Systems and methods are described for identifying and validating the routes and characteristics of roads unknown to a road mapping database. The systems and methods may combine feature recognition analysis of aerial images with other information sources such as location tracking information from a mobile device or client in order to improve the accuracy of road information stored within a road mapping database. The systems and methods may also facilitate the collection of additional information regarding the characteristics of the identified roads from a client device or user thereof.

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.

An apparatus for image conversion includes a processor configured to classify a reference region representing a predetermined feature into a shadowed region and an unshadowed region, the reference region being in an aerial image represented in RGB color space; determine a tone correction factor so that a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region in the aerial image represented in predetermined color space to which the color space of the aerial image is converted from RGB color space is less than a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region represented in RGB color space; correct tones of the aerial image with the tone correction factor; and convert the color space of the aerial image from RGB color space to the predetermined color space to generate a color conversion image.

An approach is provided for generating road curvature data. The approach, for example, involves map matching location trace data to a road network, wherein the location trace data is associated with ground truth curvature values. The approach also involves dividing the road network into a plurality of bounded areas (e.g., boxes or other shapes). The approach further involves extracting one or more training features for each bounded area of the plurality of bounded areas from the location trace data map matched to said each bounded area. The approach further involves training a machine learning model based on the one or more training features and ground truth curvature values. The approach further involves providing the trained machine learning model to predict the road curvature data.

Optical fiber network or fiber network is used for transmitting large volumes of data with maximum speed. Fiber to home is a recent technology of the fiber network where the initial fiber rollout cost is more. Hence a proper network management is necessary to rollout the fiber network in an optimized manner. Conventional methods provides construction led approach for fiber network planning and field survey. The present disclosure receives a plurality of geocoded addresses associated with a plurality of users and an average revenue per user. A fiber rollout cost projection is performed based on the input data and a fiber network is generated based on the projected fiber rollout cost. Further, field survey is performed based on the generated network and a fiber network construction design is made. Further, a fiber network rollout is performed based on the fiber network construction design and a redline deviation markup.

A computing system receives an indication of an energy project. The computing system monitors satellite image data of the energy project. The computing system determines that a first activity has been initiated for the energy project based on a change in satellite image data. The computing system monitors telemetry data associated with a location corresponding to the energy project and the satellite image data of the energy project. The computing system converts the telemetry data associated with the location corresponding to the energy project to a telemetry activity index. The computing system converts the satellite image data associated with the energy project to a satellite activity index. The computing system determines that the first activity has ended for the energy project based on changes in at least one of the telemetry activity index and the pad activity index.

According to an aspect, a computer-implemented method for water volume estimation includes detecting water based at least in part on sensor-based data; determining a volume of water based at least in part on the sensor-based data; determining a location at the water for an aircraft to retrieve water via a water retrieving apparatus; and translating the location of the water into pilot inputs to guide the aircraft to the water.

Provided herein is a method for quantifying and measuring human mobility within defined geographic regions and sub-regions. Methods may include: identifying sub-regions within a region; identifying static information associated with the sub-regions from one or more static information sources; obtaining dynamic information associated with the sub-regions from one or more dynamic information sources; determining correlations between elements of the static information associated with a respective sub-region and elements of the dynamic information associated with the respective sub-regions; generating a mobility score for the respective sub-region based, at least in part, on the correlations between the elements of the static information and the elements of the dynamic information associated with the respective sub-region; and providing the mobility score to one or more clients for guiding an action relative to the mobility score.

An embodiment device for detecting a boundary of a road in a 3D point cloud using a cascade classifier includes a rule-based classifier configured to determine whether a received LiDAR cluster has a likelihood of becoming a candidate for the boundary of the road using a box parameter surrounding a point cloud constituting the LiDAR cluster and a point parameter, and a learning-based classifier configured to apply a machine-learning scheme to the LiDAR cluster selected as the candidate for the boundary of the road by the rule-based classifier to determine the LiDAR cluster to be the boundary of the road or an object other than the boundary of the road.

An in-vehicle processing apparatus that executes control of causing a vehicle to perform automated traveling from a predetermined position to a parking space and causing the vehicle to perform automated entry into the parking space, is configured to include a notification control section that causes a portable electronic device carried by a user to display a parking position notification display in which a position of the parking space entered by the vehicle through the automated entry and the predetermined position are superimposed on an image.