If you are a power grid operator dealing with vegetation growing too close to power lines — this project developed a multi-modal sensing system that creates a live Digital Twin to eliminate wildfire risks.
Modular AI-Powered Aerial Sensing System for Infrastructure and Environmental Monitoring
Imagine a set of Lego bricks for high-tech drone sensors. Instead of buying a fixed camera or scanner, you can swap different sensor pods and controllers depending on what you need to see. It's like having a Swiss Army knife for the sky that can process data instantly using AI to spot problems on the ground.
What needed solving
Infrastructure monitoring in mining and energy is currently expensive and slow because tools are custom-built for every single use case. This leads to higher risks of wildfires and safety hazards due to inefficient vegetation and slope monitoring.
What was built
A modular sensor system consisting of multi-modal pods and a detachable NVIDIA GPU-based AI controller with a patent-pending micro-stabilizer.
Who needs this
Who can put this to work
If you are a mining site manager dealing with slope sliding and safety hazards — this project developed an all-in-one data collection system that improves operational efficiency and safety monitoring.
If you are a forestry agency dealing with high operational costs of custom-built monitoring tools — this project developed a modular sensor platform that reduces costs through a scalable, mix-and-match architecture.
Quick answers
How does this affect operational costs?
Based on available project data, the system replaces expensive custom-built solutions with a modular architecture, making data collection more cost-efficient.
Can this be scaled for different industries?
Yes, the 'Lego-brick' style architecture allows users to mix and match components for varied industrial, environmental, and infrastructure needs.
What intellectual property is protected?
The project has filed a patent protecting key innovations in stabilization and integration technologies, specifically a micro-stabilizer for Y-parallax compensation.
How is the AI integrated into the hardware?
The system uses a detachable controller compatible with NVIDIA GPU-based computing platforms for real-time onboard AI processing.
What is the current deployment timeline?
The project runs from 2024-11-01 to 2026-10-31, with customer trials already commenced ahead of schedule.
Who built it
The project is led by a single SME, AISPECO (UAB), based in Lithuania. With a 100% industry ratio and a single partner, the project is streamlined for rapid commercialization and direct market application rather than academic research.
Contact AISPECO, UAB in Lithuania
Talk to the team behind this work.
Contact us to connect with AISPECO for modular sensing integration.