[jre]

I work at Picterra[1], a swiss company doing machine learning on drone & satellite imagery.

From discussion with a number of drone service providers, it seems to me the restrictiveness really depends on the context.

For things involving flying a drone over a crowded area, it's definitely hard to get authorization and we've seen projects being abandoned because of that. So yeah, drone delivery in cities might take some time before it is allowed.

On the other hand, most of the commercial applications I've seen are around using drones for mapping. So basically taking pictures and then processing them (create orthophotos, build a 3D model, detect objects, etc...). This usually doesn't involve flying over crowd and for a number of use cases (farms, construction site, security) the fly would be over a private property. A lot of people are able to do this without any legal restrictions.

From what I know of the history of the swiss Drone Valley, the two biggest success are sensefly [2] and Pix4D [3] and those are in the mapping business. Two of the booming startups would be wingtra [4] and flyability [5]. Wingtra seems to be mapping-oriented as well and Flyability develop drones that can fly inside. So a lot of the swiss drone valley is really into drones that can take pictures for mapping & inspection and I think this segment will continue to see massive growth in the future.

[1] https://www.picterra.ch/ [2] https://www.sensefly.com/ [3] https://pix4d.com [4] https://wingtra.com/ [5] https://www.flyability.com/

[kozikow]

> On the other hand, most of the commercial applications I've seen are around using drones for mapping. So basically taking pictures and then processing them (create orthophotos, build a 3D model, detect objects, etc...).

In my opinion, the majority of those applications are better served by either CCTV or manned aircraft.

Mapping use cases - If the area is big enough, manned aircraft mapping is much cheaper than drone mapping. Typical airplane ortho is around the 10cm/pixel, so it is sufficient for the majority of use cases. E.g. Check our nearmap. If the area is small, the machine learning is not going to make lots of $, as it's better to just use humans.

Monitoring use cases - e.g. "I want to detect if construction workers are wearing helmets" or "I want to detect poachers in the area". You can provide more business value by doing computer vision on CCTV camera.