Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework

Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework

Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves segmenting all buildings and classifying their dam...

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Bibliographic Details
Main Authors: Songxi Yang, Bo Peng, Tang Sui, Meiliu Wu, Qunying Huang
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Remote Sensing
Subjects:
remote sensing
change detection
damage assessment
self-supervised learning
contrastive learning
vision transformer
Online Access:https://www.mdpi.com/2072-4292/17/15/2717
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