There is no shortage of impressive aerial data. High-resolution orthomosaics, dense point clouds, detailed 3D models, smooth fly-throughs, crisp imagery, colorized surfaces, and measurements that appear precise down to the centimeter—or even the millimeter—can all look remarkably convincing at first glance. That is exactly the problem. A deliverable can look extraordinary and still leave unanswered the most important questions about what it actually represents, how it was pro

Aerial intelligence is an increasingly common phrase. It sounds sophisticated. It suggests technology, information, perspective, and perhaps even a little bit of mystery about what happens between an aircraft leaving the ground and something useful appearing on a computer screen. But what actually defines it? For us, that question matters because we don't believe aerial intelligence is defined by the aircraft that collected the information. We don't believe it is defined by t

The drone industry spends a lot of time asking one question: What can the drone produce? An orthomosaic. A point cloud. A 3D model. A digital surface model. Progress photographs. Measurements.Contours. Those are useful answers. But they may be answers to the wrong question. The better question is: What can the project team do with what we produce? That changes the conversation. A 3D model may be impressive to look at. But what happens when a superintendent can use that model