Publication Abstracts

Erfani et al. 2022

Erfani, S.M.H., Z. Wu, X. Wu, S. Wang, and E. Goharian, 2022: ATLANTIS: A benchmark for semantic segmentation of waterbody images. Environ. Model. Softw., 149, 105333, doi:10.1016/j.envsoft.2022.105333.

Vision-based semantic segmentation of waterbodies and nearby related objects provides important information for managing water resources and handling flooding emergency. However, the lack of large-scale labeled training and testing datasets for water-related categories prevents researchers from studying water-related issues in the computer vision field. To tackle this problem, we present ATLANTIS, a new benchmark for semantic segmentation of waterbodies and related objects. ATLANTIS consists of 5,195 images of waterbodies, as well as high quality pixel-level manual annotations of 56 classes of objects, including 17 classes of man-made objects, 18 classes of natural objects and 21 general classes. We analyze ATLANTIS in detail and evaluate several state-of-the-art semantic segmentation networks on our benchmark. In addition, a novel deep neural network, AQUANet, is developed for waterbody semantic segmentation by processing the aquatic and non-aquatic regions in two different paths. AQUANet also incorporates low-level feature modulation and cross-path modulation for enhancing feature representation. Experimental results show that the proposed AQUANet outperforms other state-of-the-art semantic segmentation networks on ATLANTIS. We claim that ATLANTIS is the largest waterbody image dataset for semantic segmentation providing a wide range of water and water-related classes and it will benefit researchers of both computer vision and water resources engineering.

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BibTeX Citation

@article{er05000f,
  author={Erfani, S. M. H. and Wu, Z. and Wu, X. and Wang, S. and Goharian, E.},
  title={ATLANTIS: A benchmark for semantic segmentation of waterbody images},
  year={2022},
  journal={Environmental Modelling and Software},
  volume={149},
  pages={105333},
  doi={10.1016/j.envsoft.2022.105333},
}

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RIS Citation

TY  - JOUR
ID  - er05000f
AU  - Erfani, S. M. H.
AU  - Wu, Z.
AU  - Wu, X.
AU  - Wang, S.
AU  - Goharian, E.
PY  - 2022
TI  - ATLANTIS: A benchmark for semantic segmentation of waterbody images
JA  - Environ. Model. Softw.
JO  - Environmental Modelling and Software
VL  - 149
SP  - 105333
DO  - 10.1016/j.envsoft.2022.105333
ER  -

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