Custom algorithm | ePhi Case Study

Client

ePhi is an environmental services company based in France, providing industrial cleaning services.

The Challenge

ePhi needed to process satellite imagery covering large industrialized areas across multiple cities.The goal was to analyze every square meter of land and identify buildings that could be classified as commercial development targets.

Due to the massive data volumes involved,manual processing was not feasible.Initial plans to build custom AI solutions in-house proved too costly and time-consuming.

The Solution

After gathering project requirements,Flobotics selected tools that balanced cost,quality,and development speed.The map scraping process was based on Google Maps,which provided an optimal ratio of satellite image quality to cost.

Scraping entire metropolitan areas typically cost less than $90,depending on size,and the API ensured consistent image resolution and dimensions.Low development complexity further reduced overall project costs.

Because Google Maps Static API is not designed for machine learning use,we developed a custom algorithm to refine and enrich the image data in real time.

The automation was deployed on Amazon Web Services,due to API rate limits requiring long-running processes.AWS enabled fire-and-forget execution,and images were stored in AWS S3 for seamless integration with machine learning services such as SageMaker.

The Outcome

Flobotics delivered an algorithm capable of capturing,organizing,and sharing satellite images with consistent scale,definition,and predictable costs.The images were automatically transformed into structured,readable data for future analysis.

The entire development process took less than two months,meeting the client’s time constraints.

ePhi,thank you for your trust.