Industries
Environment
Improve Earth’s monitoring by building advanced AI-based solutions to track changes, monitor climatic events and build a more sustainable world
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Challenges
Monitoring the Earth at a large scale has always been a core goal from satellite remote sensing, but it doesn’t mean that they are several issues that make this task quite complicated today:
Large scale monitoring
Tracking changes at country or continent level is extremely computing intensive, and requires huge amounts of data. This means access to complex and scalable infrastructure, something most companies don’t have.
Multiple sources of data
Between drones, weather reports, IoT sensors, land maps and satellite imagery, it is hard to get one unified insight. The different scales, formats are complex to merge and thus limit the information potential.
Limited sensor type expertise
Due to the knowledge required to handle these different sources of information, most projects limit themselves to optical sensors, setting aside other satellite sources with a lot of value.
Build world-changing solutions at scale
SpaceSense enables you to build actionable scalable World monitoring solutions based on satellite imagery and AI. Through our platform, we help you to accelerate and expand your data sources, to have a maximum impact.
Access complex data
Use our data sources to access complex data like SAR in a packaged way
Augment your dataset
Increase your dataset’s size by using our data augmentation features
Harmonize data sources
Combine different satellite sources to get time-series over decades
Scale the analysis
Unlock continent-scale insights effortlessly with our scalability capabilities
CASE STUDY
Forest monitoring at country scale
Problem
Forests are one of the most important country’s assets. Thus it is extremely important to track them efficiently in order to see their evolution, identify illegal logging, or see the impact of specific policies on them. Doing that from the ground or by manually looking at satellite imagery is a painful task that requires significant manpower, something that the Western African government which approached us didn’t want to do.
Solution
They used our platform to create a solution that can automatically classify the different sorts of forests in the country, another that would highlight changes in forestry surface, and finally a third one used to detect illegal logging camps.
Result
As a result, they were able to focus their ground manpower much more efficiently and have a significantly increased impact in their agroforestry activities. In the first year, they also managed to increase their actions on illegal logging by 35%.
Increase your geospatial team's
productivity by up to 50%
Additional environment use cases
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Title use case 01
They used our data fusion module to create a dataset of hundreds of fields composed of Sentinel 1 & 2 data, crop type and significant event dates (like harvest and tilling).
Title use case 01
They used our data fusion module to create a dataset of hundreds of fields composed of Sentinel 1 & 2 data, crop type and significant event dates (like harvest and tilling).
