Intelligent
Forest Analytics

Harness the Power of Satellites for Intelligent Forest Management

Forest analytics by Bitcomp are based on satellite monitoring. The information you receive on the current state, risks and changes of forests is accurate and up to date. Our forest analytics utilize self-learning artificial intelligence. This enables the continuous improvement of the results based on user feedback and field observations.

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Our Forest Analyses

From Satellite Data to Forest Resource Information

How our analyses work

Data is only reliable when it is up to date. This is especially true when it comes to forest and nature values. Key to being current lays in satellite data. Our machine learning based forest analytics uses optical satellite imagery (from Sentinel 2) in addition to other data sources to calculate the variables describing forest state at the moment. Other data sources include synthetic-aperture radar (SAR), other remote sensing data sources (LiDAR, aerial photography, commercial satellite data), geospatial big data sources, field observations, open maps (National Land Survey of Finland, Inspire), weather data, and customer own data sources.

UP-TO-DATE INFORMATION

You can trust that all the information on forests is up to date. Issues can be addressed quickly and for example forest carbon storages can be reported reliably.

RESOURCE SAVINGS

Field visits and work can be focused on the areas where they are most needed. This way costs can be cut and possible additional damages prevented.

GROWTH IN FOREST VALUE

Ensure that your forest stays in good condition and continues to grow in value with forest management suggestions by artificial intelligence. AI recognizes management needs and their optimal timing. Automation also enables fluent communication between a forest professional and a forest owner.

Make Next Level Decisions

Intelligent forest analytics is more than just fancy technology. Its employment does not require special know-how as the data is introduced to the forest system automatically.

Site Recognition with Satellites

AI based forest analyses utilize high-quality and up-to-date satellite data. The machine learning algorithms are run weekly. They can determine for example the current state and development possibilities by sites and find damaged areas quickly and accurately.

Result Visualization on Map

The results are served as illustrative theme maps (WMS/WFS) that are updated weekly. In this result format, planning, monitoring and other development work becomes much more efficient. Both the change types and the magnitude of change can be seen from the maps.

Announcement Reception

When analytics recognize sites that require special attention or management, the system automatically sends out an announcement. This allows professionals to focus work and inspections on sites where they are needed the most. Forest owners can quickly contact a forest professional to request management. This is especially helpful, if their forest is located far away from where they are living.

Observations and Adding Information

User can add their own notes and observations on sites using the mobile application, thus enriching forest analytics. Analysis becomes more intelligent the more data it gets to process.