Scope of Workshop

Oceans cover 71% of the Earth's surface, yet much of the underwater domain remains unexplored. Effective surveillance is essential for environmental protection, security, and industrial monitoring. Core objectives include preserving marine ecosystems, monitoring fisheries, detecting aquatic diseases, and preventing illegal activities. Automated systems with advanced imaging and sensors support continuous coastal monitoring by identifying anomalies through audio and video data.

Underwater communication has advanced significantly, enabling data exchange between submerged vehicles, sensors, and surface stations. Challenges such as signal attenuation and scattering affect both optical and acoustic data quality. This workshop will explore recent developments in underwater vision, sensor fusion, AI-based analytics, and communication technologies. Topics include deep learning for image enhancement, generative models for data augmentation, autonomous underwater vehicles, and acoustic communication protocols. The workshop aims to connect researchers and industry experts to drive innovation in underwater surveillance.

Call for Papers

We invite original and high-quality submissions in the domain of underwater surveillance and related technologies. Authors are encouraged to contribute papers that present novel research, practical applications, or insightful reviews. AUSTech will held in conjunction of ICPR 2026 in Lyon France (17 August-22 August 2026).

Submit your papers via CMT: https://cmt3.research.microsoft.com/AUSTech2026

📰 Download the Flyer: AUSTech2026_Flyer.pdf

Timeline

Submission deadline: 01 May 2026
Notification of acceptance: 15 July 2026
Camera-ready version: 28 July 2026

Keynote Speakers

Prof. Anastasia Zakharova, La Rochelle Université, France

ORCID: 0009-0006-8725-4063

Title: Graph Neural Networks for MOS in Underwater Environment: An Inductive Approach.

Biography: Prof. Zakharova works in graph based machine learning with a focus on complex environmental data. Her recent work explores inductive graph neural models that can generalize to new sensing nodes without retraining. She applies these methods to underwater monitoring where data is often sparse, noisy, and expensive to collect. Her contributions support real time interpretation of marine observation systems.

Prof. Monika Aggarwal, Indian Institute of Technology Delhi, India

Google Scholar: Prof. Monika Aggarwal

Title: To be shared.

Biography: Prof. Monika Aggarwal is a faculty member in the Department of Electrical Engineering at the Indian Institute of Technology Delhi. Her research spans statistical signal processing, underwater sensing, and communication systems, with a strong focus on reliable acoustic–optical perception for marine environments. She has led multiple sponsored projects in underwater communication and autonomous systems and has contributed significantly to energy-aware algorithms for long-term marine monitoring. Her work integrates learning-based signal processing with field-deployable sensing platforms, supporting advances in underwater surveillance, environmental intelligence, and next-generation maritime robotics.

Topics of Interest

1. Underwater Imaging and Vision Systems
   • Optical vs. acoustic imaging techniques
   • Image enhancement and restoration (dehazing, super-resolution, low-light imaging)
   • Object detection, tracking, and deep learning models (Vision Transformers, generative models)
2. Sensor Fusion for Underwater Perception
   • Multi-modal fusion of optical, sonar, and LiDAR data
   • AI-based real-time sensor fusion and 3D reconstruction
3. Underwater Communication and Networking
   • Acoustic, optical, and RF communication challenges
   • AI-driven underwater networking and energy-efficient protocols
4. Autonomous Underwater Vehicles (AUVs) and Robotics
   • AI-powered navigation, deep reinforcement learning for path planning
   • Swarm intelligence for multi-robot collaboration
5. Applications of Underwater Surveillance
   • Defense and security: naval threat detection
   • Environmental monitoring and marine conservation
   • Industrial inspection: pipelines, offshore structures, and ship hulls
6. Challenges and Future Directions
   • Domain adaptation for underwater AI models
   • Data annotation and real-time processing constraints
   • Emerging trends: quantum sensing, bio-inspired robotics, adaptive vision