Industries
Energy
Scale your activities by building intelligent AI-based solutions to monitor critical infrastructure, identify the optimal expansion sites or better understand market dynamics
Challenges
With the impact of climate change, the energy industry is mutating more and more, giving way to new practices that will directly impact our energy mix. But with these new practices come challenges that needs to be tackled:
Large scale monitoring
Tracking assets at country or continent level is extremely computing intensive, and requires huge amounts of data. This means access to complex and scalable infrastructure and a dedicated team.
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.
Remote assets
Monitoring energy assets requires to sometimes go into remote parts of the world which are not easily accessed, and that usually means very high maintenance costs.
Scale your activities sustainably with SpaceSense
SpaceSense enables you to create advanced monitoring solutions based on satellite imagery and AI. Through our platform, we help you better assess your development options and monitor your assets at a lower cost.
Access analysis ready sources
Use our data sources to build the perfect dataset from numerous satellites
Augment your dataset
Increase your dataset’s size by using our data augmentation features
Add your data
Mesh your proprietary data to build unique datasets in a matter of minutes
Scale the analysis
Unlock continent-scale insights effortlessly with our scalability capabilities
CASE STUDY
Large scale pipeline monitoring
Problem
Monitoring pipelines is one of the most important tasks for energy providers, but also one of the most expensive, due to the length of these infrastructure. A european tech company contacted us in order to use our solution to build a satellite-based pipeline monitoring system. They needed to be able to detect any hazardous construction, vehicle or infrastructure that would be in a certain distance of the pipeline, for the length of a whole country.
Solution
They decided to use SpaceSense to build a training dataset using a combination of Sentinel 1 and Sentinel 2 that they then fused with their georeferenced labels of historical data. This was then going to be used as an input in an object detection machine learning algorithm to build a model able to detect any risk near the pipeline every time they had a new image.
Result
They managed to build a model in a few weeks that had an accuracy between 91% and 96%, depending on the type of object to detect. Without SpaceSense, they would have needed at least 5x more time to build the same solution, and they would then have needed to build a production-grade data input pipeline for their model, which was not necessary here.
Save up to 5 times in model
construction and several months
in production deployment
Additional energy 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).
