A method and system including: defining a geographic area; receiving a plurality of images; determining a plurality of image points; partitioning the geographic area into a plurality of image regions based on the plurality of image points; and stitching the plurality of images into a combined image based on the plurality of image regions.

An unmanned aerial vehicle according to an embodiment of the present disclosure includes a storing part configured to store information regarding a geo-fence area, and flight approval information or shooting approval information of a main body of the unmanned aerial vehicle with respect to the geo-fence area; and a controller configured to, when the main body approaches the geo-fence area, extract approval code from the flight approval information or the shooting approval information, and release at least one of a flight mode and a shooting mode prohibited in the geo-fence area.

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.

A game scoring camera system is disclosed for capturing images of game animals for the purpose of scoring the antlers using an accepted scoring method. One or more cameras are used in a multiscopic arrangement for capturing two-dimensional (2-D) images which are then converted to three-dimensional (3-D) data models, the resulting 3-D data models being used for determining measurements of various antler structures for calculating a score for the set of antlers captured in the images, the score being based on existing antler scoring systems. Some embodiments include one or more cameras, each being mounted on an unmanned aerial vehicle or drone, for capturing images during an aerial survey of game animals located within a particular area. Other embodiments include at least two cameras mounted in a stationary configuration for capturing images of game animals located within a particular area.

A system may include a receiver configured to receive sensor data from one or more aerial vehicles, the sensor data including map data including sensed data related to an aerial view from one or more aerial vehicles of terrain and objects on the terrain. The system may also include a map updating processor in communication with the receiver. The map updating processor may receive the map data and identify a geographic location and/or an orientation associated with the map data. The map updating processor may also align, based at least in part on the geographic location and/or the orientation, the map data with a virtual aerial map providing an aerial view of terrain and objects on the terrain. The map updating processor may also incorporate at least a portion of the map data into the virtual aerial map and generate an updated virtual aerial map.

A method for capturing aerial images is carried out using a geographical area using a self-piloted aircraft according to a predetermined flight plan and an image capture apparatus mounted on the aircraft while being movable relative to the aircraft around a rotation axis. The method includes capturing a first series of images during a single flyover of the geographical area by the aircraft by positioning the image capture apparatus iteratively according to a first sequence of angular position(s), and then capturing a second series of images by positioning the image capture apparatus iteratively according to a second sequence of angular position(s) including at least one angular position distinct from each angular position of the first sequence.

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.

In one aspect, a method for generating agricultural treatment prescriptions includes generating a pre-emergence field contour map for a field based on pre-emergence aerial data collected for the field, the pre-emergence field contour map being indicative of the ground surface topology of the field in a pre-emergence condition. The method also includes generating a post-emergence field contour map for the field based on post-emergence aerial data collected for the field, the post-emergence field contour map being indicative of a field topology of the field following plant emergence. In addition, the method includes identifying individual plant heights of the plants based at least in part on a comparison between the pre-emergence field contour map and the post-emergence field contour map, and determining a treatment prescription for applying one or more agricultural products to the field based at least in part on the individual plant heights.

A highly safe drone is provided. A remote controller and a drone are connected to each other through a network and cooperate to operate. The drone includes a flight control unit, a flight start command reception unit receiving a flight start command from a user, a drone determination unit determining a configuration of the drone itself, an external environment determination unit determining an external environment of the drone. The drone system has a plurality of states including a takeoff diagnosis state and satisfies a condition transitioning to another state. The takeoff diagnosis state includes a drone determination state where the drone determination unit determines the configuration of the drone itself and an external environment determination state where the external environment determination unit determines the external environment. The drone system makes the drone to takeoff after transitioning to the takeoff diagnosis state upon receiving the flight start command.

A mapping and control system for an aerial vehicle, the system including a payload attachable to the aerial vehicle, the payload including: a range sensor that generates range data indicative of a range to an environment; a memory for storing flight plan data indicative of a desired flight plan; a communications interface; and one or more processing devices that: use the range data to generate pose data indicative of position and orientation of the payload relative to the environment; use the pose data and the flight plan data to identify manoeuvres; generate control instructions; and transfer the control instructions to a vehicle control system of the aerial vehicle via the communications interface, to cause the aerial vehicle to implement the manoeuvres and thereby fly autonomously in accordance with the desired flight plan, wherein the range data is further for use in generating a map of the environment.