# Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery

arXiv: https://arxiv.org/abs/2012.04222 · HTML: https://mlsanity.com/paper/2012.04222/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Fast building segmentation from satellite imagery and few local labels](https://mlsanity.com/paper/2206.05377/) (2022)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [Handling Image and Label Resolution Mismatch in Remote Sensing](https://mlsanity.com/paper/2211.15790/) (2022)
- [Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts](https://mlsanity.com/paper/2505.14088/) (2025)
- [AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.13227/) (2021)
- [On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data](https://mlsanity.com/paper/2310.10490/) (2023)

## Abstract (arXiv)

Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully connected neural networks for semantic segmentation. The benchmark datasets available for training deep segmentation models in remote sensing imagery tend to be small, however, often consisting of only a handful of images from a single location with a single scale. This limits the models' ability to generalize to other datasets. Domain adaptation has been proposed to improve the models' generalization but we find these approaches are not effective for dealing with the scale variation commonly found between remote sensing image collections. We therefore propose a scale aware adversarial learning framework to perform joint cross-location and cross-scale land-cover classification. The framework has a dual discriminator architecture with a standard feature discriminator as well as a novel scale discriminator. We also introduce a scale attention module which produces scale-enhanced features. Experimental results show that the proposed framework outperforms state-of-the-art domain adaptation methods by a large margin.