Geospatial data is only useful if it knows where it belongs. That’s the simple idea behind georeferencing—a process that connects your maps, drone images, or scanned plans to real-world locations. Without it, you’re basically working with floating data that can’t be measured, overlaid, or trusted.
So whether you're mapping a mine site, monitoring erosion, or just trying to line up a few layers correctly, georeferencing is what makes spatial data accurate and usable.
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Georeferencing is the process of aligning spatial data—like drone imagery or scanned maps—to real-world coordinates. It allows you to place that data accurately on a map so it can be measured, analyzed, and compared with other geospatial layers. This alignment relies on metadata such as the coordinate system, projection, and resolution of the data.
Imagine flying a drone over a construction site. You capture a high-res image of the area, but when you open it on your computer, there’s no context—no coordinates, no way to overlay it on other maps or measure distances. Georeferencing fixes that by telling the image where it sits on the Earth’s surface.
Once the image is georeferenced, it can:
Without georeferencing, your data isn’t grounded—and that means it’s not reliable for decision-making.
Georeferencing relies heavily on metadata: the behind-the-scenes info that tells us how to interpret the spatial data. If you’ve ever opened a drone image and seen information about the GPS location, altitude, camera settings, or coordinate reference system, that’s metadata at work.
Some key types of geospatial metadata that support georeferencing include:
Most modern mapping platforms, including Birdi, read this metadata automatically during processing. That means when you upload drone images captured with GPS-enabled cameras, the system can georeference them for you—no manual work required.
Depending on the type of data you’re working with, georeferencing can happen in a few different ways:
Drone photos and satellite images often come with embedded GPS data in the metadata. If you're using a mapping platform like Birdi, it reads this information and places the imagery correctly on a map during processing.
You can view your image metadata in Birdi's table view:
For projects that need higher precision—like engineering surveys or volumetric analysis—GCPs can be used. These are known points on the ground with accurate coordinates that are marked in the imagery to increase georeferencing accuracy.
This is often used for older maps or scanned plans that don’t include location metadata. You manually match features in the image to real-world coordinates using control points (e.g. a road intersection or building corner).
In short: things won’t line up. You might experience:
For example, imagine overlaying unreferenced drone imagery on an existing site layout—it might look close, but it could be off by meters. That can be a big problem when making decisions about earthworks, vegetation removal, or utility inspections.
At Birdi, georeferencing happens automatically when users upload drone imagery with location metadata. Users on higher plans can also:
Georeferencing is what takes your imagery from "just a picture" to a useful spatial layer. Combined with accurate metadata, it ensures your geospatial outputs are reliable, measurable, and aligned with the real world.
If you're capturing data in the field, always make sure your equipment is recording location information—and check that your platform is reading that metadata correctly when you upload. It might seem like a background process, but it's the backbone of everything you do in mapping.
