti Jacopo²,Birge Cameron¹,Dodhia Rahul¹,Ferres Juan Lavista¹

Affiliation:

1. AI for Good Research Lab, Microsoft,Redmond,USA

2. 510 an initiative of the Netherlands Red Cross,The Hague,The Netherlands

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10029378/10029381/10031100.pdf?arnumber=10031100

Reference33 articles.

1. DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

2. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

3. Deep High-Resolution Representation Learning for Visual Recognition

4. Attention-based two-stream high-resolution networks for building dam-age assessment from satellite imagery;oludare;Multimodal Image Exploitation and Learning 2022,2022

5. A theory of learning from different domains

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