That's an insightful experience, thank you for sharing. To give a counter-counter point, in my experience working with LIDAR (terrestrial in my case, not airborne) there's a wealth of tools that claim to process large amounts of unstructured pointcloud data and make all kinds of fancy visualizations and 3d models (Pix4D comes to my mind). The "catch" was often to find high quality data to feed these tools (other than the sample data provided by them).
I'm convinced that having high-quality proprietary data is the moat the investors are looking for, not the algorithms. After all, universities are cranking improved algorithms all the time for free.
Oh for sure, I suppose that's also why the investors are still interested in us. But high-quality proprietary data is not what the customers are interested in. They just want to see a report that says how much m2 of brickwork needs to be repointed in the next 5 years. And they don't care if it's a machine learning model that deduced that information from high quality drone imagery, or if it was a dude on a ladder.
We're also working with terrestrial lidar btw. When we started out we thought we were going to do everything using photogrammetry, but right now we're deriving most of our geometry from the lidar.
