The Tisza’s pollution crisis is driven by a systemic "plastic flood" originating from the steep Carpathian regions of Ukraine and Romania. Due to a lack of waste management infrastructure and the absence of flat land for landfills, communal waste is frequently deposited in floodplains or illegal riverside dumps. During seasonal snowmelts or heavy rains, the rising river sweeps this accumulated waste downstream, carrying thousands of tons of PET bottles and industrial debris into Hungary, where it settles in protected ecosystem floodplains.
The core challenge is a lack of transboundary accountability; once the waste is in the water, it is difficult to prove its point of origin. Our project addresses this by using on-site ML cameras to log the exact timing and volume of debris waves, then cross-referencing that data with Sentinel satellite imagery to pinpoint which upstream dump sites were washed away. This shifts the strategy from costly, reactive cleanup to a data-driven model of source identification and international accountability.
We are a team of Eastern Hungarian engineers and innovators united by the Tisza river. Seeing the impact of trash pollution on our local ecosystem firsthand, we’ve combined our expertise in ML and satellite data to protect our home waters. We don't just solve complex problems; we solve the ones that matter to our community.
We have been doing IoT projects since 2016, our latest achievement is a HU-RO-UA collaboration sensor network with more than hundred air quality (pm2.5/pm10) sensors installed and operated across three cities.
This project is a multi-scale monitoring system designed to tackle water pollution by combining localized computer vision with global satellite intelligence. By bridging the gap between immediate detection and long-range source tracking, the system provides a comprehensive solution for environmental protection and watershed management.
The hardware unit costs around 800 EUR. Operating costs depends on the communication and server platforms, estimated around 500 EUR / year for 100 units.
• Interreg: EU funding programme supporting cross-border and transnational cooperation projects, especially in regional development, innovation, and environmental sustainability.
• LIFE Programme (Calls for proposals 2026): EU funding instrument dedicated to environmental protection, climate action, and energy transition projects.
• European Union Agency for the Space Programme (Copernicus): Provides funding and opportunities for projects using Copernicus satellite data and services, especially in downstream applications and innovation.
• GoFundMe: Online crowdfunding platform where individuals or teams can raise funds directly from the public for specific projects or causes.
• YouTube channel advertising support: Financial support generated through ad revenue and sponsorships on a YouTube channel, typically requiring consistent content and audience growth
Edge Layer (Data Acquisition & Detection)
Hardware: An In-situ SBC (Single Board Computer) connected to a Camera. This is currently a RaspberryPi4 on our demo equipment, using Galileo satellites for location and anti-theft.
Intelligence: Performs Local Inference with huggingface resnet-50_plastic_in_river pyTorch model, meaning the machine learning model runs directly on the device to identify debris without needing to stream raw video to the cloud.
Connectivity: Uses NBIoT/Kiénis for telemetry. It's a Low Power Wide Area Network (LPWAN) designed for low-bandwidth, long-range environmental sensing.
Cloud Infrastructure (Backend & Storage)
Application Framework: Django serves as the primary backend engine, orchestrating data between the edge devices and the database.
Database: TimescaleDB is used for spatiotemporal data storage. Since it's built on PostgreSQL, it’s optimized for handling the time-series nature of sensor alerts and geographical coordinates.
Satellite Integration: A dedicated pipeline connects to the Copernicus API to pull Sentinel data, which then undergoes satellite image Inference to cross-reference edge detections with orbital views. Using Sentinel-1 VV and VH polarization and Sentinel-2 as complementary data.
Visualization Layer (Frontend)
Web Server: NodeJS acts as the delivery layer for the user interface.
User Interface: A Hexbin UI is used for map visualization. Hexagonal binning is particularly effective for spatial data because it reduces visual noise and makes density patterns (like debris clusters) much easier to spot compared to standard heatmaps.
Based on infrastructure data from Eurostat:
There are commercial satellites that provide higher spatial resolution and much more frequent imaging than the Sentinel satellites within the Copernicus Programme. Both Sentinel missions and systems such as ICEYE and Capella include SAR (Synthetic Aperture Radar) capability, enabling all-weather, day-and-night observations. The main difference is that commercial constellations typically offer finer spatial detail and higher revisit rates, which can improve the detection of illegal waste dumping sites by increasing location accuracy and lowering the minimum detectable size of deposits.
Q: How do you distinguish between trash and natural debris (like logs or algae) from space?
A: That is the power of Copernicus Sentinel-2s multispectral bands. We don't just look at a picture; we look at the spectral signature. Plastics and synthetic materials reflect light differently than organic matter like wood or algae. While our demo version is a baseline, our roadmap includes training a custom model on the Tisza signature - and for rivers generally - to exponentially increase our detection accuracy and reduce false positives.
Q: A 60 km sector is quite large. How can authorities actually use that?
A: Currently, authorities have to monitor the entire river length—thousands of kilometres — blindly. Narrowing it to a 60 km. A High-Probability Zone allows for a 95% reduction in search area. This makes it feasible to deploy targeted drone flights or local boat patrols. However, the current specifics are for a minimum viable product. We can integrate high-resolution commercial satellite data upon request to increase resolution and feedback frequency.
Q: If you aren't profit-driven, how do you sustain the operation long-term?
A: We operate on an avoided cost model. Governments currently lose billions in clean-up and infrastructure repair. We are positioning this as a public utility. Sustainability comes from Service Level Agreements (SLAs) with national environmental agencies and waterplants who pay a subscription fee that is significantly lower than the cost of the damage we prevent.