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.

An approach is provided for detecting and map-coding a tunnel based on probes and image data. The approach involves, for example, identifying a gap in probe data collected from one or more location sensors of a plurality vehicles. The gap represents a probe gap segment along which at least one probe point of the probe data does not occur or occurs below a threshold number. The approach also involves retrieving image data depicting a geographic area based on location coordinate data associated with the gap. The approach further involves processing the image data to identify one or more end points of a road network depicted in the image data. The approach further involves locating a tunnel start point and/or a tunnel end point based on the one or more endpoints. The approach further involves providing the tunnel start point and/or the tunnel end point as a map data output.

Method and apparatus for generating a road map, electronic device, and non-transitory computer storage medium are disclosed, including: inputting a remote sensing image into a first neural network to extract first road feature information of multiple channels via the first neural network; inputting the first road feature information of multiple channels into a third neural network, to extract third road feature information of multiple channels via the third neural network, where the third neural network is a neural network trained by using road direction information as supervision information; fusing the first road feature information and the third road feature information; and generating a road map according to a fusion result.

Drone systems and methods for package pickup and delivery include, in an air traffic control system configured to manage UAV flight in a geographic region, communicating to one or more UAVs over one or more wireless networks; directing a UAV to pick up the package at the pickup location and to deliver the package to a delivery location; and directing the UAV, in transit, to travel along at least one of a road, highway, and street for a predetermined distance and at a predetermined altitude.

Aspects of the invention include generating a combined raster image from point cloud data and reference data describing an original location of a power line. Selecting a set of candidate pixels from the combined raster image describing an updated location of a power line, wherein the selection is based at least in part on a location of pixels in the combined raster image that describe the original location. Detecting pixels from the set of candidate pixels that describe an updated location of a power line. Modifying the combined raster image to reflect the updated location of the power line.

A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Provided is a method and system that includes an imaging device to be disposed in a lighting fixture to capture images, a remote computing device in communication with the imaging device, and comprising at least one processor. The processor is capable of processing data related to images from the at least one imaging device and from a satellite imagery system, performing comparison operation by detecting a first set of points of interest in an image from the at least one imaging device and corresponding second set of points of interest in an image of a same area from the satellite imagery system, and calculating a homography matrix by matching the first set of points of interests in the image from the at least one imaging device and the second set of points of interest in the image from the satellite imagery system, to determine latitude and longitude coordinates of the image from the imaging device.

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

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 and method obtain first image data of a route at a location of interest from a first optical sensor disposed onboard a vehicle system moving along the route. The first image data depicts the route at the location of interest prior to passage of the vehicle system over the route at the location of interest. Second image data of the route at the location of interest is obtained from a second optical sensor disposed onboard the vehicle system. The second image data depicts the route at the location of interest after passage of the vehicle system over the route at the location of interest. A determination is made as to whether a change in the route has occurred at the location of interest by comparing the first image data with the second image data.