The 5 key elements of a scalable geospatial platform
Scalable is a fairly overused word in tech. It’s everywhere. If you go to nearly any software-as-a-service company’s site, you’ll see it. If you’re on LinkedIn, you’ll see it. X? You’ll see it.
We may be at the point where readers are starting to tune it out. Are there any SaaS products out there that aren’t scalable? If it’s not, perhaps it’s not worth buying, especially for bigger organizations. Scalability should be a given by now.
So why are we still going on about it?
Because, while SaaS is a relatively recent concept in the era of human business, Earth observation data platforms are even more recent. We thought it would be worth diving into the key elements of a scalable geospatial platform.
First we’ll talk about scalability in the general sense, and then dive into how platforms can solve its biggest challenges.
The definition of "scalability"
"Scalability" is business language. It's not a term that comes up in everyday conversation. Here's the definition, taken from Investopedia, which is probably one of the best sources for business language.
"Scalability refers to the ability of an organization or a system such as a computer network to perform well under an increased or expanding workload. A system that scales well will be able to maintain or increase its level of performance even as it's tested by growing operational demands."
When we apply this definition to a SaaS product, it should cover two different possibilities:
- When a single customer uses the product, they're able to ramp up their usage of the product without a problem. This could mean more database storage, more operations per day, or any other number of possibilities.
- As the product gains more customers, existing customers don't experience a decline in performance.
In one sentence: customers should be able to scale up their operations with the product without running into obstacles, and the product should be able to scale up its own operations to match an increasing customer base.
Scalability as it relates to Earth observation
When we think of a successfully scaled business, there are some obvious ones. Amazon, for example, started off selling books and now sells almost everything. Most big tech companies can be seen as successful at scaling, from Microsoft and Apple to Netflix.
But what does successfully scaling in EO look like? The industry is currently filled with big and small players. The bigger players usually focus on their own products. Smaller companies and startups are trying to fill the need gaps around buying different types of data from different suppliers as easily as possible. Like us.
And to do so, you need a platform. As we see it, here are the top 5 key elements to making a geospatial platform scalable, their challenges, and our solutions.
1. Efficient data ordering and management
It should be as easy as possible for individuals and small businesses to check out available EO data, compare options, and order.
But if users are doing this in a browser, it's not exactly scalable, especially when they need to place hundreds or thousands of orders. They also need a way to sort through and, preferably, classify their orders so that they can keep track of them.
How we're solving this
While our console is available to users who want to visualize their scenes on a map, users can then scale up their ordering via our API or Python SDK so that they don't have to enter every order manually. Instant price estimates show up as soon as AOIs are finalized, which helps with budgeting and planning. Users can save their AOIs and reuse AOIs across shared accounts. They can also use the same set of ordering parameters to quickly integrate new products into their workflows.
The NSG UP42 platform lets users add custom attributes to orders, like titles and tags, so that you can easily sort and filter your orders, for individual or enterprise accounts.
2. Data standardization
One major challenge in our industry is the complexity of the data. Optical sensors collect very different types of data from SAR sensors. And even a single image may span tens or hundreds of kilometers and contain multiple layers of information in the form of different electromagnetic wavelengths. In addition to this, different providers often use different file types and formats.
All of this adds up to a lot of work for end users, who need to be familiar with many different types of data and know how to work with them.
How we're solving this
NSG UP42 uses STAC (SpatioTemporal Asset Catalog), a specification that was designed to establish a standard in geospatial data. It allows searching across multiple providers for geospatial assets which share a common structure and set of metadata.
All data deliveries in the NSG UP42 platform are transformed: raster data into cloud-native GeoTIFFs, and vector data into GeoJSONs. All transformed assets use the STAC specification, so you can easily search for, access, and stream them, without needing to worry about how to work with different data formats.
3. Processing
As mentioned above, EO data is incredibly complex. Once the data is collected, it needs to be processed before it can be useful. What we refer to as "noise" often interferes with image collection, and can be caused by anything from temperature fluctuation and atmospheric effects to changing light levels.
While some users will know how to process data to get the best results from it, most won't. That's where we come in.
How we're solving this
NSG UP42 offers pre-processing for the vast majority of our data products. For example, many optical imagery collections offer the following levels of pre-processing:
- Primary processing makes an optical image suitable for generating digital elevation models, but still stays close to the rawest form of the data.
- Georectification adjusts imagery to align with an existing coordinate reference system, which allows for more meaningful geospatial analysis.
- Orthorectification is more advanced, and involves imagery first being georectified, and then orthorectified: distortions such as terrain variations, sensor angle, and many other factors are removed. This allows for accurate measurements and comparisons in geographic analysis.
We're also broadening our processing abilities even further with the addition of our pansharpening capabilities, which allow users to fuse color images (higher spectral resolution, lower spatial resolution) with panchromatic images (grayscale images with low spectral resolution but higher spatial resolution) for full-color, higher spatial resolution images. Expect even more processing updates soon.
EO as an industry: Nowhere near our full potential
If nothing else, we hope this article has at least helped you look at the word "scalability" in a new light. There's a reason that it's such a popular word.
But we hope you're taking away more than just that. EO is a young industry, and it's no wonder that we're facing scaling challenges: we have unique problems, and vast ambitions of capturing and analyzing titanic amounts of data on a planet-wide scale. Honestly, it's amazing that we've come so far in such a relatively short amount of time. Though there's still a long way to go.
Whether you need a few scenes, or want to scale up your EO operations, NSG UP42 is here. Reach out and tell us about your use case. We'd be happy to help.
