mlsanityData to 7 Oct 2026

ATLANTIS: A Benchmark for Semantic Segmentation of Waterbody Images

arXiv 2111.11567 ↗First posted 22 Nov 2021cs.CVcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 22 Nov 2021.

Builds on

34 of 56 references.

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22 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 2.

10 papers.

  1. 2023CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentationcited by 215
  2. 2024CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentationcited by 33
  3. 2023What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentationcited by 24
  4. 2023Stable Segment Anything Modelcited by 6
  5. 2024High-Quality Mask Tuning Matters for Open-Vocabulary Segmentationcited by 5
  6. 2025Show or Tell? Effectively prompting Vision-Language Models for semantic segmentationcited by 4
  7. 2024MoPEFT: A Mixture-of-PEFTs for the Segment Anything Modelcited by 3
  8. 2025Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Imagescited by 2
  9. 2026Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Taskscited by 0
  10. 2026Preference-Guided Adaptation for Open-Vocabulary Semantic Segmentation via Prompt Disagreementcited by 0