Cityscape‑Adverse A benchmark for evaluating semantic segmentation robustness under realistic adverse conditions. Overview Cityscape‑Adverse extends the original Cityscapes dataset by applying eight realistic environmental modifications—rainy, foggy, spring, autumn, snowy, sunny, night, and dawn—using diffusion‑based image editing. All transformations preserve the original 2048×1024 semantic labels, enabling direct evaluation of model robustness in out‑of‑distribution scenarios. Paper & Citation Cityscape‑Adverse: Benchmarking Robustness of Semantic Segmentation with Realistic Scene Modifications via Diffusion‑Based Image Editing N. Suryanto, A. A. Adiputra, A. Y. Kadiptya, T. T. H. Le, D. Pratama, Y. Kim, and H. Kim, IEEE Access , 2025. DOI: 10.1109/ACCESS.2025.3537981 ArXiv: arxiv.org/abs/2411.00425 Dataset Summary Base dataset : Cityscapes (finely annotated images; 2975 train / 500 val publicly available) Modifications : Weathers : rainy, foggy Seasons : spring, autumn, snowy Lightings : sunny, night, dawn Generation : Diffusion‑based instruction editing with CosXLEdit Prompts e.g. "change the weather to foggy, photo realistic" , guidance scales 5–7, 20 diffusion steps. Resoluti…
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