LAST: Utilizing Synthetic Image Style Transfer to Tackle Domain Shift in Aerial Image Segmentation

Recent deep learning models often struggle with performance degradation due to domain shifts. Addressing domain adaptation in aerial image segmentation is challenging due to the limited availability of training data. To tackle this, we utilized the Unreal Engine to construct a synthetic dataset featuring images captured under diverse conditions such as fog, snow, and nighttime settings. We then proposed a latent space style transfer model that generates alternate domain versions based on the real aerial dataset. This approach eliminates the need for additional annotations on shifted domain data. We benchmarked nine different state-of-the-art segmentation methods on the ISPRS Vaihingen, Potsdam datasets, and their shifted foggy domains. Extensive experiments reveal that domain shift leads to significant performance drops, with an average decrease of - 3.46% mIoU on Vaihingen and -5.22% mIoU on Potsdam. Finally, we adapted the model to perform well in the shifted domain, achieving improvements of +2.97% mIoU on Vaihingen and +3.97% mIoU on Potsdam, while maintaining its effectiveness in the original domain.