Data that is gathered by unmanned vehicles such as UAVs (unmanned aerial vehicles), UUVs (unmanned underwater vehicles) and USVs (unmanned surface vessels) typically needs to have some kind of further processing applied to it in order to make it actionable.
The most common application for drone data processing is photogrammetry. Images captured by drones may be combined with data from an IMU (inertial measurement unit), GNSS receiver or in-field GCPs (ground control points). These images are then stitched together, with the data processing software handling such tasks as geotagging the images and calculating the overlap. The results are then used to create products such as orthophotos, high-accuracy maps, and 3D models, which can in turn be used for accurate measurement of distances, surface areas, volumes of stockpiles and other required parameters.
Drone data processing solutions usually involve one of three choices –
The choice of workflow will be different depending on the size, accuracy requirements and type of the dataset.
Local self-processing can be the best choice for small data sets, although inexperienced users who require high-accuracy results may find it unsuitable. Large datasets may require a powerful computer or multiple servers to process, and this hardware is unlikely to be owned by many drone companies. Specialist drone data processing companies may have invested in suitable hardware, and may also be staffed by experts who have the experience to correctly calibrate and process the data and spot errors and anomalies.
Uploading data to an automated cloud data processing service is quick and easy and allows users to take advantage of powerful hardware. Some cloud data processing solutions may use artificial intelligence (AI) and deep learning to process the information and perform tasks such as automated feature extraction.
AI data processing can be used by drone inspection companies to carry out tasks such as fault-finding in wind turbines, power lines and other infrastructure, as well as to process and remove noise from large sonar datasets captured by UUVs and AUVs (autonomous underwater vehicles).