The Geospatial Data Stack

What is the Geospatial Data Tech Stack?

Geospatial data is any data that is connected to a place on earth. We often think about satellite imagery, but geospatial data is broader than that. It can be a GPS coordinate, weather related data, a drone image etc.

The landscape I created is about the ecosystem of companies building around this data. From collecting to processing geospatial data in order to turn it into useful decisions.

What are the major types of geospatial data?

Concretely, there are four main forms of geospatial data:

• Maps and location data. Basic geographic information about the physical world, from roads and buildings to land ownership, infrastructure, or “administrative” boundaries.

• Satellite and aerial imagery. Images of the Earth collected from above. Whether by satellites, aircraft, drones, or other aerial systems. Depending on the use case, these images can capture visible light, heat, radar signals, or other forms of sensing.

• Signals and sensor data. Location based data that does not necessarily look like an image. It can come from ships, vehicles, weather systems, connected devices, radio signals, or atmospheric measurements.

• Derived geospatial intelligence. Raw geospatial data is transformed into something easier to act on, such as a risk score, an alert, a prediction, or a recommendation.

What does the geospatial data tech stack look like?

I split my map into a three layer stack:

• Layer #1: Proprietary geospatial data sources. These are the companies creating or unlocking proprietary geospatial data. This includes satellite imagery, SAR, hyperspectral imagery, RF signals, weather data, maritime data, drone data, street level data, mobility data, and sensor networks. The important thing to keep in mind is that often these companies also provide APIs, analytics, or dashboards in addition to raw “geospatial data”. But their main asset is the data source itself.

• Layer #2: Geospatial data infrastructure. These are the tools that make geospatial data easier to access and use. They usually do not collect proprietary raw data themselves. Instead, they help customers turn fragmented geospatial data from many sources into something usable. This means making the data easier to find (marketplaces of data providers), access, clean, process, combine, and integrate into the customer’s workflows.

• Layer #3: Geospatial intelligence & applications. These are the companies turning geospatial data into decisions. This is basically the layer where geospatial becomes a verticalization play. The same geospatial data can create very different products depending on the industry: From crop monitoring for agriculture to risk analysis for insurance, movement visibility for logistics, or intelligence for defense. Here, the customer is not really buying geospatial data. They are buying an answer to a business question.

Is this an emerging or mature market?

The geospatial data stack is not new. Governments have long used maps to manage land and public infrastructure. Militaries use satellite imagery to monitor other countries. Utilities use GIS to monitor power lines, pipes. Insurers use location data to assess exposure to floods, fires, or storms. etc.

This market has already gone through several major evolutions:

• From paper maps to digital GIS, roughly 1960s to 1980s. The first big shift was the digitization of geographic information. Instead of working with static maps and manual surveys, organizations could store, layer, query, and analyze geographic data inside specialized GIS software. This made geospatial data a professional software category, mostly used by experts.

• From government satellites to commercial Earth observation, roughly 1970s to 2000s. Satellite imagery was initially driven by governments, defense, and scientific programs. Over time, commercial Earth observation companies made this data more accessible to private companies. The result was a larger market for imagery. Not only for states and researchers, but also for agriculture, insurance, energy, logistics, and finance.

• The consumerization of location data, roughly 1990s to 2010s. With GPS, smartphones, navigation apps, and consumer mapping products, location data became part of daily life. Maps were no longer only specialist tools. They became consumer interfaces.

• The democratization of geospatial data,  roughly 2010s to today. Historically, geospatial workflows often meant large local files and formats that were difficult to work with and access. Over the past decade, the stack has moved into the cloud and become much easier for software teams to use. This makes geospatial data easier to integrate into modern software products.

Why is this ecosystem changing now?

Now, mostly thanks to AI, the geospatial data stack is going through a new evolution.

• There is more data, but also too much data to interpret manually. The world is being measured more often and with better precision. There are more satellites, more drones, more sensors, more public datasets, more commercial datasets, and more location based signals than before. But this creates a new problem: collecting more imagery or location data is not enough if users cannot extract the right signal from it.

• AI is turning geospatial data into something more readable. Historically, a lot of geospatial analysis required specialists or narrow models built for one specific task. AI makes it easier to detect what changed, identify what matters, and transform raw observations into usable signals. Basically expanding the scope of what we can do with geospatial data.

• Intelligence is moving closer to the point of collection. The interesting shift is not only that AI can analyze satellite images after they are downloaded. Over time, satellites, drones, and sensor networks can start filtering what they see directly. Instead of sending everything back, they can prioritize what matters first (and this is pretty amazing btw).

• Geospatial workflows are becoming easier to put in place. Users don’t need GIS experts to work with geospatial data. They can increasingly ask questions, define constraints, and get answers through more intuitive software interfaces.

What are the major trends shaping the landscape?

Let’s look at the major trends, layer by layer.

Layer #1: Proprietary geospatial data sources

• Data providers are moving from discrete imagery to continuous monitoring. A lot of companies in this layer are no longer just selling access to a specific image or dataset. They are packaging their data as a recurring monitoring capability. Satellogic is a good example with Aleph Observer and Merlin, which are built around continuous coverage of specific locations. BlackSky is doing something similar with Spectra and Gen-3. Vantor also pushes this direction with Sentry, a product focused on persistent monitoring and predictive intelligence.

• Defense and sovereignty are becoming one of the strongest growth drivers. This is very visible in the funding and GTM events. ICEYE raised a large Series F around sovereign intelligence from space. BlackSky signed major contracts with defense customers and the NRO. Vantor partnered with Rheinmetall and Anduril. Satellogic signed defense contracts and even moved into sovereign satellite transfer. In this layer, the strongest buyers are often customers who need secure, reliable, and recurring access to intelligence.

• New types of geospatial data are becoming commercial products. The market is moving beyond standard satellite images. Some companies, like ICEYE, Capella, Umbra, and Synspective, use radar based satellites that can see through clouds and work at night. Others, like Pixxel, Wyvern, and Orbital Sidekick, use sensors that can detect more than what the human eye can see. This makes it possible to identify crop stress, methane leaks, materials, or unusual activity at sea. The important shift is that these technologies are not only sold as “better data”. They are increasingly packaged around a specific customer problem.

• Plenty data providers are going up the stack. Many data providers are building more software around their core data. Planet launched Planet Insights Platform. ICEYE launched API Platform 2.0. Capella keeps improving tasking, archive access, and automated monitoring workflows. Tomorrow.io combines its weather satellite network with operational software for aviation and enterprise weather decisions. So even if the core asset is proprietary data, the product increasingly looks like a platform.

Layer #2: Geospatial data infrastructure

• The infrastructure layer is moving from data access to workflow platforms. Several companies in this layer started from a simple pain: geospatial data is hard to find and use. But the category is now moving beyond marketplaces. SkyWatch is a good example with HUB, BUILD, and Content Store for ArcGIS. UP42 is building a more complete workflow around tasking, processing, ordering, and marketplace access. Arlula is also moving in this direction with modular infrastructure for discovery, tasking, delivery, and normalization.

• Geospatial data is becoming more cloud native and developer friendly. This is where companies like Wherobots, Earthmover, and Ellipsis Drive are interesting. Wherobots is building around cloud-native spatial analytics, Python workflows, Databricks integration, and AI coding tools. Earthmover is building infrastructure for large scientific and geospatial datasets with Icechunk, Arraylake, and Flux. Ellipsis Drive is turning spatial files into shareable web services and map-native compute. The broader point is simple: geospatial data is becoming easier for software teams to work with.

• Enterprise readiness is becoming a product feature. A lot of the events in this layer are not flashy, but they are important. SkyWatch launched HUB to help teams manage buying, sharing, approvals, and spend. UP42 added budget controls and better project-level workflows. Wherobots achieved SOC 2 Type 2 and integrated with Databricks Unity Catalog. Earthmover added fine-grained access controls. These are the kinds of features that make geospatial infrastructure usable inside large organizations.

• AI increases the need for better geospatial infrastructure. AI does not remove the need for clean data infrastructure. It actually makes it more important. Wherobots launched RasterFlow to run AI models on satellite and overhead imagery. Earthmover launched a data marketplace for AI-ready weather and climate datasets. Arlula launched a data normalization pipeline to make imagery easier to analyze. If companies want to use geospatial data inside AI workflows, they still need the data to be clean, queryable, and connected to the rest of their stack.

Layer #3: Geospatial intelligence & applications

• Geospatial intelligence is becoming more vertical. This is the layer where the verticalization of geospatial data is the most obvious. Plume helps renewable energy developers decide where to build. TerraEye helps mining teams identify exploration targets. OneSoil helps farmers monitor fields. ZestyAI helps insurers understand property risk. Floodbase helps insurers and governments design flood insurance products. LiveEO monitors infrastructure and supply chains. The same underlying data can support all these markets, but the product looks very different depending on the buyer.

• The best applications are moving from insight to workflow. A lot of companies in this layer are not stopping at dashboards. They are trying to embed geospatial intelligence into the customer’s actual process. Floodbase launched a Quote API for instant parametric flood insurance quoting. LiveEO launched the TradeAware API for EUDR compliance workflows. OneSoil connects satellite insights to farm equipment workflows through BBLeap. Plume is moving from site selection into a broader site-to-permit workflow for infrastructure developers.

• Climate, risk, and regulation are strong monetization drivers. Many application companies are being pulled by insurance, finance, compliance, and climate risk. ZestyAI focuses on property and climate risk for insurers. Floodbase builds around flood risk and parametric insurance. Sust Global moved into institutional climate-risk workflows and was acquired by ISS STOXX. Kayrros expanded from methane monitoring into nature risk before being acquired by Energy Aspects. Treefera and LiveEO are both building around supply chain risk and EUDR compliance. When geospatial intelligence affects underwriting, investment, or regulation, the willingness to pay becomes clearer.