Systems and methods for identifying inclined regions are provided. In one aspect, a method is provided that includes receiving shadow data for at least one first ground object in a first region, wherein each first ground object is depicted in one overhead image of the first region, wherein the shadow data comprises a length of the respective first ground object as identified from the respective overhead image; receiving shadow data for at least one second comparable ground object in a second region, wherein each second ground object is depicted in one overhead image of the second region, wherein the shadow data comprises a length of the respective second ground object as identified from the respective overhead image; calculating a statistical measure describing the variability of the shadow lengths between objects in the first region and the second region; comparing the statistical measure to a predetermined threshold; and based on the comparison, identifying that the first region is inclined relative to the second region.

A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.

A client device receives a first map tile, a second map tile, and map terrain data from a mapping system, the first and second map tiles together including map feature having a geometric base with a height value, the geometric base represented by a set of vertices split across the first and second map tiles. The client device identifies edges of the geometric base that intersect a tile border between the first and second map tiles. The client device determines a set of sample points based on the identified edges and determines a particular sample elevation value corresponding to a sample point in the set. The client device renders the map feature based on the particular sample elevation value and displays the rendering of the map feature.

Radar, lidar, and other active 3D imaging techniques require large, heavy sensors that consume lots of power. Passive 3D imaging techniques based on feature matching are computationally expensive and limited by the quality of the feature matching. Fortunately, there is a robust, computationally inexpensive way to generate 3D images from full-motion video acquired from a platform that moves relative to the scene. The full-motion video frames are registered to each other and mapped to the scene coordinates using data about the trajectory of the platform with respect to the scene. The time derivative of the registered frames equals the product of the height map of the scene, the projected angular velocity of the platform, and the spatial gradient of the registered frames. This relationship can be solved in (near) real time to produce the height map of the scene from the full-motion video and the trajectory.

A method of registering geological data at a formation core tracking system includes, at the tracking system, registering a formation core provided within a field of view of an optical imaging system of the tracking system; tracking the orientation of the formation core relative to the tracking system and the distance of the formation core relative to the tracking system; obtaining data associated with a first section of the formation core which is located at a predetermined distance from the tracking system, displaying the data together with an image of the formation core such that an augmented reality image is provided on a display device of the tracking system, changing the distance between the tracking system and the core; and updating the displayed data by obtaining data associated with a second section of the formation core which is located at said predetermined distance from the tracking system.

Systems, devices, methods, and computer-readable media for determining planarity in a 3D data set are provided. A method can include receiving or retrieving three-dimensional (3D) data of a geographical region, dividing the 3D data into first contiguous regions of specified first geographical dimensions, determining, for each first contiguous region of the first contiguous regions, respective measures of variation, identifying, based on the respective measures of variation, a search radius, dividing the 3D data into respective second contiguous or overlapping regions with dimensions the size of the identified search radius, and determining, based on the identified search radius, a planarity of each of the respective second contiguous or overlapping regions.

The present invention relates to a rock classification method wherein at least one image (IMA) of the rock to be classified is acquired, and wherein a decision tree (ARB) classifying the rocks according to several descriptors is used, as well as a machine learning method (APP) from a rock image database (BIR). Machine learning is applied for each descriptor considered.

A method includes generating a set of sub-images of a subsurface formation based on measurement values acquired by a plurality of sensors corresponding to one or more signals that have propagated through the subsurface formation, wherein each of the set of sub-images correspond to one of the plurality of sensors. The plurality of sensors are on a tool in a borehole, wherein each of the plurality of sensors are at different spatial positions with respect to each other. The method also includes generating a combined image by aligning the set of sub-images based on the measurement values, wherein the aligning of the set of sub-images is independent of acceleration of the tool during tool motion.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s). Replacement pixel data may be calculated based on the unobscured pixels and used to generate a transient-obstruction-free version of the digital image.

A prediction device 100 of the present invention includes a detection means 121 for, on the basis of a river image that is an image obtained by capturing a river and associated with capturing position information representing a position where the river is captured, detecting river condition information representing a condition of the river at the position where the river image is captured; and a prediction means 122 for, on the basis of the capturing position information, the river condition information, and topography information representing the topography of the river, predicting a river condition representing a condition of the river at a given point of the river. The given point is different from the position represented by the capturing position information.