A lane extraction method uses projection transformation of a 3D point cloud map, by which the amount of operations required to extract the coordinates of a lane is reduced by performing deep learning and lane extraction in a two-dimensional (2D) domain, and therefore, lane information is obtained in real time. In addition, black-and-white brightness, which is most important information for lane extraction on an image, is substituted by the reflection intensity of a light detection and ranging (LiDAR) sensor so that a deep learning model capable of accurately extracting a lane is provided. Therefore, reliability and competitiveness is enhanced in the field of autonomous driving, the field of road recognition, the field of lane recognition, and the field of HD road maps for autonomous driving, and the fields similar or related thereto, and more particularly, in the fields of road recognition and autonomous driving using LiDAR.

In a computer-implemented method, one or more digital aerial images of a property of a current or potential policyholder may be received. The digital aerial image(s) may be processed to determine one or more features of the property, including one or more features of a tree. A predicted location of roots of the tree is determined based upon the tree feature(s). The property feature(s) is/are analyzed to determine a risk of damage to a structure located on the property, by analyzing at least the predicted location of roots of the tree to determine a risk of damage to a foundation of the structure. Based at least in part on this risk, a risk output is generated that includes an indication of whether action should be taken to mitigate the risk of damage and/or whether insurance coverage should be offered, and/or includes a measure of the risk of damage.

A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.

Disclosed herein is an apparatus for the real-time monitoring of construction objects. The apparatus for the real-time monitoring of construction objects includes: a communication unit configured to receive image data acquired by photographing a construction site, and to transmit safety information to an external device; and a monitoring unit provided with a prediction model pre-trained using binary image sequences of construction objects at the construction site as training data, and configured to detect a plurality of construction objects from image frames included in image data received via the communication unit and convert the detected construction objects into binary images, to generate future frames by inputting the resulting binary images to the prediction model, and to derive proximity between the construction objects by comparing the generated future frames with the resulting binary images and generate the safety information.

Systems and methods for structure footprint detection from oblique imagery are disclosed, including a computer system configured to receive geo-referenced oblique images; analyze pixels of the images to: identify pixels representing a structure with walls; determine ground locations for the walls, geographic locations and orientations of pixels representing vertical edges of the walls, and relative lengths of the walls to produce horizontal line segments representing the base of the walls and having a relative length and an orientation, the horizontal line segment(s) determined from horizontal edge(s) extending a length between vertical edges above the bottoms of the vertical edges such that the horizontal edge is above the base of the structure; and assemble the horizontal line segments based on their relative lengths and orientations to form a footprint of the structure.

Systems and methods for generating and using statistical models to mitigate atmospheric effects in images are described. In some embodiments, a statistical model may be generated by selecting a vegetation type that grows in continuous healthy canopies; identifying a vegetation reference value that is a stable reflectance property of the vegetation type; in a plurality of images, selecting one or more plots of the vegetation type and obtaining top-of-atmosphere reflectance for the plots; selecting discrete areas near the plots and obtaining top-of-atmosphere reflectance for the discrete areas; obtaining image statistics for the discrete areas; and generating a statistical model based on the acquired data.

A system provides descriptor-based feature matching during terrain relative navigation (TRN). A scale and orientation (SO) module acquires a source image, image and slope pixel windows, and ring mask. The SO module combines corresponding pixels from the image pixel window and the slope pixel window to generate intermediate values, accumulates the intermediate values into ring accumulators, sums the accumulated values to generate a final ring value, and determines an orientation stability measure, and final scale and orientation values. An extract descriptors (ED) module acquires the source image, the image and slope pixel windows, final scale and orientation values, sector values, and a rink mask value. The ED module identifies pixels of interest, reorients the sector values. combines corresponding pixels from the image pixel window and the slope pixel window, accumulates and normalizes the intermediate values, and generates an image feature descriptor per coordinate.

A system for efficiently sensing collision threats has an image sensor configured to capture an image of a scene external to a vehicle. The system is configured to then identify an area of the image that is associated with homogeneous sensor values and is thus likely devoid of collision threats. In order to reduce the computational processing required for detecting collision threats, the system culls the identified area from the image, thereby conserving the processing resources of the system.

Methods, systems, and computer-program products used for assessing athletic ability and generating performance data. In one embodiment, athlete performance data is generated through computer-vision analysis of video of an athletic performing, e.g., during practice or gameplay. The generated performance data for the athlete may include, for example, maximum speed, maximum acceleration, time to maximum speed, transition time (e.g., time to change direction), closing speed (e.g., time to close the distance to another athlete), average separation (e.g., between the athlete and another athlete), play-making ability, athleticism (e.g., a weighted computation and/or combination of multiple metrics), and/or other performance data. This performance data may be used to generate and/or update a profile associated with the athlete, which can be utilized for recruiting, scouting, comparing, and/or assessing athletes with greater efficiency and precision.

Aspects of the disclosure provide solutions for determining a position of an object in a video frame. Examples include: receiving a segmentation mask of an identified object in a video frame; adjusting a 3D representation of a moveable part of the object based on constraints for the moveable part; comparing the 3D model of the object to the segmentation mask of the object; determining a match between the 3D model of the object to the segmentation mask of the object is above a threshold; and based on the match being above the threshold, determining a position of the object.