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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07542 |
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| _version_ | 1866917981927243776 |
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| author | Lyu, Hongyu Berrio, Julie Stephany Shan, Mao Worrall, Stewart |
| author_facet | Lyu, Hongyu Berrio, Julie Stephany Shan, Mao Worrall, Stewart |
| contents | Autonomous Vehicles (AVs) are being partially deployed and tested across various global locations, including China, the USA, Germany, France, Japan, Korea, and the UK, but with limited demonstrations in Australia. The integration of machine learning (ML) into AV perception systems highlights the need for locally labelled datasets to develop and test algorithms in specific environments. To address this, we introduce SydneyScapes - a dataset tailored for computer vision tasks of image semantic, instance, and panoptic segmentation. This dataset, collected from Sydney and surrounding cities in New South Wales (NSW), Australia, consists of 756 images with high-quality pixel-level annotations. It is designed to assist AV industry and researchers by providing annotated data and tools for algorithm development, testing, and deployment in the Australian context. Additionally, we offer benchmarking results using state-of-the-art algorithms to establish reference points for future research and development. The dataset is publicly available at https://hdl.handle.net/2123/33051. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SydneyScapes: Image Segmentation for Australian Environments Lyu, Hongyu Berrio, Julie Stephany Shan, Mao Worrall, Stewart Computer Vision and Pattern Recognition Autonomous Vehicles (AVs) are being partially deployed and tested across various global locations, including China, the USA, Germany, France, Japan, Korea, and the UK, but with limited demonstrations in Australia. The integration of machine learning (ML) into AV perception systems highlights the need for locally labelled datasets to develop and test algorithms in specific environments. To address this, we introduce SydneyScapes - a dataset tailored for computer vision tasks of image semantic, instance, and panoptic segmentation. This dataset, collected from Sydney and surrounding cities in New South Wales (NSW), Australia, consists of 756 images with high-quality pixel-level annotations. It is designed to assist AV industry and researchers by providing annotated data and tools for algorithm development, testing, and deployment in the Australian context. Additionally, we offer benchmarking results using state-of-the-art algorithms to establish reference points for future research and development. The dataset is publicly available at https://hdl.handle.net/2123/33051. |
| title | SydneyScapes: Image Segmentation for Australian Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.07542 |