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Archive for June, 2015

OpenAerialMap, OpenImageryNetwork, MapKnitter, OpenTerrain, and OpenDroneMap (cont. 1)

Posted by smathermather on June 7, 2015

Citing my previous post, let’s move on to more specifics on my thoughts regarding the integration of OpenAerialMap, OpenDroneMap, and MapKnitter as projects.

Image from kite over Seneca Golf Course

OpenDroneMap ❤ OpenAerialMap.

OpenAerialMap will become a platform by which drone users can share their imagery under an open license.

So, as the metadata spec for OpenAerialMap and OpenImageryNetwork matures, and as soon as a publicly available place for drone users to push their data comes online, ODM will write appropriate metadata and geotiffs to go into OIN to be indexed by OAM. Probably as an added bonus, ODM should be able to optionally auto-upload outputs from to the appropriate node on the OpenImageryNetwork.

Lincoln Peak Vinyard

OpenDroneMap ❤ MapKnitter.

MapKnitter / ODM integration is pretty straight forward in my mind too. There are ways that MapKnitter complements ODM, and vice versa. ODM does not have a graphical user interface at this time. MapKnitter promises to fill that role in a future OpenDroneMap implementation. MapKnitter has no image blending or auto-matching tools. OpenDroneMap will soon have both.

  • Ways MapKnitter may help OpenDroneMap:
    • MapKnitter’s clever use of Leaflet to handle affine transformation of images is really exciting, and may help with improving final georeferencing for ODM datasets.
    • Regarding the above, one really useful thing for fliers launching balloons, drones, and kites without GPS would be the ability to quickly and easily perform really approximate georeferencing. I would envision a workflow where a user moves an image to its approximate position and size relative to a background aerial. ODM would be able to take advantage of this approximate georeferencing to optimize matching.
  • Ways OpenDroneMap could benefit MapKnitter
    • For large image datasets, matching images can be very tedious. Automatic feature extraction and matching can help. OpenDroneMap could be adapted to serve back match information to Mapknitter to ease this process. This will become increasingly important as MapKnitter raises the ~60 image limit on images that it can process.
    • A near future version of ODM will have image blending / smoothing / radiometric matching. For the server portion of the MapKnitter infrastructure, this feature could be a really useful addition for production of final mosaics.

These projects (plus OpenTerrain…) are really exciting in their own right. Together, they represent amazing opportunities to foster, cultivate, process, and serve a large community of imagery providers, from individuals and small entities capturing specific datasets using kites, drones, and balloons, to satellite imagery providers hosting their own “image buckets” of open imagery data. Exciting times.

Image over Groth Memorial from kite

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