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

arXiv: https://arxiv.org/abs/2404.10626 · HTML: https://mlsanity.com/paper/2404.10626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery](https://mlsanity.com/paper/1910.12060/) (2019)
- [ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks](https://mlsanity.com/paper/1907.12859/) (2019)
- [GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates](https://mlsanity.com/paper/2304.07750/) (2023)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)

## Abstract (arXiv)

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.