Recently, the Port of Klaipėda has encountered increasing cases of GPS signal interference. Currently, there is no alert system in place—neither for those participating in vessel traffic nor for the port’s cargo handling companies that depend on GPS signals for their operations. Such disruptions create serious risks for safe navigation, especially in zones with dense traffic of ships and tugboats and can disrupt the smooth execution of cargo handling tasks.

Interference of this kind may result in inaccurate navigation, hazardous maneuvers, or even incidents. Reliable positioning information is becoming ever more crucial for the safety and efficiency of port operations.

Challenge objective:

Design a real-time GPS interference detection and warning system that will automatically notify vessels and cargo operation participants in case of signal disruptions. The system should:

– Provide clear and tangible benefits to end-users by delivering timely alerts;

– Include a technological concept for how an innovative platform can enable quick identification of positioning issues;

– Reduce the risk of navigation errors;

– And help prevent potential incidents within the port area for all who rely on GPS-based equipment.

Port operations are increasingly relying on digitalisation to enhance efficiency in logistics processes. The accurate identification of transportation units such as containers, tank containers, and trailers remains a critical task for achieving more efficient terminal operations.

This challenge calls for the development of a robust computer vision system capable of reliably identifying transportation units.

The solution should address five core components:

– Segmentation of transportation unit instances (instance segmentation);

– Detection of the ISO6346 compliant ID text area (text detection);

– Recognition of the detected ID text area (text recognition);

– Validation of the extracted ID text using the check digit;

– Association of the detected transportation unit segment with the extracted ID code.

The system must perform effectively with both aerial and ground-level imagery, adapting to varying lighting conditions, weather, and camera angles encountered in port environments. These conditions are present in the TRUDI dataset.

The ferry is a vital lifeline between Klaipėda and Smiltynė. While some visitors use private boats to reach Smiltynė, many return to the city by ferry and often without a valid ticket. This causes financial losses for the operator, reduces overall service quality, and frustrates passengers who pay their fare. Ensuring that every traveler holds a valid ticket is essential to keeping the journey fair, efficient, and sustainable.

Challenge Goal

Design an innovative, sustainable, and passenger-friendly solution that guarantees fair ticket control on ferries to and from Smiltynė.

The solution should emphasize:

– Smart technological innovations;

– Sustainable use of resources;

– Passenger service practices that are simple and convenient;

– A clear vision of how the idea will improve service quality, ensure fairness, and support long-term sustainability.

Klaipėda Free Economic Zone (FEZ) is a dynamic community of modern companies that implement advanced technologies and often conduct research and development activities. Covering 412 hectares of industrial land, the zone hosts more than 100 businesses. Strategically located, Klaipėda FEZ is seamlessly connected to major local and international road, rail, and sea transport corridors.

To reduce transport-related emissions, increase logistics efficiency, and optimize electricity use, Klaipėda FEZ is committed to adopting green energy solutions that will also lower energy costs across the cluster of companies operating within the zone.

To achieve this, the FEZ aims to leverage artificial intelligence (AI) and unmanned technologies (including autonomous trucks, robots, and drones). These innovations would help optimize the movement of employees and goods as well as improve energy efficiency within the FEZ territory.

The challenge is to design a digital platform that integrates multiple functions and simulations, such as:

– Identifying and modeling alternative ways for employees, raw materials, and products (including heavy truck transport) to access and move within the FEZ, focusing on efficiency and CO₂ reduction;

– Exploring solutions for autonomous vehicles, public transport, shared mobility, and route optimization;

– Forecasting energy demand and storage needs, including smart charging for both companies and electric vehicles;

– Integrating existing monitoring systems with Narrow Band IoT technologies for unified and efficient management.

Ignitis Renewables is a leading international green energy company operating in the Baltic States and Poland. The company specialises in sustainable power generation and green flexibility solutions. It develops onshore and offshore wind projects, solar energy installations, and advanced energy storage and conversion technologies, including batteries and power-to-X (P2X) systems. Ignitis Renewables is actively working to implement the Ignitis Group’s strategic vision of achieving 4–5 GW of operational green capacity by 2030, contributing to the transition toward a cleaner and more resilient energy future in the region.

Offshore wind farms are a crucial component of Lithuania’s renewable energy strategy and its journey toward energy independence. To protect crews, ensure vessel integrity, and support uninterrupted operations, it is essential to maintain safe and efficient vessel transit between ports and offshore sites.

The objective of this challenge is to create innovative solutions that enhance the resilience of vessel transits between Lithuanian ports and offshore wind farm sites. 

Proposed solutions should:

– Safeguard crews and vessels from physical and cyber risks.

– Utilise AI, autonomous systems, and unmanned technologies.

– Enhance operational efficiency while promoting environmentally sustainable practices.

This challenge presents a unique opportunity to enhance renewable energy security, drive innovation in maritime technology, and support the safe and sustainable development of offshore wind operations in the Baltic region.

Ports are strategic assets in national defense, serving as critical nodes for military logistics, naval operations, and emergency response. In today’s evolving threat landscape, these infrastructures face increasing risks from hostile actions, sabotage, climate-related events, and maritime accidents. Traditional static defenses are often insufficient, slow to deploy, and vulnerable to dynamic threats.

Challenge Objective

Design and propose an innovative floating protection system that enhances the security, resilience, and operational continuity of port infrastructure in defense scenarios.

Key Requirements

– Threat Mitigation: Protect against physical threats such as vessel collisions, explosive impacts, and unauthorized access.

– Rapid Deployment: Enable fast installation and relocation in response to changing tactical needs.- Modularity & Scalability: Adapt to different port sizes and configurations, including temporary or mobile bases.

– Smart Integration: Include technologies for surveillance, threat detection, and automated response (e.g., sensors, drones, AI systems).

– Environmental Resilience: Withstand extreme weather, rising sea levels, and wave energy while minimizing ecological impact.

Deliverables

1. Conceptual Design – Visuals, 3D models, or simulations of the proposed system.

2. Technical Report – Covering:
o Materials and construction
o Deployment and anchoring strategy
o Threat response capabilities
o Cost and logistical considerations

3. (Optional) Prototype or scaled model for demonstration

Problem

Maritime transport in the Baltic Sea region generates vast amounts of data (AIS, port calls, traffic statistics), yet decision-makers often face challenges in interpreting this information for operational planning, environmental monitoring, and supply chain optimization. Traditional analytics struggle to integrate diverse datasets (traffic, CO₂ emissions, congestion, energy consumption) into actionable insights.

Task

Fine-tune an open-source Large Language Model (LLM) with historical AIS cargo traffic data (1.3M events across Sweden, Finland, Estonia, Latvia, Lithuania, Poland, 2021–2022). Develop instruction-dataset, prompts, and conversational use cases where the LLM provides decision support on:

– Forecasting vessel arrivals at ports,

– Identifying congestion risks and scheduling alternatives,

– Estimating CO₂ emissions and energy consumption,

– Enhancing situational awareness for logistics chains.

Expected Outcome

Prototype an AI assistant capable of generating human-like discussions and scenario simulations for maritime logistics. The solution should demonstrate an instruction dataset proposal and how fine-tuned LLMs can support smarter, greener, and more resilient port operations in the Baltic Sea.