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Vision-based semantic segmentation of waterbodies and nearby related objectsprovides important information for managing water resources and handlingflooding emergency. However, the lack of large-scale labeled training andtesting datasets for water-related categories prevents researchers fromstudying water-related issues in the computer vision field. To tackle thisproblem, we present ATLANTIS, a new benchmark for semantic segmentation ofwaterbodies and related objects. ATLANTIS consists of 5,195 images ofwaterbodies, as well as high quality pixel-level manual annotations of 56classes of objects, including 17 classes of man-made objects, 18 classes ofnatural objects and 21 general classes. We analyze ATLANTIS in detail andevaluate several state-of-the-art semantic segmentation networks on ourbenchmark. In addition, a novel deep neural network, AQUANet, is developed forwaterbody semantic segmentation by processing the aquatic and non-aquaticregions in two different paths. AQUANet also incorporates low-level featuremodulation and cross-path modulation for enhancing feature representation.Experimental results show that the proposed AQUANet outperforms otherstate-of-the-art semantic segmentation networks on ATLANTIS. We claim thatATLANTIS is the largest waterbody image dataset for semantic segmentationproviding a wide range of water and water-related classes and it will benefitresearchers of both computer vision and water resources engineering.

ATLANTIS: A Benchmark for Semantic Segmentation of Waterbody Images