ATLANTIS: A Benchmark for Semantic Segmentation of Waterbody Images

TL;DR

ATLANTIS is a comprehensive benchmark dataset with over 5,000 waterbody images and detailed annotations, designed to advance semantic segmentation research for water resource management and flooding emergencies.

Contribution

This paper introduces ATLANTIS, the largest waterbody image dataset with detailed annotations, and proposes AQUANet, a novel neural network architecture for improved waterbody segmentation.

Findings

AQUANet outperforms existing segmentation networks on ATLANTIS.

ATLANTIS provides extensive water-related classes for research.

The benchmark facilitates progress in water resource computer vision applications.

Abstract

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.