A method for quantifying an exposure dose for a surface is disclosed. The method may include emitting one or more beams of 222 nm light onto a portion of the surface using one or more far ultraviolet (UV) light sources capable of emitting 222 nm light, the portion of the surface being coated with one or more fluorescent coatings. The method may include capturing images of the portion of the surface. The method may include adjusting one or more image characteristics for the captured images using one or more filtering methods. The method may include generating a histogram of the adjusted images based on the one or more filtering methods. The method may include determining a pixel surface area for the generated histogram. The method may include calculating the exposure dose for the surface based on the generated pixel surface area and a predetermined calibration curve.

A method for quantifying an exposure dose for a surface is disclosed. The method may include emitting one or more beams of 222 nm light onto a portion of the surface using one or more far ultraviolet (UV) light sources capable of emitting 222 nm light, the portion of the surface being coated with one or more fluorescent coatings. The method may include capturing images of the portion of the surface. The method may include adjusting one or more image characteristics for the captured images using one or more filtering methods. The method may include generating a histogram of the adjusted images based on the one or more filtering methods. The method may include determining a pixel surface area for the generated histogram. The method may include calculating the exposure dose for the surface based on the generated pixel surface area and a predetermined calibration curve.

Systems and methods are disclosed for transporting people using air vehicles.

Systems and methods are disclosed for transporting people using air vehicles.

A user terminal generates a virtual drone camera image, as an estimated captured image where it is assumed that a virtual drone camera mounted on a drone has captured an image of a planned landing position on the basis of a captured image obtained by capturing the planned landing position of the drone with the user terminal, and transmits the generated virtual drone camera image to the drone. The drone collates the virtual drone camera image with the image captured by the drone camera and lands at the planned landing position in the image captured by the drone camera. The user terminal generates a corresponding pixel positional relationship formula indicating a correspondence relationship between a pixel position on the captured image of the user terminal and a pixel position on the captured image of the virtual drone camera, and generates the virtual drone camera image using the generated relationship formula.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a ground surface is within the field of view of the camera during taxiing of the aircraft. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera for docking guidance by identifying, within the captured image data, a region on the ground surface corresponding to an alignment fiducial indicating a parking location for the aircraft, determining, based on the region of the captured image data corresponding to the alignment fiducial indicating the parking location, a relative location of the aircraft with respect to the alignment fiducial, and outputting an indication of the relative location of the aircraft to the alignment fiducial.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a ground surface is within the field of view of the camera during taxiing of the aircraft. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera for docking guidance by identifying, within the captured image data, a region on the ground surface corresponding to an alignment fiducial indicating a parking location for the aircraft, determining, based on the region of the captured image data corresponding to the alignment fiducial indicating the parking location, a relative location of the aircraft with respect to the alignment fiducial, and outputting an indication of the relative location of the aircraft to the alignment fiducial.

A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.

A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.

Methods and systems for automatically deploying an autonomous drone from a vehicle in response to a triggering event or accident so that data associated with the triggering event or accident may be automatically obtained are described. In one embodiment, a method for deploying an autonomous drone in response to a triggering event is described. The method includes providing an autonomous drone in a vehicle. The method also includes detecting a triggering event associated with the vehicle. Upon detection of the triggering event, the method includes automatically deploying the autonomous drone from the vehicle. The method further includes implementing, by the autonomous drone, a plurality of automatic actions, including recording data associated with the vehicle in which the autonomous drone is provided.