How satellites create enterprise opportunity for geospatial machine learning

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As the cost of launching satellites and drones has dropped in recent years, satellite and aerial images have become more affordable and accessible. Switzerland-based Picterra, whose no-code machine learning platform allows enterprises to gain insights from Earth observation imagery, is looking to take advantage of opportunities to help companies anticipate and mitigate risk globally.

The company today announced $ 6.5 million in Series A funding, part of the global aerial imagery landscape, which is projected to grow to about $ 5 billion by 2026. – Compared to something like Wikipedia, which has only 80 terabytes of data in all its languages, “said Pierrek Paulanas, CEO and co-founder of Picatra. “The only viable way to gain insight from such a large amount of data is to use machine learning and AI.”

The geospatial machine learning landscape is currently dominated by consultants who train manually algorithms, he added, adding that most of these projects fail. “Say you want to train an algorithm that will be able to count the trees on the surface of the planet,” he said. “First you need to create a huge training data set consisting of tree pictures to train the algorithm, then a machine learning model from scratch to input the training data.” But this method can be inefficient, it can take months to complete. And once the model is deployed on the enterprise’s IT infrastructure, it can also present deviations and biases, leading to insights that are not useful.

Other startups in the market use machine learning to train automated algorithms, but Paulenas claimed that Pictera was the only one to develop a workflow in a single product. According to the company, its no-code machine-learning SaaS platform allows users, both technical and non-technical, to “train, manage and deploy powerful geospatial algorithms that quickly transform images into a real-world positive effect.”

The ESG sector is an important case in point

Picterra is an important use in the ESG sector, as companies seek to substantiate their reporting claims as well as anticipate and mitigate the risks associated with climate change. Their global clients, including SGS, CYIENT, Westwood and The World Bank, actively oversee transportation, infrastructure and energy networks, among other things.

Earth observation imagery always deals with land observation, mapping and management. Another Picterra customer, Nespresso, monitors coffee plantations to ensure its 1,000 farms grow coffee sustainably and as part of its commitment to creating sustainable farming communities, farmers do not rely solely on coffee for a living.

“Gaining insights from Earth observation imagery only makes sense on a large scale,” Paulenas said. “Nespresso wants to be able to report consistently on the farming practices of those 1000 farms.”

As investors seek to take advantage of the ESG reporting trend, there is a growing gap between what geospatial machine-learning technology can do, the needs of the market, and the funding available. For example, “We’ve been working to track deforestation since the early days of Pictera, but since then tracking, say, illegal logging activities in West Africa has been seen more by tax angles,” he said. “Now the concept is different – consumers are actually tracking deforestation for its impact on biodiversity and so much more, when they also find monetary value” when it comes to ESG reporting pressures and potential new requirements – such as the SEC’s proposed certification Rules Climate-related advertisements.

Expect risk globally

Picterra is also looking for financial value to track supply chain issues by analyzing Earth observation imagery. “With disruptions on the supply chain, as we have seen during epidemics or due to climate change, enterprises can get a snapshot of what’s going on globally, such as knowing where their containers are around the world,” Paulenas said. “Global companies that source raw materials and convert them into consumer goods need to control everything in the supply chain.”

Overall, the biggest enterprise opportunity is “the ability to use Earth observation imagery, combined with machine learning, to effectively anticipate and mitigate risk globally,” he said.

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