Categories
Thoughts

Developing a Method to Discover Assets Inside Digital Twins

On Monday I had a bit of a tweetstorm to get some thoughts on paper.

In there I laid out what I thought addressing inside a building should look like. A couple of responses came to the “why” or “this isn’t an issue” but the important thing here is with smart buildings, they need to be able to route people not only to offices for “business” but workers to IoT devices to act upon issues that might occur (like a water valve leaking in a utility closet). Sure one, could just pull out an as-built drawing and navigate, or in the case of visiting a company, the guard at the front door, but if things such as Apple Glass and Google Glass start becoming a real thing, we’ll need a true addressing system to get people where they need to be.

Apple and Google are working this out themselves inside their ecosystems but there needs to be an open standard that people can use inside their applications to share data. I mentioned Placekey as a good starting point with their what@where.

The what is an address – poi encoding and the where is based on Uber’s H3 system. As great as all this is, it doesn’t help us figure out where the leaky valve is in the utility closet. This all is much better than other systems and is a great way to get close. I’ve not seen any way to create extensions to Placekey to do this but we’ll punt the linking problem for now.

The other problem with addressing inside a building is the digital twin might not be in any projection that our maps understand. So we’ll need to create a custom grid to figure out where the IoT and other interesting features are located. But there seems to be a standard being created that solves just this problem, UBID.

UBID builds on the open-source grid reference system and is essentially the north axis-aligned “bounding box” of the building’s footprint represented as a centroid along with four cardinal extents.

I really like this, it might even compete with Placekey, but that’s not my battle, I’m more concerned with buildings in this use case. There is so much to UBID to digest and I encourage you to read the Github to learn more.

But if we can link these grids of buildings, with a Placekey, we have a superb method of navigating to a building POI and then drilling down into navigating to that location using all the great work that companies like Pixel8 are doing. But all that navigation stuff is not my battle, just a location of an IoT sensor in a digital twin that may or may not be in a project we can use.

Working toward that link, a unique grid of a digital twin to a Placekey would solve all problems with figuring out where an asset inside a building is and what is going on at that location. The ontologies to link this could open up whole new methods of interrogation of IoT devices and so much more. e911 and similar systems could greatly benefit from this as well.

Categories
Thoughts

Smart Cities and Digital Twins Will Be Built Using Smart Phone Cameras

I’ve spent years trying to build worldwide building datasets for Smart City and Digital Twin applications. I’ve tried building them using off-the-shelf data providers that give you COLLADA files, I’ve tried using APIs such as the Mapbox Unity SDK and buying buildings one by one to fill in gaps. None of these solutions have the resolution needed to perform the types of analysis needed to make better choices for cities and development potential. How to create real 3D cities with enough resolution has been out of our grasp until now.

I’ve been following Pixel8 for a while now and it is clear that crowdsourcing these models is going to be the only way forward. Over 10 years ago, Microsoft actually had this figured out with their Photosyth tool but they never were able to figure out what to do with it. Only today are we seeing startups attack this problem with a solution that has enough resolution and speed that we can start seeing cities build highly detailed 3D models that have actual value.

Example stolen from Pixel8

It is still early days with these point cloud tools, but at the speed they’ve improved over the last year, we should be seeing their use more and more. Mixing the data from smartphones, lidar and satellite imagery can make large areas of cities mapped in 3D with high accuracy. Pixel8 isn’t the only company attempting this so we should see real innovation over the next year. Stay tuned!