A method and system may survey a property using aerial images captured from an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV) or from a satellite device. The method may include identifying a commercial property for a UAV to perform surveillance, and directing the UAV to hover over the commercial property and capture aerial images at predetermined time intervals. Furthermore, the method may include receiving the aerial images of the commercial property captured at the predetermined time intervals, detecting a surveillance event at the commercial property, generating a surveillance alert, and transmitting the surveillance alert to an electronic device associated with an owner of the commercial property.

A method for discovering a newly added road, includes: determining trajectory data in a target area within a preset time period, in which the trajectory data includes a plurality of trajectories and attribute information of each trajectory point in respective trajectories; determining trajectory features of a plurality of grids in the target area, according to the plurality of trajectories, the attribute information of each trajectory point in respective trajectories, and position features of the plurality of grids; generating current grid portrait data of the target area, according to the position features and trajectory features of the plurality of grids; and determining newly added road information of the target area, according to the current grid portrait data and historical grid portrait data of the target area.

In embodiments the invention provides methods and systems for improved monitoring of roadways and related resources. The methods employ frugal devices such as remote webcams, and provide methods for improving the quality and usefulness of the data obtained from such devices.

This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

A system includes a mobile platform that moves under remote and/or autonomous control, a sensor package supported by the mobile platform that obtains information relating to a component of a transportation network, and one or more processors that receive the sensor information and analyze the information in combination with other information that is not obtained from the sensor package. The processors also generate an output that displays information relating to one or more of a status, a condition, and/or a state of health of the component of the transportation network; initiates an action to change an operational state of the component; identifies a hazard to one or more vehicles traveling within the transportation network; and/or collects the information relating to the component. Optionally, the component is not communicatively coupled to an information network and the mobile platform provides the information obtained by the sensor package to the information network.

A method and system may assess the damage to infrastructure using aerial images captured from an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV) or from a satellite device. Specifically, an item of infrastructure may be identified for assessing damage. The UAV, MAV, or satellite device may then capture aerial images within an area which surrounds the identified infrastructure item. Subsequently, the aerial images may be analyzed to determine a condition and the extent and/or severity of the damage to the infrastructure item. Furthermore, the aerial images along with indications of the extent of the damage may be displayed on a computing device, where one indication of the severity of the damage surrounds another indication of the severity of the damage on the display.

According to an exemplary embodiment of the present disclosure, a method of detecting an object is disclosed. The method of detecting an object includes computing an image including an object by using an object detection model including a local block and a non-local block, in which the local block computes a relationship between adjacent pixels included in a feature map, and the non-local block computes a relationship between non-adjacent pixels included in the feature map.

The disclosure provides methods, apparatus, and products for attention guidance and labeling. In one aspect, a method comprises receiving metadata associated with a perspective image captured by an image capture device located at the image position and having the image pose, wherein the metadata comprises the image position and the image pose. The method comprises defining a field-of-view indicator having a first indicator position located at the image position and having the image pose; receiving ground control point (GCP) information identifying a GCP, wherein the GCP information comprises a GCP position; defining a GCP indicator having a second indicator position located at the GCP position; and causing display of an indicator layer comprising the field-of-view indicator and the GCP indicator in a second portion of an interactive user interface (IUI) of a labeling tool, wherein at least the perspective image is displayed in a first portion of the IUI.

A system includes an aerially mounted video capture device and a processor. The processor is operable to direct the video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, which substantially surround their respective objects of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. Each danger zone represents a distance threshold about a respective object of interest. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for two or more objects of interest, and to generate an alert at least partially in response to the near-miss condition.

For automated detection and tracking of multiple targets, an apparatus, method, and program product are disclosed. The apparatus includes a camera that captures video data and a processor that compensates for camera motion in the video data, processes the compensated video data to remove noise and spurious returns, detects one or more targets within the processed video data, and identifies target information for each target in the processed video data.