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Waste Detection and Change Analysis based on Multispectral Satellite Imagery
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This study used medium-to-high-resolution multispectral satellite imagery and machine learning to detect illegal waste dump hotspots and river surface plastic blockages along the Tisza River. Results confirmed that satellite imagery combined with machine learning can locate and monitor waste accumulation and change over time, offering a scalable tool for environmental monitoring organizations to identify and respond to plastic pollution sources.
One of the biggest environmental problems of our time is the increase in illegal landfills in forests, rivers, on river banks and other secluded places. In addition, waste in rivers causes damage not only locally, but also downstream, both in the water and washed ashore. Large islands of waste can also form at hydroelectric power stations and dams, and if they continue to flow, they can cause further damage to the natural environment along the river. Recent studies have also proved that rivers are the main source of plastic pollution in marine environments. Monitoring potential sources of danger is therefore highly important for effective waste collection for related organizations. In our research we analyze two possible forms of waste detection: identification of hot-spots (i.e. illegal waste dumps) and identification of water-surface river blockages. We used medium to high-resolution multispectral satellite imagery as our data source, especially focusing on the Tisza river as our study area. We found that using satellite imagery and machine learning are viable to locate and to monitor the change of the previously detected waste.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.