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Yandex B2B Tech, Yandex School of Data Analysis, and the Far Eastern Federal University (FEFU), have developed and open-sourced a neural network designed to streamline coastal waste cleanup in hard-to-reach regions. Deployed successfully in the remote areas of South Kamchatka Federal Nature Reserve, the technology is now being tested in the Arctic and beyond.
Aligned with World Environment Day 2025’s focus on ending plastic pollution, this open-sourced solution can help environmental agencies and volunteers accelerate solid waste removal, including plastics, in ecologically sensitive zones worldwide.
The problem and evolution of solutions
More than 11 million tons of plastic — approximately 85% of all marine waste — end up in the world’s oceans annually, mainly in the form of food packaging and fishing gear. If no action is taken, the amount of plastic waste could increase to 29 million tons by the year 2040.
Marine plastic pollutants, once believed to take centuries to decompose, degrade far faster into microplastics when exposed to sunlight and seawater, threatening the life and health of wildlife that ingests it. Enhancing this threat are “ghost nets” — discarded fishing gear accounting for 60% of ocean debris — that lethally trap various marine species. While much of the waste remains in the oceans, a significant part is washed ashore, often in remote coastal areas.
Meanwhile, volunteers often focus on accessible coastal areas, remote sites remain polluted — largely because teams cannot determine the critical resources (team size, tools, specialized equipment) required for effective cleanup operations beforehand. Moreover, traditional mapping of polluted areas relies on labor-intensive manual geotagging, limiting scalability.
With machine learning automating waste detection and analysis, the neural network developed by Yandex and FEFU researchers now streamlines pollution assessment, offering a faster, cost-effective alternative to outdated methods — a critical step in combating the marine crisis globally.
Proven impact and opportunities for global adoption
During expeditions in Kamchatka’s nature reserves, the neural network revealed that 33–39% of coastal waste was plastic containers and packaging, while 27–29% derived from industrial fishing. By deploying the tool, volunteer teams cleared 5 tons of waste four times faster than traditional methods, mobilizing an optimal number of volunteers and determining the pieces of equipment needed for the cleanup.
Further project development in 2025 includes deployments across Far Eastern and Arctic national parks, where challenging terrain complicates waste management.
Addressing the pressing issue of pollution, this solution can be further developed and implemented by local volunteer teams and government agencies in Indonesia and other countries with coastal areas, riverbanks, and similar environments, enabling more effective solid waste monitoring and cleanup. Additionally, having an open codebase, it can be customized to detect new types of waste, monitor endangered species, and support other environmental efforts.
How the AI solution works
The AI solution development leveraged computer vision, specifically semantic image segmentation, to automate solid waste detection. This method divides images into pixel groups, assigning each to a specific waste type: fishing nets, iron, rubber, large pieces of plastic, concrete, and wood, achieving over 80% accuracy.
The neural network then maps waste locations, estimates volume and weight, and calculates the required workforce and equipment (for instance, dump trucks, all-terrain vehicles). This data-driven approach optimizes logistics, reducing cleanup time and costs.
The neural network can be integrated with various mapping tools, such as the open-source QGIS.
The neural network codebase is fully open-sourced and available on GitHub. Environmental agencies and volunteer organizations around the world can use the model for free and modify it for their own pollution management tasks.
About Yandex
Yandex is a global technology company that builds intelligent products and services powered by machine learning. The company aims to help consumers and businesses better navigate the online and offline world. Since 1997, Yandex has been delivering world-class, locally relevant search and information services and has also developed market-leading on-demand transportation services, navigation products, and other mobile applications for millions of consumers across the globe.
Supporting visuals: https://bit.ly/cleanupimages
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