Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction

• URL: http://arxiv.org/abs/2404.10626v1
• Date: Tue, 16 Apr 2024 14:52:15 GMT
• Title: Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction
• Authors: John Francis, Stephen Law,
• Abstract summary: Methods for adapting a trained UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively.
• Score: 1.2277343096128712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively.

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