An information processing apparatus includes circuitry. The circuitry creates a surface image of the object and a cross-section image of the object. The circuitry receives a creation instruction that instructs to create an image indicating a particular position in an object. The circuitry creates a surface position image indicating the particular position in the surface image and a cross-section position image indicating the particular position in the cross-section image according to the received creation instruction.

A method of producing an image of at least one rock cutting. The method can include forming or obtaining a substrate having a patterned top surface. The method can also include using the patterned top surface of the substrate to support at least one rock cutting, controlling an image acquisition system to acquire at least one image of the rock cutting for storage and subsequent image processing.

A system for integrity monitoring comprises a processor onboard a vehicle, a terrain map database, vision sensors, and inertial sensors. Each vision sensor has an overlapping FOV with at least one other vision sensor. The processor performs integrity monitoring of kinematic states of the vehicle, and comprises a plurality of first level filter sets, each including multiple position PDF filters. A consistency monitor is coupled to each first level filter set. Second level filter sets, each including multiple position PDF filters and a navigation filter, are coupled to the consistency monitor, the map database, the vision sensors, and the inertial sensors. Consistency check modules are each coupled to a respective second level filter set. A third level fusion filter is coupled to each of the consistency check modules. An integrity monitor is coupled to the fusion filter and assures integrity of a final estimate of the vehicle kinematic state statistics.

Methods and systems for estimating a surface runoff based on a pixel scale are disclosed. In some embodiments, the method includes the following steps: (1) calculating a vegetation canopy interception water storage, a litterfall interception water storage, and a soil water storage according to an obtained original remote sensing dataset of a climate element in a study area; (2) calculating a vegetation-soil interception water conservation in the study area based on an established vegetation-soil interception water conservation estimation model according to the vegetation canopy interception water storage, the litterfall interception water storage, the soil water storage, and monthly precipitation; and (3) calculating a surface runoff in the study area based on an established water balance water conservation estimation model according to the monthly precipitation, monthly snowmelt, monthly actual evapotranspiration, and the vegetation-soil interception water conservation in the study area.

System and methods for image gap-filling are provided. An image of a rock formation is obtained from an imaging tool disposed within a borehole. The obtained image is analyzed to identify gaps of missing image data. One or more image masks corresponding to the identified gaps are generated. A machine learning model is trained to produce modeled image data for filling in the missing image data in the identified gaps, based on the generated image mask(s). The image is reconstructed by filling the gaps of missing image data with the modeled image data. The reconstructed image is analyzed to identify geological features of the rock formation.

The invention relates to computer-implemented method for the 3D reconstruction of a ground surface area by stereophotogrammetry, comprising the steps of: determining corrected Rational Polynomial Camera, RPC, models by performing bundle adjustment (BA) of original RPC models each provided with an image of a set of images of the ground surface area acquired by a remote imaging sensor and each associated to a corresponding original projection function ({P.sub.m}) from a 3D object space to a 2D image space, wherein determining the corrected RPC models comprises determining corrected projection functions ({P.sub.m.sup.cor}) from the 3D object space to the 2D image space; and determining (PC) a 3D point cloud representative (3DPC) of the ground surface area by triangulation, based on the corrected RPC models, of stereo correspondences within images of the set of images.

In accordance with the invention, determining the corrected projection functions comprises determining, for each of the original projection function, a 3D corrective rotation around a remote imaging sensor center to be applied in the 3D space before performing the original projection function.

Seismic image data acquired for a subsurface formation from a data acquisition system is input into a deep neural network to generate fault detection data for the subsurface formation comprising probability values at a grid of locations in the subsurface formation. The fault detection data is preprocessed via downsampling and distributed weighted factors and inputted into a generative adversarial network (GAN) upscaling generator to create high resolution fault detection data with minimized distortion and artifacts. The GAN upscaling generator is pre trained on synthetic fault data in a GAN training system using adversarial training against a GAN upscaling discriminator, and both the GAN upscaling generator and the GAN upscaling discriminator learn to approximate the distribution of the synthetic fault data.

The present disclosure provides a method and a system for modelling a poor texture tunnel based on a vision-lidar coupling. The method includes: obtaining point cloud information collected by a depth camera, laser information collected by a lidar, and motion information of an unmanned aerial vehicle (UAV); generating a raster map based on the laser information, and obtaining pose information of the UAV based on the motion information; obtaining a map model through fusing the point cloud information, the raster map, and the pose information by a Bayesian fusion method; and correcting a latest map model by feature matching based on a previous map model.

A ground material density measurement system is disclosed. The ground material density measurement system may receive a moisture measurement associated with an amount of moisture on a ground surface of a section of ground material. The ground material density measurement system may determine a GPR measurement associated with the section of ground material. The ground material density measurement system may process the GPR measurement based on the moisture measurement to account for the amount of moisture. The ground material density measurement system may provide density information associated with the section of ground material based on the processed GPR measurement.

A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.