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Sonar Image Datasets: A Comprehensive Survey of Resources, Challenges, and Applications

Larissa S. Gomes, Gustavo P. Almeida, Bryan U. Moreira, Marco Quiroz, Breno Xavier, Lucas Soares, Stephanie L. Brião, Felipe G. Oliveira, Paulo L. J. Drews-Jr

TL;DR

This paper addresses the scarcity and fragmentation of publicly available sonar datasets by providing a comprehensive catalog of datasets across five core modalities—Side-Scan Sonar, Forward-Looking Sonar, Synthetic Aperture Sonar, Multibeam Echosounder, and DIDSON. It maps datasets to tasks such as classification, detection, segmentation, and 3D reconstruction, and presents a master table and a chronological timeline to illuminate resource availability and evolution (2020–2025). By analyzing dataset characteristics, annotation schemes, and typical applications, the work highlights gaps and offers a roadmap to standardize benchmarking and accelerate progress in underwater acoustic perception. The study’s practical impact lies in furnishing researchers and developers with a structured reference to locate relevant data, understand modality-specific constraints, and plan multi-sensor or cross-domain analyses for robust underwater robotics and exploration.

Abstract

Sonar images are relevant for advancing underwater exploration, autonomous navigation, and ecosystem monitoring. However, the progress depends on data availability. The scarcity of publicly available, well-annotated sonar image datasets creates a significant bottleneck for the development of robust machine learning models. This paper presents a comprehensive and concise review of the current landscape of sonar image datasets, seeking not only to catalog existing resources but also to contextualize them, identify gaps, and provide a clear roadmap, serving as a base guide for researchers of any kind who wish to start or advance in the field of underwater acoustic data analysis. We mapped publicly accessible datasets across various sonar modalities, including Side Scan Sonar (SSS), Forward-Looking Sonar (FLS), Synthetic Aperture Sonar (SAS), Multibeam Echo Sounder (MBES), and Dual-Frequency Identification Sonar (DIDSON). An analysis was conducted on applications such as classification, detection, segmentation, and 3D reconstruction. This work focuses on state-of-the-art advancements, incorporating newly released datasets. The findings are synthesized into a master table and a chronological timeline, offering a clear and accessible comparison of characteristics, sizes, and annotation details datasets.

Sonar Image Datasets: A Comprehensive Survey of Resources, Challenges, and Applications

TL;DR

This paper addresses the scarcity and fragmentation of publicly available sonar datasets by providing a comprehensive catalog of datasets across five core modalities—Side-Scan Sonar, Forward-Looking Sonar, Synthetic Aperture Sonar, Multibeam Echosounder, and DIDSON. It maps datasets to tasks such as classification, detection, segmentation, and 3D reconstruction, and presents a master table and a chronological timeline to illuminate resource availability and evolution (2020–2025). By analyzing dataset characteristics, annotation schemes, and typical applications, the work highlights gaps and offers a roadmap to standardize benchmarking and accelerate progress in underwater acoustic perception. The study’s practical impact lies in furnishing researchers and developers with a structured reference to locate relevant data, understand modality-specific constraints, and plan multi-sensor or cross-domain analyses for robust underwater robotics and exploration.

Abstract

Sonar images are relevant for advancing underwater exploration, autonomous navigation, and ecosystem monitoring. However, the progress depends on data availability. The scarcity of publicly available, well-annotated sonar image datasets creates a significant bottleneck for the development of robust machine learning models. This paper presents a comprehensive and concise review of the current landscape of sonar image datasets, seeking not only to catalog existing resources but also to contextualize them, identify gaps, and provide a clear roadmap, serving as a base guide for researchers of any kind who wish to start or advance in the field of underwater acoustic data analysis. We mapped publicly accessible datasets across various sonar modalities, including Side Scan Sonar (SSS), Forward-Looking Sonar (FLS), Synthetic Aperture Sonar (SAS), Multibeam Echo Sounder (MBES), and Dual-Frequency Identification Sonar (DIDSON). An analysis was conducted on applications such as classification, detection, segmentation, and 3D reconstruction. This work focuses on state-of-the-art advancements, incorporating newly released datasets. The findings are synthesized into a master table and a chronological timeline, offering a clear and accessible comparison of characteristics, sizes, and annotation details datasets.

Paper Structure

This paper contains 6 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Sonar Types and their Features
  4. Overview of Sonar Image Datasets
  5. Challenges and Applications
  6. Conclusions

Figures (2)

  • Figure 1: Sonar images: (a) Side Scan Sonar, (b) Forward Looking Sonar, (c) Synthetic Aperture Sonar, (d) Multibeam Echo Sounder, and (e) DIDSON.
  • Figure 2: Timeline of sonar dataset publications (2020–2025). Colorful lines/arrows by modality: SSS (blue), FLS (green), SAS (purple), MBES (orange), DIDSON (red). Hybrid cases appear with two parallel arrows.