At Rise of AI 2019 in Berlin, a representative of the European Space Agency introduces ESA's free Copernicus Earth-observation data before two startups demo how they apply AI to it. The ESA speaker explains that the Sentinel satellite constellation delivers petabytes of optical, radar and atmospheric data at 10-meter resolution roughly twice a week, free and guaranteed for decades, and describes ESA's challenge programs and plans to run AI directly on satellites. Daniel Seidel, founder and CTO of LiveEO, then shows how his company combines satellite imagery with AI to monitor infrastructure like electricity grids and railway vegetation, using Deutsche Bahn as its founding case before expanding to US and Austrian grids. Anouk Visser, co-founder and CTO of Birds.ai, closes by demonstrating how her company uses drone RGB and thermal imagery with supervised machine learning to detect defects on solar farms and wind turbines, delivered through a subscription platform called Bird's Eye View. A Q&A covers workflow ownership and each startup's path from an earlier, different idea to its current business.
Key takeaways
- ESA's Copernicus Sentinel satellites provide free optical and radar Earth-observation data at 10-meter resolution roughly twice a week.
- LiveEO found that vegetation exposure along Deutsche Bahn's rail network ranged from about 20% to 70% depending on the region.
- LiveEO scaled the same satellite-based monitoring pipeline from a single German customer to infrastructure grids in the US and Austria.
- Birds.ai combines drone RGB and thermal imagery with supervised machine learning to detect solar panel defects like broken substrings.
- Birds.ai charges a monthly software-as-a-service fee for platform access plus a fixed fee per new drone-based inspection.
Detailed notes
ESA's free Earth-observation data
The ESA speaker introduces Earth Observation as the agency's use of satellites to monitor the planet, comparing the Copernicus program's Sentinel satellites to a doctor tracking vital signs, covering vegetation health, the ozone hole, clouds and atmospheric chemistry. The data, at 10-meter resolution and refreshed about twice a week in both optical and radar domains, is free, publicly funded and guaranteed for continuity over the next 50 years, alongside a newer commercial layer of small satellites offering daily, higher-resolution but paid imagery. He notes the scale, tens of petabytes, makes machine learning necessary, though a scientist is still needed to interpret results correctly, illustrated by AI-generated radar-to-optical imagery that looks convincing but can misrepresent reality. He describes a project with the World Food Programme and UNICEF that used a drone campaign to map crops in Malawi, then used transfer learning to scale the model from high-resolution drone data down to Sentinel's 10-meter resolution so results could extend from a field to an entire region or continent. ESA also runs challenge programs like FDL to pair Earth-observation and AI specialists, and is moving toward running AI directly on satellites, including a chip-equipped satellite launching in September and a planned pair of satellites that will share computing power over a laser link to decide what data is worth downloading.
LiveEO: monitoring infrastructure grids from space
Daniel Seidel describes LiveEO's mission to monitor what he calls the biggest machine on earth, the US electricity grid, which alone spans around 9 million kilometers, as part of a wider category of infrastructure grids, electricity, rail and pipelines, that together span more than 30 million kilometers globally, about 80 times the distance to the moon. He says this infrastructure is typically inspected manually, often by aerial survey or people walking the grid with paper and pencil. LiveEO combines free Copernicus data with higher-resolution private satellite imagery to classify vegetation risk along infrastructure automatically. Its founding case was Deutsche Bahn, which had suffered more than 800 train collisions with fallen trees over three years and did not know how much of its roughly 33,000 kilometers of track was exposed to vegetation; its original plan to send around a thousand inspectors out over five years was abandoned once LiveEO's satellite-based classification, later scaled to cover all of Germany, showed that vegetation exposure ranged from about 20% to 70% of track length depending on the region, letting the company reallocate budget instead of spreading it evenly. LiveEO has since applied the same pipeline to monitor electricity grids for customers in the US and Austria.
Birds.ai: drone and AI inspection of solar farms
Anouk Visser explains that Birds.ai, working first with the Austrian utility she refers to as Wien Energie, built a platform giving operations and maintenance managers a bird's-eye view of solar farms. Drone operators capture RGB and thermal images at about five-centimeter resolution, since thermal cameras reveal defects, such as hot spots from a broken substring or a faulty junction box, invisible in visible light. Supervised machine learning models, trained with input from inspection experts, detect issues like bird droppings, broken glass, overgrown vegetation, missing panels and thermal hot spots, and bounding boxes locate individual panels to build a blueprint of the farm. The findings are enriched with severity ratings and recommended actions and shown to customers on the Bird's Eye View platform or in PDF reports. The business model combines a monthly software-as-a-service fee for platform access with a fixed fee per new drone-based inspection. Visser says the company inspected 9.7 megawatts of solar capacity, about 40,000 panels, in its first year, and is expanding the same defect-detection approach to wind turbines and exploring uses like coatings on sea vessels.
Q&A highlights
Asked about workflow ownership, Visser says Birds.ai always sets the flight protocol even when customers run their own drones, and that usage of its app by maintenance teams in the field is more frequent than the underlying satellite or drone analysis cycle. Asked how they moved from idea to startup, Visser recounts starting with a challenge to protect game reserves from poachers, then testing several industries such as agriculture before settling on infrastructure inspection. Seidel recalls starting with hardware ideas for small satellite components, realizing LiveEO was about five years too late to build hardware, briefly considering a tourism app to measure beach crowding from space, and pivoting after market analysis toward the infrastructure and utility sector.
Quotes
We are around five years too late if we want to build hardware, so that's why we wanted to build a software solution.
Daniel Seidel
Artificial inspectors are a lot more cheaper than human inspectors.
Anouk Visser
Questions answered in this talk
What satellite data does ESA provide for free for AI applications?
According to the ESA speaker at Rise of AI 2019, the Copernicus program's Sentinel satellites provide free optical and radar Earth-observation data at 10-meter resolution, updated about twice a week, with continuity planned for the next 50 years.
How does LiveEO help Deutsche Bahn manage vegetation risk along railways?
According to Daniel Seidel of LiveEO at Rise of AI 2019, LiveEO combined satellite imagery with AI to classify vegetation along Germany's roughly 33,000 kilometers of track, after Deutsche Bahn had no data on how much track was exposed and had planned to send inspection teams out over five years.
How does Birds.ai detect defects in solar farms?
According to Anouk Visser of Birds.ai at Rise of AI 2019, Birds.ai uses drones to capture RGB and thermal images of solar farms, then applies supervised machine learning trained with inspection experts to detect defects such as bird droppings, broken substrings and hot junction boxes, shown on a platform called Bird's Eye View.