Winward is a wind turbine inspection service provider in China, and they are using DJI drones and Sterblue software to achieve new levels of efficiency. Our ambition: to jointly inspect up to 3,000 wind turbines in China within one year!Read More
Once again, Sterblue spread its wings and flew sky high — this time for Energijos Skirstymo Operatorius AB (ESO) and Lietuvos Energija innovation hub (LE) in Lithuania. While carrying out electric grid inspections, our drone operators could gain exciting insights into the forests and birds of the Baltic State.
We are happy to announce a new partnership with United States drone-network operator, DroneBase, whose headquarters are located in Los Angeles, CA. Sterblue, based in France and in Los Angeles, CA builds software for drones to inspect power lines and wind turbines automatically. The announcement of this newly formed partnership with DroneBase comes just two months after the announcement that Sterblue would be entering the North American market.
French start-up, Sterblue, is officially open for business in the United States and has big plans to re-invent the world of energy infrastructure inspections in the U.S., starting in California. Sterblue has developed a software for drones to inspect power grids and wind turbines automatically.
Sterblue officially reached its third continent! After Europe and Africa, Sterblue inspected its first asian electrical grids in Hong-Kong. I am just returning from a 5 days trip in Hong-Kong to meet CLP (China Light and Power), the local utility company.
According to French futurologist Laurent Alexandre “Today, Europe does not control any of the components of AI, whose industrialisation is based on the marriage between the power of computers, the mountains of big data and the neural networks for deep learning”. It is true. Remember that without large amounts of data, the machine learning algorithms, even the most efficient, will be of no use.
These previous years, artificial intelligence algorithms (neural networks) have been brought to the forefront thanks to a hardware computation power increase and the labelling of huge data bases that made possible the pre-training of these algorithms. Thanks to a suitable training on thousands of labelled data, these algorithms can recognize some complex patterns such as animals, cars, bridges, etc.