SPATIAL ANALYTICS – THE HEART OF OUR SERVICES
Data is only valuable when properly analyzed. This is why Bitcomp offers versatile spatial analysis services for enhancing business in forestry, agriculture and environmental sectors. We utilize newest machine learning algorithms and cloud solutions, which enable quick combining and refining of large spatial data sets into custom-tailored analysis products
With our analysis products, you receive the information that matters to you – for example the value of the forest and how it will evolve in the future – always based on up-to-date data from multiple sources, processed with the help of artificial intelligence.
Satellite-based Spatial Data
Recently we have been focusing on utilizing the Sentinel 1 radar (SAR) data and drone data to meet the needs of forestry and agricultural sectors – namely for forest inventory and change detection as well as crop monitoring. We also use optic satellite data (Sentinel 2, Landsat) and other remote sensing data (LiDAR, aerial images) in combination with data from other spatial big data sources. These sources include for example spatial notes created by mobile users, open map data and weather data.
The analysis products are available as map layers (WMS/WFS). Alternatively, they can be accessed via real-time analysis service. Also, with the analysis service the summary statistics for a user-given area can be calculated dynamically. Service can be included both in Bitcomp’s own or 3rd party web or mobile applications via data interfaces.
other remote sensing data (LiDAR, drone images, aerial images)
SAR (Sentinel 1 radar) and commercial satellites
Optic satellite data (Sentinel 2, Landsat)
Open map data
The analysis products are available as map layers (WMS/WFS) or accessed via real-time analysis service.
Spatial notes and other field data
Customer’s own data
Other big data sources
What kind of spatial data analysis would help your business?
Cut down the costs by focusing costly field work to the correct areas. Satellite data, processed utilizing artificial intelligence, enables faster-than-before automatized monitoring of the state of the environment. This allows quick estimation of, for example, damages caused by forest fires, storms or insects.
The analysis of remote sensing data can also be utilized for mapping out optimal actions for carbon storage by combining the analyzed data with carbon balance models.
In production since June, the next steps for the forest change detection service are adding features to the current service and the integration to other services, for example as a map layer to WoodsApp. The service also has great potential to be expanded to other countries.