The current model for scale is to use a hobby-class drone. This makes sense at a high level. These off-the-shelf UAVs are affordable and widely available. If you crash one into a tower, it’s easy to buy another. Limited specialized training is required, and anyone can purchase one. The idea is you can push a button and let it go – autonomously.
This sounds great in theory, but in practice it doesn’t work in Telecom. Tower structures are dissimilar, complex, and capturing full coverage of the structure is challenging. Furthermore, the camera sensor on these drones is designed to be extremely lightweight, small, and affordable, so they appeal to the hobbyist budget too. The resulting tower data collected using these drones is spotty and imprecise. While there are certainly good 3d examples where tower data was collected in ideal conditions when wind, heat, cloud cover, and sunshine weren’t an issue – the mathematics of photogrammetry limit what is possible with these drones. And rather than modify the camera to better capture tower information, drone manufacturers and flight software companies ask operators to fly closer to structures and trust onboard sense-and-avoid systems to identify wires, cabling, and other hazards.
With all this, you still only get “inspection-class”, centimeter-accurate 3d data. Typically, there are isolated areas where accuracy might be sufficient, but data accuracy and coverage deteriorate as you move down the 3d model. You can run this data through Digital Twin software for tower analysis, but AI can’t extract dimensional measurements that are required to calculate loading and capacity. This leaves you with a high margin of error and a low-confidence result.