There are many automated solutions that solve really interesting problems, but at this point in time, it is the semi-automated solutions that really fascinate me. These are solutions that combine the subtlety and intelligence of the human mind with the scale of automation.

In this context, I have been thinking a lot about OpenDroneMap — what are the parts of the toolchain which should be automated and improved, and where can a human touch help. Mostly I have been thinking about how ODM can be improved by a human touch, especially where such interpretation can aid in creating better 3 dimensional structure. However, recently as I thought about Micromappers I coined (I think I coined it) the phrase “geocoding from structure from motion”.

Imagine if you will a system that allows a small army of volunteers to easily code images with information in order to help make sense of the world through those images. Imagine this system is tied to aerial imagery and used in a humanitarian crisis. Put these things together and you have Micromappers.

So, what if any thing digitized or circled was automatically geocoded in 3D space based on the 3D information derived from Structure from Motion. By geocoded, I don’t mean geocoded to the location of the camera, but geocoded to the location of the feature in 3D space based on deriving implicit 3D information from the video combined with the GPS position of the camera.

OpenDroneMap and a host of other tools already generate 3D info based on image inputs. Below is a video of LSD-SLAM, a technique that does this in real time that might make it a little clearer what this magic is.

(Enter, selfish side of this thought)

If a workflow like this worked out, then we can geocode anything by marking up individual stills in an image series. Further, the information we derive from this markup can then be used to help classify and improve other outputs (like digital surface models, digital elevation models, etc.) from Structure from Motion. Finally, prior to OpenDroneMap being feature complete as a drone imagery processing tool, we have an easy-to-use tool for deriving good enough secondary products, i.e. geocoding, with primary products slated for improvement being orthophoto, mesh, and point cloud.

Post script note: this is not a funded project, but an interesting thought experiment. I’ll have a future of OpenDroneMap (as I see it) post up here shortly.