Challenges with Current Earth Imagery Solutions

In the rapidly growing ecosystem of earth imagery capture and its applications, challenges to growth and access remain.

Cost of Earth Imagery Data: Currently, producing earth imagery is expensive. Both satellite and manned-aircraft imagery have high upfront capital costs. A single large satellite, like those used by Maxar, cost in the $100s of millions to develop and launch, with further ongoing annual maintenance costs associated with flight monitoring, instrument maintenance, command operations, and data processing and storage. Smaller satellites, like those used by Planet Labs, are less costly to manufacture, launch, and maintain; however, capital costs for satellites remain high. Companies producing imagery using manned aircraft also have high capital costs, with aircraft costs in the range of $1-5 million, and annual operating costs ranging from $500,000 to $2 million per aircraft, including fuel, maintenance and pilots.

These companies then price their products accordingly, with some customers paying a subscription cost to access imagery products (in the thousands or hundred thousands annually) and others paying higher prices for custom projects and analytics. For some smaller, low-margin industries or potential applications, existing imagery options are simply cost-prohibitive.

Infrequent Capture: Existing models of producing aerial imagery vary in frequency, with Planet Labs producing daily satellite images, and Nearmap providing updates up to four times per year, but none yet provide a method for producing enterprise-scale, frequent, high-resolution images that are capable of capturing changes in a quickly evolving landscape. While Planet Labs images are highly frequent, their low resolution limits its application. Only custom drone-captured imagery does this today, and there is not yet a scaled and standardized solution that would offer a subscription-level price point.

Inconsistent and Fragmented Data: While drones are widely used in custom imagery projects and applications, the resulting data is usually not shareable or scalable due to a lack of standardization in local regulations, image angles, drone altitude, and image resolution. Current drone-captured imagery is highly variable, siloed, and fragmented, which limits access and applicability beyond the original commissioned project. Furthermore, there has not been a way to orchestrate collection of standard data at scale. In essence, each drone and each project is operating on an island, without a coordination layer to bring the pieces together into a larger data ecosystem.

The potential for drone-powered earth imagery is immense. Almost all human industries are deeply embedded in the physical world and sensitive to its many changes. Systems that capture how these are changing and why can enable new categories of products, evolutions in business processes, and more efficient pricing. But the challenges in enabling a global geospatial layer of high-resolution imagery have so far stymied this transformation. Consumer drones are also a widely adopted technology, incredibly capable, exist everywhere, and are improving in capability at the rate of Moore’s Law, just like cell phones.