Moscow Region Sets a New Standard for Digital Environmental Monitoring
The Moscow Region has deployed an artificial intelligence-powered system to monitor protected natural areas and automatically flag potential violations of environmental regulations. Its developers report that the system can classify changes on a digital map with an impressive accuracy of up to 92%.
The system does not rely solely on satellite imagery. It combines information from several sources, including radar data, satellite images and other observation parameters.
A Digital Map of the Forest
This integrated approach helps detect threats that might otherwise go unnoticed. For example, if a satellite indicates that green cover remains intact but radar data show a decline in vegetation density, the algorithm flags a potential illegal logging operation. The technology covers not only forests but also fields and built-up areas. The system maintains a digital map of the territory and updates it continuously. When a new “patch” appears on the map – such as a section of forest disappearing or a construction site or road emerging – the system detects the change and directs an inspector to that specific location.
The economic and management benefits of deploying AI have already been assessed. The system is expected to make nature protection significantly more effective. Illegal logging, unauthorized construction and ecosystem damage should be detected more quickly. According to the Moscow Region’s Ministry of Ecology, the accuracy of violation detection is expected to increase by about 30%, while the time inspectors spend on field inspections is expected to fall by a quarter. The system has already passed operational testing and is ready to be scaled up.
Space Technology
In 2022, the Federal Forestry Agency (Rosleskhoz) and the State Space Corporation Roscosmos launched the Tsifrovaya Zemlya (“Digital Earth”) project for continuous satellite monitoring of forests. Artificial intelligence was soon added to the analysis of Earth observation data. An experiment conducted in the Republic of Buryatia, Zabaykalsky Krai and Amur Region showed that a neural network detected logging 62% faster and 12% more accurately than a human. Meanwhile, researchers in Buryatia developed a specialized AI-powered program that analyzes multispectral satellite images to identify changes in forest areas and quickly flag illegal logging, wildfires and broader forest degradation, including tree diseases. The Tsifrovoy Les (“Digital Forest”) system is being developed in Nizhny Novgorod Region. Its AI has helped identify more than 150 cases of changes in forest cover, ranging from violations of forestry laws to damage caused by natural events.
AI is also being used to analyze data from camera traps in protected areas. For example, a pilot project by Russia’s Ministry of Natural Resources and Environment and ER-Telecom Holding is underway in Losiny Ostrov National Park to automate wildlife surveys in specially protected natural areas. Algorithms analyze camera-trap images to identify 10 animal species, count individual animals and track their movements.
Russia’s Ministry of Natural Resources and Environment and the Moscow Institute of Physics and Technology have developed AI software based on neural networks to monitor populations of rare animals. The technology was tested in seven protected areas, including Land of the Leopard National Park in Primorsky Krai. The software can recognize Amur leopards, Amur tigers and other animals. It can process up to one million images a day. Since 2025, seven Russian protected areas have joined forces to establish the country’s first unified wildlife camera-monitoring network. More than 400 cameras are being used to track mammal populations.
Bolshoy Khekhtsir Nature Reserve in Khabarovsk Krai has also become a “smart reserve.” There, AI is intended to help combat poaching and monitor wildlife across the protected area.
Deeper Into the Forest, Better Protection
Solutions like the one deployed in the Moscow Region could serve as the foundation for Russian environmental-monitoring platforms in Siberia, the Russian Far East and the Arctic, where ground-based methods make it difficult to monitor vast areas. The future of environmental technology lies in integrated systems that combine satellite data, geographic information systems (GIS), drones and machine learning.
Beyond expansion into other regions, the system is slated to be integrated with government environmental platforms. Its capabilities are also expected to grow, from detecting wildfires to monitoring coastlines and land pollution. The accumulated data could support the creation of digital twins of natural areas, making it possible to forecast environmental risks.
