A method comprises receiving an area of interest (AOI) selection. The method further comprises accessing an AOI device location data for the AOI, the AOI device location data indicating locations of devices over time received within the AOI. The AOI device location data is filtered to only include the device location data that match one or more characteristics. A proximity zone is determined for the for the AOI that includes the area of the AOI. A zone device location data for the proximity zone is determined, which indicates locations of devices over time reported within the proximity zone. The method further comprises normalizing the filtered AOI device location data by computing a ratio of the filtered AOI device location data and the zone device location data to generate an AOI user estimate, and transmitting the AOI user estimate to a client device of a requestor.

A feature mapping computer system configured to (i) receive a first localized image including a first photo and a first location; (ii) receive a second localized image including a second photo and a second location; (iii) identify a roadway feature depicted in both the first and second photos; (iv) generate, using a photogrammetry module, a point cloud based upon the first and second photos and first and second locations; (v) generate a localized point cloud by assigning a location to the point cloud based upon at least one of the first and second locations; and (vi) generate an enhanced base map that includes a roadway feature by embedding an indication of the identified roadway feature onto the localized point cloud.

The disclosure presents novel methods to conduct aerial surveying, inspection and measurements with higher accuracy in a fast and easy way, comprising: (1) flying a drone with an accelerometer, gyro, and camera sensors over a target object; (2) capturing a first aerial image at a first position; (3) capturing a second aerial image at a second position, wherein the second position has a horizontal and vertical displacement from the first position; (4) calculating the displacements between the first and second location using a sensor fusion estimation algorithm from the position sensors' data; (5) solving for the pixel depth information of the aerial images by using a single-camera stereophotogrammetry algorithm with the relative altitude and horizontal distance; (6) deriving the ground sample distance (GSD) of each pixel from the calculated depth information; (7) using the image pixel GSD values to compute any geometric properties of the target objects.

The disclosure presents novel methods to conduct aerial surveying, inspection and measurements with higher accuracy in a fast and easy way, comprising: (1) flying a drone with an accelerometer, gyro, and camera sensors over a target object; (2) capturing a first aerial image at a first position; (3) capturing a second aerial image at a second position, wherein the second position has a horizontal and vertical displacement from the first position; (4) calculating the displacements between the first and second location using a sensor fusion estimation algorithm from the position sensors' data; (5) solving for the pixel depth information of the aerial images by using a single-camera stereophotogrammetry algorithm with the relative altitude and horizontal distance; (6) deriving the ground sample distance (GSD) of each pixel from the calculated depth information; (7) using the image pixel GSD values to compute any geometric properties of the target objects.

Sports and fitness applications for an autonomous unmanned aerial vehicle (UAV) are described. In an example embodiment, a UAV can be configured to track a human subject using perception inputs from one or more onboard sensors. The perception inputs can be utilized to generate values for various performance metrics associated with the activity of the human subject. In some embodiments, the perception inputs can be utilized to autonomously maneuver the UAV to lead the human subject to satisfy a performance goal. The UAV can also be configured to autonomously capture images of a sporting event and/or make rule determinations while officiating a sporting event.

Sports and fitness applications for an autonomous unmanned aerial vehicle (UAV) are described. In an example embodiment, a UAV can be configured to track a human subject using perception inputs from one or more onboard sensors. The perception inputs can be utilized to generate values for various performance metrics associated with the activity of the human subject. In some embodiments, the perception inputs can be utilized to autonomously maneuver the UAV to lead the human subject to satisfy a performance goal. The UAV can also be configured to autonomously capture images of a sporting event and/or make rule determinations while officiating a sporting event.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for predicting connections in electric grid models are disclosed. A method includes obtaining geospatial data representing a geographic area that includes an electrical distribution system; and generating, from the geospatial data, asset data that represents characteristics of electrical distribution system assets. The asset data includes: load data representing electrical loads of the electrical distribution system; and node data representing nodes of the electrical distribution system. The method includes processing the asset data using a connection model that is configured to predict electrical connections between assets of the electrical distribution system; and obtaining, from the connection model; output data indicating predicted electrical connections between assets of the electrical distribution system. The geospatial data includes at least one of overhead imagery or street level imagery of the geographic area.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for predicting connections in electric grid models are disclosed. A method includes obtaining geospatial data representing a geographic area that includes an electrical distribution system; and generating, from the geospatial data, asset data that represents characteristics of electrical distribution system assets. The asset data includes: load data representing electrical loads of the electrical distribution system; and node data representing nodes of the electrical distribution system. The method includes processing the asset data using a connection model that is configured to predict electrical connections between assets of the electrical distribution system; and obtaining, from the connection model; output data indicating predicted electrical connections between assets of the electrical distribution system. The geospatial data includes at least one of overhead imagery or street level imagery of the geographic area.

A computer implemented method includes obtaining data for raw image frames captured by a moving camera. The raw image frames are indexed geographically, and a graph is created from the multiple raw image frames. The graph includes image frames as vertices and edges that represent image frames having overlapping image information. The method further includes skipping frames based on the amount of overlap, determining a frame having an interesting feature, using the graph to find additional raw image frames that have the interesting feature, combining multiple raw image frames to form a unique image frame, and transmitting the unique image frame.

An appliance includes a camera for calibrating and determining whether the door of the appliance is in a closed position. A controller is operably coupled to the camera. The controller is configured for obtaining one or more images of the appliance chamber or door. An artificial intelligence image recognition process is used to perform image classification and establish a baseline image to determine whether subsequent closing of the appliance door is successful. In the event of a failure to obtain the baseline image or a determination that the door is not closed, operation of the appliance may be disabled.