Methods, systems and apparatus, including computer programs encoded on computer storage media for determining asset efficiency. Unmanned Aerial Vehicles (UAVs) may be used to obtain aerial images of locations, property or structures. The aerial images may be geo-rectified, and a ortho-mosaic, digital surface model, or a point cloud may be created. In the context of an operation where mobile assets are used, such as construction or earth moving equipment, location-based event information may be obtained. The location-based event information may be used to determine road segment conditions or road topology where problematic road conditions likely exist.

A system and method for determining location and/or orientation of a sensor may include, stored in a database a representation of an element in a first space. A mapping between the representation and input from a first sensor may be created. Using the mapping and input from a second sensor in a second space, one or more elements in the database may be identified. A location and/or orientation of the second sensor in the second space may be determined based on the one or more elements.

A method, apparatus and computer program product are provided for establishing correspondences between images using through generation of a translation between the different perspectives. Methods may include: receiving first sensor data from a first image sensor, where the first sensor data includes a first image of an environment captured from a first perspective; receiving second sensor data from a second image sensor of a second image of the environment captured from a second perspective; identifying image correspondence points between the first sensor data and the second sensor data; computing pairwise vectors between corresponding pairs of first projected points of the first sensor data and second projected points of the second sensor data; clustering the pairwise vectors according to magnitude and orientation; and generating a translation vector from clusters of pairwise vectors, where the translation vector represents a shift of the second image sensor data to correspond to ground truth data.

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.

An apparatus for generating a map includes a processor configured to detect road areas representing roads from a bird's-eye view image, detect skeletal lines of the road areas and detects individual branch points of the skeletal lines as candidates for the centers of intersections, detect candidate intersection areas for the respective candidates for the centers of intersections from the bird's-eye view image, and detect two or more of the candidate intersection areas that at least partially overlap each other as a single intersection area. The candidate intersection areas respectively include the candidates for the centers.

A method including generating, by a navigation service, a route for navigating from a route origin to a route destination using a private roads repository. The method includes identifying a ghost origin and a ghost destination of a ghost road along the route. The method includes sending, using an application programming interface of a base roads engine, a first request for a route from the ghost origin to the ghost destination. The method includes receiving, from the base roads engine in response to the first request, a replacement section from the ghost origin to the ghost destination. The method includes replacing, in the route, the ghost road with the replacement section to create an updated route including segments. The method includes generating an estimated travel time from the route origin to the route destination over the segments of the updated route. The method includes presenting the estimated travel time.

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.

A method, apparatus and computer program product are provided for warping a perspective image into the ground plane using a homography transformation to estimate a bird's eye view in real time. Methods may include: receiving first sensor data from a first vehicle traveling along a road segment in an environment, where the first sensor data includes perspective image data of the environment, and where the first sensor data includes a location and a heading; retrieving a satellite image associated with the location and heading; applying a deep neural network to regress a bird's eye view image from the perspective image data; applying a Generative Adversarial Network (GAN) to the regressed bird's eye view image using the satellite image as a target of the GAN to obtain a stabilized bird's eye view image; and deriving values of a homography matrix between the sensor data and the established bird's eye view image.

Techniques are disclosed for sharing sensor information between multiple vehicles. A system for sharing sensor information between multiple vehicles, can include an aerial vehicle including a first computing device and first scanning sensor, and a ground vehicle including a second computing device and second scanning. The aerial vehicle can use the first scanning sensor to obtain first scanning data and transmit the first scanning data to the second computing device. The ground vehicle can receive the first scanning data from the first computing device, obtain second scanning data from the second scanning sensor, identify an overlapping portion of the first scanning data and the second scanning data based on at least one reference object in the scanning data, and execute a navigation control command based on one or more roadway objects identified in the overlapping portion of the first scanning data and the second scanning data.

An approach is provided for confirming road vector geometry based on aerial image(s). For example, the approach involves retrieving a feature and a vector representation of a road link. The approach also involves processing one or more aerial images depicting the road link to extract a list of spectral pixel values corresponding to the vector representation. The approach further involves determining a degree of misalignment between the spectral pixel values and a spectral signature of the feature of the road link. The approach further involves initiating a confirmation of a geometry of the vector representation based on the degree of misalignment. The approach further involves providing the confirmation as an output.