Abstract: A simulation environment is disclosed. In embodiments, the simulation environment includes deep learning neural networks trained to generate photorealistic geotypical image content while preserving spatial coherence. The deep learning networks are trained to correlate geo-specific datasets with input images of increasing detail and resolution to iteratively generate output images until the desired level of detail is reached. The correlation of image input with geo-specific data (e.g., labeled data elements, land use data, biome data, elevational data, near-IR imagery) preserves spatial coherence of objects and image elements between output images, e.g., between adjacent levels of detail and/or between adjacent image tiles sharing a common level of detail.

Abstract: A simulator environment is disclosed. In embodiments, the simulator environment includes graphics generation (GG) processors in communication with one or more display devices. Deep learning neural networks running on the GG processors are configured for run-time generation of photorealistic, geotypical content for display. The DL networks are trained on, and use as input, a combination of image-based input (e.g., imagery relevant to a particular geographical area) and a selection of geo-specific data sources that illustrate specific characteristics of the geographical area. Output images generated by the DL networks include additional data channels corresponding to these geo-specific data characteristics, so the generated images include geotypical representations of land use, elevation, vegetation, and other such characteristics.

Abstract: A simulator environment is disclosed. In embodiments, the simulator environment includes graphics generation (GG) processors in communication with one or more display devices. Deep learning neural networks running on the GG processors are configured for run-time generation of photorealistic, geotypical content for display. The DL networks are trained on, and use as input, a combination of image-based input (e.g., imagery relevant to a particular geographical area) and a selection of geo-specific data sources that illustrate specific characteristics of the geographical area. Output images generated by the DL networks include additional data channels corresponding to these geo-specific data characteristics, so the generated images include geotypical representations of land use, elevation, vegetation, and other such characteristics.

Abstract: A new and useful wireline extensometer is provided that uses a tensioned cable extending between a supply spool and an anchor (e.g. of a type connected to a slope mass), measures movement of the cable to reflect movement of the anchor (e.g. due to movement of the slope mass), and includes a magnetic brake configured to provide a predetermined constant non-frictional braking force on the supply spool. The preferred wireline extensometer is designed to (i) minimize the risk of the extensometer reporting data that suggest slope movement where the slope mass has not moved to an undesirable extent, (ii) measure slope movement at relatively slow rates as well as rapid rates, and to (iii) measure slope mass movements continuously, and in a manner that can be efficiently and effectively communicated to responsible personnel.