As the cost of satellite or aerial imagery has decreased in recent years, satellite and aerial imagery has become significantly more affordable and accessible. Picterra, a Swiss company that has developed a machine learning platform that allows businesses to extract insight from earth observation imagery, is looking to take advantage of the opportunity to alleviate risks on a worldwide scale.
The company has raised $6.5 million in Series A funding, which is being shaped by a growing global aerial imagery landscape. According to Pierrick Poulenas, the total of all satellites collect between 200 and 300 terabytes of data every day. "The only viable method to extract insight from this enormous amount of data is to utilize machine learning and AI."
Despite the fact that most of these projects fail, you need to create a huge training data set of tree pictures to measure trees at the surface of the planet. This process, however, can be ineffective, requiring months to complete. This method, once implemented on an enterprise's IT infrastructure, may also lead to errors and bias.
In this market, other businesses utilize machine learning to automatically train algorithms, but Poulenas claims that Picterra is the only one to create the workflow into a single product. According to the company, its no code machine-learning SaaS platform allows both technical and non-technical users to "train, manage, and deploy powerful geospatial algorithms that rapidly transform images into a real-world positive impact."
The ESG sector is a valuable practice.
One of Picterra's most important use cases is in the ESG sector, where companies strive to demonstrate their reporting claims as well as anticipate and mitigate the effects of climate change. Their global customers, which include, and proactively monitor, among other things, transport, infrastructure, and energy networks.
Earth observation imagery has always dealt with land observation, mapping, and management. Another Picterra customer, Nespresso, monitors coffee plantations to ensure their 1,000 farms cultivate coffee in a sustainable manner, as part of its commitment to fostering sustainable farming communities.
Gaining knowledge from earth observation imagery only makes sense on a large scale, Poulenas said. Nespresso wants to be able to document the farming habits of those 1000 farms in a concise manner.
As investors look to the benefits of the, there is a growing connection between what the geospatial machine-learning technology can do, the needs of the market, and the funding that is getting into it. For example, tracking deforestation has been something we have been working on since the early days of Picterra, but back then, tracking, say, illegal logging activities in West Africa was more seen through the angle of taxation, he said. Now the perception is different customers are really tracking de
Anticipating risk at an all-time high
Picterra is also exploring financial significance in analyzing earth observation imagery. "With the constraints on the supply chain, as we have seen during the pandemic or due to climate change, enterprises can get a visual representation of what is going on at global scale, such as knowing where their containers are around the globe," Poulenas said. "Global companies that source raw materials and convert them into consumer goods must be in charge of everything in the supply chain."
Globally, the largest enterprise possibility is the capability to utilize earth observation imagery, combined with machine learning, in an effective manner to anticipate risk and mitigate risk at a global scale.