The present disclosure is related to improving image quality in a scanning camera system via scan angle selection to obtain images having overlap for performing image stitching, dynamically tuning an aperture of a camera in the scanning camera system, updating pixel values of an image using vignetting data, or a combination thereof.

A method for image generation, preferably including: generating a set of mission parameters for a UAV mission of the UAV associated with aerial scanning of a region of interest; controlling the UAV to perform the mission; generating an image subassembly corresponding to the mission; and/or rendering the image subassembly at a display.

An unmanned aerial vehicle includes a camera, one or more sensors, memory storing first instructions that define an overall mission, and memory storing one or more mission cues. The vehicle further includes one or more processors configured to execute a first part of the first instructions to perform a first part of the overall mission. The processors are configured to process at least one of the image data and the sensor data to detect a presence of at least one of the mission cues. The processors are configured to, in response to detecting a mission cue, interrupting execution of the first instructions and executing second instructions to control the unmanned aerial vehicle to perform a first sub-mission of the overall mission. The processors are configured to after executing the second instructions, performing a second part of the overall mission by executing a second part of the first instructions.

A method for real-time crowd management includes receiving LiDAR point cloud data from area of interest, forming a 3D static surface model of area of interest from LiDAR point cloud data, obtaining real-time CCTV camera images of area of interest, adding real-time CCTV camera images to 3D static surface model to generate real-time dynamic 3D model, identifying dynamic objects in the 3D model, generating a density map of an area of interest, adding density map to the 3D model, identifying which dynamic objects are people, replacing each person with animated character, displaying real-time dynamic 3D model, monitoring the 3D model in the area of interest for dangerous situations, simulating evacuation of area of interest by manipulating animated characters onto pathways leading away from area of interest, forming an evacuation strategy for crowd, and transmitting a notice of the dangerous situation and the evacuation strategy to an emergency authority.

Techniques are disclosed for mapping in a movable object environment. A system may comprise a movable object, a payload coupled to the movable object, the payload comprising an embedded system including an embedded processor, a scanning sensor, one or more cameras, and an inertial navigation system (INS). The payload further including an online processing application, the online processing application including instructions which, when executed by the embedded processor, cause the online processing application to obtain mapping data from the scanning sensor, obtain image data from a camera of the one or more cameras, obtain positioning data from the INS, associate the mapping data with the positioning data to generate georeferenced data, downsample the georeferenced data to generate downsampled georeferenced data, and provide the downsampled georeferenced data to a client device to be visualized on the client device in real-time.

A method and system to receive, one or more first images, one or more second images, one or more ambient weather related information, and one or more land related information, wherein the one or more land related information comprise vegetation features, terra firma topography, elevation, slope and aspect, of one or more regions of interest, of a geographical region; to map automatically, one or more risk areas of the one or more regions of interest; to recognize automatically, one or more smoke or fire related signals; and to predict computationally, existence of a fire causing smoke, a fire, a fire-growth and spread.

A system having: a processor and addressable memory, where the processor is configured to: receive a geographic data defining a selected geographical area; receive an operating mode associated with the selected geographical area, where the received operating mode restricts at least one of: a viewing of a UAV data and a recording of the UAV data by at least one user device; and broadcast the UAV data to the at least one user device based on the selected geographical area and the received operating mode.

In an aspect, a decision platform that optimizes honey value chain can be provided. The decision platform may receive images of a geographic region including catchment areas, run a first machine learning model with the images as input to identify resources in the catchment areas, run a second machine learning model with the identified resources to predict pollen and nectar concentration in the catchment areas, run a third machine learning model with at least the predicted pollen and nectar concentration to predict honey yield in each of the catchment areas, and determine placement of a swarm to at least one of the catchment areas. The decision platform may also control an unmanned aerial vehicle to guide the swarm to at least one of the catchment areas.

A system having: a processor and addressable memory, where the processor is configured to: receive a geographic data defining a selected geographical area; receive an operating mode associated with the selected geographical area, where the received operating mode restricts at least one of: a viewing of a UAV data and a recording of the UAV data by at least one user device; and broadcast the UAV data to the at least one user device based on the selected geographical area and the received operating mode.

The present disclosure relates to systems, methods, and computer-readable storage devices for the coordination of actions between movable objects. For example, a method may coordinate actions between at least a first and a second movable object. The method may detect, by the first movable object, the position of a target. The method may control the position of a first movable object based on the position of the target. A command may be received for the first movable object to perform an action in coordination with the second movable object. In some embodiments, the command may be to take images of the target to create a bullet time effect.