Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction
This work addresses domain adaptation for urban canopy prediction using remote sensing data, offering incremental improvements in efficiency for environmental monitoring applications.
The paper tackled adapting a trained multi-task UNet for urban canopy prediction to new geographic settings without extensive fine-tuning, finding that selective image matching methods, such as pixel distribution and Fourier domain adaptation, outperformed baselines in zero-shot and few-sample scenarios.
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.