An absolute coordinate acquisition method is an absolute coordinate acquisition method for acquiring absolute coordinates of an underground pipeline, the method including: a marking step S101 of setting a plurality of reference points on a road surface and setting a measurement point on a pipeline installed inside an excavated ditch; a measurement step S102 of measuring absolute coordinates of the reference points; a relative position calculation step (S103 to S105) of calculating relative positions of the reference points and the measurement point to a reference position; and an absolute coordinate calculation step S106 of calculating absolute coordinates of the measurement point based on the absolute coordinates of the reference points and the relative positions of the reference points and the measurement point.

An absolute coordinate acquisition method is an absolute coordinate acquisition method for acquiring absolute coordinates of an underground pipeline, the method including: a marking step S101 of setting a plurality of reference points on a road surface and setting a measurement point on a pipeline installed inside an excavated ditch; a measurement step S102 of measuring absolute coordinates of the reference points; a relative position calculation step (S103 to S105) of calculating relative positions of the reference points and the measurement point to a reference position; and an absolute coordinate calculation step S106 of calculating absolute coordinates of the measurement point based on the absolute coordinates of the reference points and the relative positions of the reference points and the measurement point.

A system for monitoring survey reflectors arranged at a plurality of locations on an object, having: a camera, including: one or more light sources arranged to illuminate a field in space corresponding to at least 10% of a field of view of the camera, preferably the whole field of view; an image sensor receiving light beams from reflections of the beam by the survey reflectors and providing data; a body with an optical entry system, the image sensor located on a first side and the light source on a second side of the body; and a processing unit processing the data.

The processing unit is configured to determine locations of the survey reflectors from the image sensor data and detect movement of the survey reflectors based on a comparison of the determined locations with previously determined locations.

A feature mapping computer system configured to (i) receive a localized image including a photo depicting a driving environment and location data associated with the photo, (ii) identify, using an image recognition module, a roadway feature depicted in the photo, (iii) generate, using a photogrammetry module, a point cloud based upon the photo and the location data, wherein the point cloud comprises a set of data points representing the driving environment in a three dimensional (“3D”) space, (iv) localize the point cloud by assigning a location to the point cloud based upon the location data, and (v) generate an enhanced base map that includes a roadway feature.

A feature mapping computer system configured to (i) receive a localized image including a photo depicting a driving environment and location data associated with the photo, (ii) identify, using an image recognition module, a roadway feature depicted in the photo, (iii) generate, using a photogrammetry module, a point cloud based upon the photo and the location data, wherein the point cloud comprises a set of data points representing the driving environment in a three dimensional (“3D”) space, (iv) localize the point cloud by assigning a location to the point cloud based upon the location data, and (v) generate an enhanced base map that includes a roadway feature.

Examples disclosed herein may involve (i) obtaining a first layer of map data associated with sensor data capturing a geographical area, the first layer of map data comprising an aggregated overhead-view image of the geographical area, where the aggregated overhead-view image is generated from aggregated pixel values from a plurality of images associated with the geographical area, (ii) obtaining a second layer of map data, the second layer of map data comprising label data for the geographical area derived from the aggregated overhead-view image of the geographical area, and (iii) causing the first layer of map data and the second layer of map data to be presented to a user for curation of the label data.

Examples disclosed herein may involve (i) obtaining a first layer of map data associated with sensor data capturing a geographical area, the first layer of map data comprising an aggregated overhead-view image of the geographical area, where the aggregated overhead-view image is generated from aggregated pixel values from a plurality of images associated with the geographical area, (ii) obtaining a second layer of map data, the second layer of map data comprising label data for the geographical area derived from the aggregated overhead-view image of the geographical area, and (iii) causing the first layer of map data and the second layer of map data to be presented to a user for curation of the label data.

A technique is directed to methods and systems for creating ground control points (GCPs). In some implementations, a GCP system can analyze satellite data for a location, at which multiple photons were measured, and extract terrain and canopy information from the satellite data. The GCP system can create GCPs with the extracted ICESat-2 data and calculate a quality indicator, which indicates a metric of reliability, for each GCP using photon information. In some implementations, the GCP system classifies and filters the GCPs based building footprint data, water data, forest index data, and/or landform classification mask data.

A technique is directed to methods and systems for creating ground control points (GCPs). In some implementations, a GCP system can analyze satellite data for a location, at which multiple photons were measured, and extract terrain and canopy information from the satellite data. The GCP system can create GCPs with the extracted ICESat-2 data and calculate a quality indicator, which indicates a metric of reliability, for each GCP using photon information. In some implementations, the GCP system classifies and filters the GCPs based building footprint data, water data, forest index data, and/or landform classification mask data.

One aspect of the invention relates to a fully automatic method for calculating the current, geo-referenced position and alignment of a terrestrial scan-surveying device in situ on the basis of a current panoramic image recorded by the surveying device and at least one stored, geo-referenced 3D scan panoramic image.