Unfortunately sometimes we are unable to process the data that you upload to GeoNadir. Here are some of the most common challenges we see with some ideas for how to be more effective in your data capture next time to achieve success. If you feel that none of the below apply to you and your data, please get in touch with us!

As we process your data, the computer is looking for matching features (pixels) in areas of overlap and sidelap between sequential photos. If there's not enough images (e.g. only one image), such process could not take place at all. If you're uploading your first dataset, a minimal flight lines with sufficient overlap and sidelap is a good starting point.

As we process your data, the computer is looking for matching features (pixels) in areas of overlap and sidelap between sequential photos. If the overlap and sidelap is insufficient, the computer can't find enough matching features. We recommend using 80% overlap and 80% sidelap for all missions.

If there aren't many distinctive features in your imagery, the computer finds it difficult to match similar pixels. Large areas of water, grasses, dry agricultural fields, or concrete sometimes have this challenge. Try flying at a higher altitude as this will often mean that each photo will encompass a greater variety of features that the computer can recognise.

In cases where there are large water bodies that are highly reflecting sunlight, the computer processing algorithms view these areas as homogenous and are unable to find matching features. Try capturing your data earlier or later in the day to avoid glint, particularly in the middle of your photos. Increasing altitude can also mean that each photo will encompass a greater variety of features that the computer can recognise.

Our processing algorithms are optimised for nadir (top-down) imagery. While we can process datasets with some oblique images, for best results make sure that you have covered your entire site with the drone camera pointing directly down.

If photos are too bright (overexposed) or too dark (underexposed) the computer will have difficulty finding matching features in the photos. Have a look at the camera white balance and ISO settings - usually automatic works well as a starting point.

If features in the images are blurry, it will be difficult for the computer to identify matching pixels. Focus issues can arise from flying too fast, or having the incorrect focal point. Usually autofocus is the best setting to use, though try infinity focus if you are having difficulty with autofocus changing throughout a capture. If you use a mapping mission planning app (preferable), you shouldn't have an issue with speed, though it's worth checking this parameter as well. Finally, check that the lens is clean.

This is often an issue on windy days with trees or grasses that move around a lot (e.g. palm trees, sugar cane, wave action on the water). Sometimes this will result in duplicate features in an orthomosaic, but in extreme cases it may mean that the orthomosaic can't be created at all.

We are currently processing RGB, thermal, and multispectral drone data (essentially correctly geotagged jpg and tiff images) captured as still images only (not video).

Drones sometimes allow users to add timestamps to images or include watermarks to mark ownership. At first glance, this seems convenient, as it eliminates the need to check metadata for these details. However, since timestamps and watermarks are directly embedded onto the images, they can cause significant issues during feature matching, potentially leading to orthomosaic failure.

Here is an example of how it looks sometimes.

Want to learn more? Check out our range of training options to help you take your drone mapping skills to the next level!