Industries
Infrastructure
Save millions by building intelligent AI-based solutions to detect risks around your infrastructures, follow repair and construction work or assess the state of your infrastructure
Challenges
Infrastructure monitoring has always been a time and resource consuming activity that represents a significant cost percentage for most companies with remote or spread infrastructures. This is due to several reasons, the main ones being:
High insight quality
Having robust information about the state of an infrastructure is critical since any error can result in catastrophic consequences.
Remote assets
Monitoring assets requires to sometimes go into remote parts of the world which are not easily accessed, and that usually means very high maintenance costs.
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.
Scale your monitoring 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
Understand your model
Use our feature engineering tool to exactly grasp what goes into your model
Fuse data sources
Built complex multi-sources datasets thanks to our data fusion module.
Scale the analysis
Unlock continent-scale insights effortlessly with our scalability capabilities
CASE STUDY
Monitoring at lower a cost using AI
Problem
A European company reached out to us to help them better monitor and track gravel movement between quarries and construction sites. This would help them better assess demand, supply, and overall activity in any given area.
Solution
They started using SpaceSense to build a change detection solution based on Sentinel-1, and be able to detect significant height changes between two dates. After identifying these areas, they trained a machine learning algorithm that would look over these areas and try to detect what they were (quarries, construction sites…) only from the imagery.
Result
The creation of this solution enabled them to divide their monitoring and scouting costs by 25. Previously they had to hire people to drive around and do the monitoring manually, or rely on word-to-mouth. This method was much more reliable, and enabled them to expand this solution to new countries before they launched their solution commercially.
Divide your costs up to 25 times
while monitoring your
infrastructure
Additional infrastructure use cases
.jpg)
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).
