A pothole monitoring and repair system for a road surface includes at least one UAV which includes a video camera, a video processor, a computing unit and a global positioning receiver. The UAV generates video streams of road surfaces using the video camera which are processed by the video processor to extract road frames. The computing unit generates a geo-fenced area of the road surface, and determines whether there is at least one pothole in each of the road frames within the geo-fenced area. The cloud server generates a map of the geo-fenced area, labels location coordinates of each pothole on the map, identifies and labels geographical features in the road frames within the geo-fenced area, extracts pothole features in the road frames, and uses a classifier to predict repair actions based on pothole features and geographical features, which are presented on a front end interface.

In some implementations, a UAV flight system can dynamically adjust UAV flight operations based on thermal sensor data. For example, the flight system can determine an initial flight plan for inspecting a flare stack and configure a UAV to perform an aerial inspection of the flare stack. Once airborne, the UAV can collect thermal sensor data and the flight system can automatically adjust the flight plan to avoid thermal damage to the UAV based on the thermal sensor data.

A verification application executing at a user's mobile phone directs the user to take a photo of an identification document (e.g., driver's license or other form of government issued identification) or of their face. After the verification application sends the image to a backend service, the service may then dispatch a drone to the user's location and take a photograph of the user. The service may then validate the previously-imaged document or photo of the user's face against the photograph of the user and the geolocation sent with the first image. The service may then verify the new account and provision a token to the verification application to complete a secured account generation.

A remote controlled lighter-than-air drone assembly that is capable of prolonged flight. The drone assembly utilizes a balloon structure. Separately, a reservoir is provided for holding a smaller second volume of gas. A propulsion system and a control unit are carried by the balloon structure. The control unit selectively transfers the gas from the reservoir to the balloon structure, and selectively vents the gas as needed. A receiver is used to receive command signals from an external source. The command signals are utilized to operate the propulsion system. An electronics suite is provided that can be altered depending upon duties. The electronics suite is used to scan or otherwise monitor an area below the drone assembly. In flight, the balloon structure is translucent and internally illuminated. A projector can be provided for projecting images onto the interior of the balloon structure.

The present disclosure relates to a method for measuring a water level by using a UAV and virtual water control points and a method for generating 3D water surface spatial information, and a UAV used therefor. According to an embodiment, an UAV for a water surface survey includes: a position measurement unit configured to receive a GPS signal and to obtain position information of the UAV; a distance measurement unit including a plurality of laser measurement devices configured to project lasers toward the water surface; and a controller configured to calculate a moving distance of the UAV, based on measurement values of the position measurement unit and the distance measurement unit.

An unmanned aerial vehicle (UAV) or unmanned surface vessels (USV) collect electro-optical images, LiDAR or sonar to create a three-dimensional (3D) point cloud referenced to a geodetic datum. The resulting point cloud is a digital surface representation of the earth's surface. The process of the geospatial cleaning and point cloud compression entails cleaning of the point-cloud surface and to the automated extraction of a set of 3D vector polylines and mass points. The resultant electronic file retains the details of the bare-earth point-cloud with an approximate 10× compression in file size or more.

A drone is deployed from a vehicle, e.g., an autonomous or semi-autonomous vehicle, to assist in vehicle control, e.g. in situations in which the vehicle's embedded sensors may not provide sufficient information to perform a desired operation safely, e.g. backing up, parking in a tight environment, traversing a very narrow road, navigating a sharp corner, or bypassing an obstruction, etc. The deployed drone includes sensors, e.g. cameras, radars, LIDARs, etc, which capture sensor data from a position offset from the vehicle. Captured sensor data is communicated from the drone to a vehicle control system in the vehicle and/or to a remote control system, e.g., including an operator who can make decisions. Based on the captured sensor data, which supplements sensor data collected by the vehicle's embedded sensors, vehicle movement is controlled.

In one aspect of the present disclosure, a gimbal assembly is described for use with an image capturing device. The gimbal assembly includes a motor assembly, a first housing defining an internal compartment that is configured and dimensioned to receive the motor assembly, and a second housing that is mechanically connected to the motor assembly such that actuation of the motor assembly causes relative rotation between the first and second housings. The first housing includes a first guide that is configured and dimensioned to support transmission media adapted to communicate electrical and/or digital signals. The second housing defines a channel that is configured and dimensioned to receive the first guide such that the first guide extends into the second housing through the channel. The transmission media is supported on the first guide such that the first guide routes the transmission media from the first housing into the second housing.

A post-disaster conditions monitoring system. The system may include plurality of aircraft drones configured to take photographic images; a conditions monitoring center having a controller including a device processor and a non-transitory computer readable medium including instructions executable by the device processor to perform the following steps: receiving images from the plurality of aircraft drones in a geographic region; determining conditions in the geographic region based on the images received from the plurality of aircraft drones; and sending information regarding the determined conditions to one or more users of the system.

A method for operating a commercial shipping asset includes entering one of a docking and an undocking mode of operations using a vehicle controller of a commercial shipping asset, identifying at least one trusted video source remote from the commercial shipping asset, determining a distance between the commercial shipping asset and at least one object based at least in part on an image analysis of a video feed from the trusted video source, and responding to the determined distance by providing at least one of a warning to an operator of the commercial shipping asset and adjusting an automated or semi-automated operation of the commercial shipping asset.