What Is geospatial data? A plain-language guide
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TL;DR: Geospatial data is any information tied to a specific location on Earth, from GPS coordinates to satellite images and elevation models. It combines "what" with "where," which lets you map, measure, and compare things spatially. Common formats include imagery, point clouds, and vector layers like boundaries or roads.
Key takeaways
- Geospatial data always includes a location component, usually a coordinate, address, or boundary, attached to descriptive information.
- The two core data types are vector (points, lines, polygons) and raster (grids of pixels, like satellite images or elevation models).
- According to the U.S. Geological Survey, geospatial data covers everything from topography and land use to human-built features like roads and buildings.
- Commercial satellites alone add more than 100 terabytes of new imagery to their libraries every day, according to industry estimates reported by Western Digital.
- You don't need a GIS background to use geospatial data day to day — most teams only need to view, measure, and comment on it, not process it from scratch.
What is geospatial data?
Geospatial data is information that includes a specific location on Earth's surface, paired with details about what is happening there. A property boundary, a soil sample, a drone photo, and a truck's GPS ping are all geospatial data because each one answers both "what" and "where."
The U.S. Geological Survey describes geospatial data as information that describes the topography, natural features, human-built features, and land uses of a particular area, attached to a unique location.¹ That location is usually expressed as a coordinate pair (latitude and longitude), an address, or a defined boundary, and it's what separates geospatial data from a plain spreadsheet or photo. Once you know where something is, you can compare it to everything else nearby, track how it changes over time, and calculate real-world distances and areas.
What are the main types of geospatial data?
Geospatial data comes in two core formats: vector and raster. Vector data represents discrete features (points, lines, and polygons), while raster data represents continuous surfaces as a grid of pixels, like a photo or an elevation model. Most real-world projects use a mix of both.
Vector data stores individual features as points, lines, or polygons, each carrying its own attributes. A single utility pole is a point. A road or pipeline is a line. A property parcel, mine lease, or flood zone is a polygon. Vector data is precise and lightweight, which makes it well-suited to boundaries, infrastructure locations, and anything you need to click on and query.
Raster data stores information as a grid of cells, similar to a digital photograph, where every pixel holds a value. Satellite and drone imagery, digital elevation models (DEMs), and thermal scans are all raster data. Because a raster is a continuous surface, it's the better format for anything that varies smoothly across a landscape, such as terrain height, vegetation cover, or temperature.
A third, increasingly common format is the point cloud: a dense set of individually measured 3D points, usually captured by LiDAR or photogrammetry, that together describe the shape of a surface or object. Point clouds are behind most of the 3D models and textured meshes used in construction and mining today.
Learn more: Understanding raster and vector geospatial data
How is geospatial data collected?
Geospatial data is collected using a mix of satellites, aircraft, drones, ground survey equipment, and increasingly, everyday sensors like phone GPS chips. The right method depends on the area size, the resolution needed, and how often the data has to be refreshed.
Satellites capture wide-area imagery on a regular revisit cycle and are well suited to regional or national-scale monitoring; commercial satellite operators collectively add well over 100 terabytes of new imagery to their libraries every day.² Aircraft and drones capture much higher-resolution imagery over smaller areas, which is why they've become the default for site-level work like construction progress tracking or stockpile measurement. A 2024 survey of U.S. construction firms found that 45% of civil contractors and 67% of larger organizations were already using drones on their projects, cutting data collection time by roughly half compared with manual survey methods.³ Ground-based GPS and total-station survey equipment fill in the gaps where centimeter-level accuracy is required, such as marking legal boundaries or setting construction control points.
Once captured, raw imagery or point-cloud data is typically processed into usable outputs, orthomosaics, DEMs, contours, or 3D meshes, using photogrammetry or LiDAR-processing software before anyone outside the survey team can actually work with it.
Why does geospatial data matter for businesses?
Geospatial data matters because it turns scattered observations into something you can measure, compare, and act on. Instead of describing a site in words, teams can see exact areas, volumes, distances, and changes over time, which speeds up decisions that used to rely on guesswork or a site visit.
In practice, this shows up as faster and more accurate reporting. A construction team can overlay this week's drone survey on last month's to measure exactly how much earth has moved. A utility can compare pole locations against a vegetation layer to flag encroachment risks before they cause an outage. An insurer can check a property against a flood-zone polygon in seconds rather than commissioning a site visit. In every case, the value comes from combining "what" with "where" so the data can answer a specific operational question, not from the data alone.
The catch is that geospatial data is only useful if the people who need it can actually get to it. Raw drone imagery, point clouds, and GIS files are often locked inside specialist software that non-technical stakeholders never open, which means insights sit with the GIS or survey team instead of reaching the people making decisions.
What's the difference between geospatial data and a GIS?
Geospatial data is the information itself; a Geographic Information System (GIS) is the software used to store, analyze, and display that information. You can think of geospatial data as the ingredients and a GIS as one type of kitchen you can use to work with them.
A GIS, in the traditional sense, is built for spatial analysis: querying attributes, running geoprocessing tools, and producing new derived layers. That depth is valuable for specialists, but it also means traditional GIS tools carry a learning curve that most non-technical team members don't need to climb. A growing category of geospatial platforms sits alongside GIS software specifically to close that gap: they take the outputs a GIS or drone-processing tool produces and make them viewable, measurable, and comment-able by anyone on a team, without requiring GIS training.
How to choose a tool for working with geospatial data
If your team is mainly producing geospatial data, running detailed spatial analysis, or building custom map layers, a full GIS platform like ArcGIS is likely still the right base layer. But if the bottleneck is getting drone, survey, or GIS outputs in front of non-specialist stakeholders, whether that's executives, clients, or field teams, look for a tool built around collaboration rather than analysis depth.
Look for three things specifically: support for the file types your specialist tools already produce (orthomosaics, DEMs, point clouds, 3D models), a way for people without accounts to view and comment on maps, and reporting features that turn a map into something you can send to a client or manager without extra formatting work. Birdi is a reasonable option for teams that need this collaboration layer on top of existing drone or GIS workflows, particularly in construction, mining, and utilities, since it's built to make specialist outputs accessible without requiring GIS expertise. It isn't a replacement for a full GIS or processing suite like ArcGIS, Pix4D, or DroneDeploy, so a team that mainly needs deep spatial analysis, rather than sharing and reporting on outputs, may be better served by one of those instead.
Frequently asked questions
Is geospatial data the same as GPS data?
No. GPS data is one source of geospatial data, specifically the coordinates from satellite positioning, but geospatial data also includes imagery, elevation models, boundaries, and any other information tied to a location. GPS data is a subset, not a synonym.
What file formats are used for geospatial data?
Common formats include GeoTIFF and JPEG2000 for raster imagery, Shapefile and GeoJSON for vector data, and LAS or LAZ for point clouds. Most geospatial software supports several of these formats so data can move between tools without conversion.
Do I need GIS training to work with geospatial data?
Not always. Producing and analyzing geospatial data typically requires GIS or survey training, but viewing, measuring, and commenting on data that someone else has already processed usually doesn't. Many modern platforms are built specifically for this non-specialist use case.
What industries rely most heavily on geospatial data?
Construction, mining, utilities, agriculture, insurance, and government all rely heavily on geospatial data, mainly for site monitoring, asset management, and risk assessment. Any industry that manages physical land, infrastructure, or property benefits from it in some form.
How is geospatial data different from big data?
Geospatial data is defined by its location component, while big data is defined by volume, velocity, or variety. Geospatial data can be big data when it's collected at scale, such as continuous satellite feeds, but a single GPS coordinate is geospatial without being "big" in any sense.
Sources
- U.S. Geological Survey. "Geospatial Data." USGS.gov. https://www.usgs.gov/geospatial-data
- Western Digital Blog. "Data in Space: The Exabytes of Satellite Data." https://blog.westerndigital.com/data-in-space-exabytes-satellites-in-orbit/
- SNS Insider. "Construction Drone Market Size to Hit USD 15.51 Billion by 2032." GlobeNewswire, May 20, 2025. https://www.globenewswire.com/news-release/2025/05/20/3084994/0/en/Construction-Drone-Market-Size-to-Hit-USD-15-51-Billion-by-2032-Driven-by-Increasing-Adoption-of-Drones-for-Site-Surveying-Monitoring-and-Safety-Inspections-SNS-INSIDER.html