A method for determining an actual geometry of a track by a track inspection vehicle which is movable on the track, wherein reference points positioned in a lateral environment of the track are automatically recorded by a non-contacting recording system arranged on the track inspection vehicle and their respective actual distance from the track is determined. A three-dimensional trajectory of the track is recorded by an inertial measuring system arranged on the track inspection vehicle, wherein the trajectory is divided by a computing unit into trajectory sections each having a section starting point related to a first reference point and a section end point related to a second reference point, wherein a virtual longitudinal chord is defined for each trajectory section in relation to the assigned reference points, and wherein actual distances between the trajectory and the respectively defined longitudinal chord are calculated for each trajectory section.

A feature extraction system extracts map features from an aerial image. The feature extraction system receives an aerial image having pixels and predicts, for each pixel, a probability that the pixel corresponds to a map feature based on a machine learning model. The machine learning model is trained to determine a probability that a pixel corresponds to the map feature based on a training dataset comprising pairs of aerial images and corresponding mask images that describe known instances of the map feature. The feature extraction system identifies a subset of pixels of the plurality of pixels. Each pixel in the subset has a predicted probability that is greater than or equal to a threshold probability that a pixel corresponds to the map feature. The feature extraction system further determines a bounded geometry enclosing the identified subset of pixels, the bounding geometry encompassing an instance of the map feature.

Various embodiments of the teachings herein include a method for designing a traffic infrastructure on the basis of height data provided by a height monitoring object comprising: a) evaluating the height data detected by a sensor unit of the height monitoring object and generating an evaluation dataset; b) identifying a component of the traffic infrastructure from the evaluation dataset; c) identifying a user of the traffic infrastructure from the evaluation dataset; and d) determining a utilization of the component by the user.

The ground information about the uneven state of the ground is easily detected. A ground information detection method according to the present invention includes: a point group data acquisition step for acquiring point group data generated as three-dimensional coordinates for each point on the ground with laser light emitted from a three-dimensional scanning device installed at a known point; and a ground information detection step for detecting ground information about an uneven state of the ground using the point group data acquired at the point group data acquisition step.

A method for detecting road diseases by intelligent cruise via an unmanned aerial vehicle (UAV), the UAV and a detecting system therefor are provided. The method for detecting road diseases by intelligent cruise via UAV, wherein a road disease detection model and a road recognition model based on deep learning network are built in the UAV, wherein the method specifically comprises a step of: automatically flying the UAV on a predetermined route on the actual road determined by the road recognition model, and obtaining road disease test results by the road surface disease detection model. The present invention adopts the road recognition model and road disease detection model based on deep learning network, which can realize automatic cruise and automatic road disease detection, only need to set a predetermined route or area range, which is convenient and fast.

A system and method for pipeline data acquisition may include a software program that can autonomously review new and legacy videos collected by camera-equipped robotic systems from inside the pipelines, and automatically detect and categorize different features. Three-dimensional (3-D) point clouds may also be generated using software algorithms that stitch together like features in different video frames.

It is possible to estimate a slack level accurately in consideration of a shape of a deformed cable. A point cloud analysis device sets a plurality of regions of interest obtained by window-searching a wire model including a quadratic curve model representing a cable obtained from a point cloud consisting of three-dimensional points on an object, the region of interest being divided into a first region and a second region. The point cloud analysis device compares information on the first region with information on the second region based on the point cloud included in the region of interest and the quadratic curve model for each of the plurality of regions of interest, calculates a degree of division boundary representing a degree to which a division position between the first region and the second region of the plurality of regions of interest is a branch point of the cable, and detects a division boundary point that is a branch point of a cable represented by the quadratic curve model based on the degree of division boundary calculated for each of the plurality of regions of interest.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

In some examples, artificial intelligence based plantable blank spot detection may include generating a plurality of clusters of input images of areas that are to be analyzed for plantable blank spot detection. For each cluster of the plurality of clusters, a model may be identified to analyze corresponding images of a cluster. A model may be selected, from the models identified for the plurality of clusters, to analyze the input images. Canal lines may be identified in the analyzed images. Plantable blank spots may be determined in the analyzed images. An operation of a drone may be controlled to validate the determination of the plantable blank spots.

System includes a controller configured to obtain one or more of a route parameter or a vehicle parameter from discrete examinations of one or more of a route or a vehicle system. The route parameter is indicative of a health of the route over which the vehicle system travels. The vehicle parameter is indicative of a health of the vehicle system. The discrete examinations of the one or more of the route or the vehicle system separated from each other by one or more of location or time. The controller is configured to examine the one or more of the route parameter or the vehicle parameter to determine whether the one or more of the route or the vehicle system is damaged. The system also includes examination equipment configured to continually monitor the one or more of the route or the vehicle system responsive to determining that the one or more of the route or the vehicle is damaged.