ATLANTIS: A benchmark for semantic segmentation of waterbody images. (March 2022)

• Record Type: Journal Article
• Authors: Erfani, Seyed Mohammad Hassan; Wu, Zhenyao; Wu, Xinyi; Wang, Song; Goharian, Erfan
• Is Part Of: Environmental modelling & software. Volume 149(2022)
• Publication Date: 2022-03
• Subjects: Water resources engineering -- Computer vision -- Waterbody image dataset -- Semantic segmentation -- Deep learning models; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105333
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: ATLANTIS, a large-scale dataset for semantic segmentation of waterbodies and water-related structures is introduced. ATeX (ATLANTIS TeXture), a new benchmark for classification and texture analysis of water in different waterbodies is introduced. AQUANet, a novel network, developed for semantic segmentation of waterbodies achieves the best performance on ATLANTIS.