Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck

Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods.However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods.Self-supervised learning using

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Development of human-in-the-loop experiment system to extract evacuation behavioral features: A case of evacuees in nuclear emergencies

Evacuation time estimation (ETE) is crucial for the effective implementation of resident protection measures as well as planning, owing to its applicability to nuclear emergencies.However, as confirmed in the Fukushima case, the ETE performed by nuclear operators does not reflect behavioral features, exposing thus, gaps that are likely to appear in

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