Abstract: Techniques for three-dimensional measurement and imaging may be provided. Systems may include an integrated instrument package, and remote processors. The instrument package may include video cameras, LiDAR sensors, a GPS receiver, a controller, and a data acquisition and control computer. The instrument package may be configured to synchronize the camera shutters to produce simultaneous images, LiDAR sensor data returned point cloud frames and images, and GPS position readings. The remote processors may be configured to compare the images to create a stereoscopic effect and infer depth of field from parallax, align lidar point cloud frames and images with a spatially and temporally consistent image and video frame, then associate it with GPS position readings, assign relative 3D coordinates to every pixel in an image, transform the relative coordinates into absolute 3D coordinates, and identify objects in the image and infer a GPS position and elevation of the objects.

Abstract: Techniques for three-dimensional measurement and imaging may be provided. Systems may include an integrated instrument package, and remote processors. The instrument package may include video cameras, LiDAR sensors, a GPS receiver, a controller, and a data acquisition and control computer. The instrument package may be configured to synchronize the camera shutters to produce simultaneous images, LiDAR sensor data returned point cloud frames and images, and GPS position readings. The remote processors may be configured to compare the images to create a stereoscopic effect and infer depth of field from parallax, align lidar point cloud frames and images with a spatially and temporally consistent image and video frame, then associate it with GPS position readings, assign relative 3D coordinates to every pixel in an image, transform the relative coordinates into absolute 3D coordinates, and identify objects in the image and infer a GPS position and elevation of the objects.

Abstract: A system and method for creating a digital elevation model, and for reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset may be provided. The system may include one or more processors configured to receive input data, provide the input data to a neural network (NN), and generate a digital elevation model based on the predicted elevations output by the NN. The NN may be configured to include an input layer; a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; and an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data.

Abstract: According to various embodiments, a neural network for performing vertical error regression analysis is disclosed. The neural network includes an input layer including input data related to a target pixel, a target location, a slope at the target location, population density at the target location, tree canopy height at the target location, vegetation density at the target location, and an Ice, Cloud, and Land Satellite (ICESat) differential map at the target location. The neural network further includes a plurality of hidden layers connected to the input layer, where the plurality of hidden layers is configured to iteratively analyze the input data. The neural network also includes an output layer connected to the plurality of hidden layers, where the output layer is configured to output a predicted vertical error based on the analysis of the input data.