Mapping using your own UAV imagery in

2 min readDec 29, 2021

Here is a short guide that we’ve been requested for by some of users — how it’s possible to process your own aerial / UAV imagery.
For Mapflow, as a platform, you have different apps to interact with and we encourage our partners to build apps connected to Mapflow API for maximum of capabilities.

E.g. using Mapflow-QGIS you can upload your own data in GeoTIFF format. All raster layers currently loaded in your QGIS project are visible in the drop-down list and can be selected for upload.

However we’ve not implemented such a data uploader in, collecting more evidence of how it could be better implemented. In the meanwhile — if you have your UAV imagery prepared with non-sensitive information — you can use third-party services or a local tile server to supply your data to be processed with
Let me give you an example 👀:
Openaerialmap ( is an open collection of UAV imagery data, crowdsourced by users. The project is supported by a consortium of companies developing open source software and services for working with spatial data.

As soon as your aerial image published on Openaerialmap it’s presented on the public map and can be fetched using TMS/WMTS protocols.

Search for imagery in Openaerialmap

Copy link to TMS and paste it into the “Custom imagery URL” in your new Mapflow processing. Go through the next steps (AI model, processing params) to run your processing.

Check if you see the image on the map and run the processing

Here you go for your results (e.g. Building Detection):

Building Detection in aerial imagery provided by external source (Openaerialmap)

We started accumulating some useful tips and FAQ about Mapflow usage — so you’ll be able to find more in our documentation.

⚠️ Of course if you have some sensitive data that you don’t want to be presented on the public map — consider to roll your local environment for imagery tile server that will be compatible with TMS / WMTS protocols. We look forward to facilitating in this for the Mapflow users and partners knowing better how to deal with the large volumes of locally stored aerial data.




We apply Machine learning to automated analysis over Earth observation data