Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction
<|reference_start|>Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction: 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.<|reference_end|>
@article{francis2024exploring,
    title={Exploring selective image matching methods for zero-shot and few-sample
  unsupervised domain adaptation of urban canopy prediction},
    author={John Francis and Stephen Law},
    journal={arXiv preprint arXiv:2404.10626},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10626},
    primaryClass={cs.CV stat.AP}
}