Abstract: This patent discloses innovative technologies for efficient and scalable collection, processing, and analysis of geospatial data. The proposed solutions encompass advanced hardware components, such as sensors, cameras, vehicles, satellites, LiDAR systems, and GPS devices, enabling high-precision data acquisition. The invention also introduces sophisticated software algorithms and techniques for processing and analyzing large volumes of geospatial data, including data fusion, feature extraction, image processing, and machine learning approaches. Additionally, the patent addresses the challenges of scalability and efficiency by optimizing data acquisition workflows, reducing processing time and resource requirements, and improving the accuracy and reliability of collected data.

Abstract: Embodiments of architecture, systems, and methods used to provide map data, sensor data, and asset signature data including location data, depth data, and positional data for a terrestrially mobile entity, location and positional data for pseudo-fixed assets and dynamic assets relative to the terrestrially mobile entity via a combination of aerial sensor data and terrestrial data. Other embodiments may be described and claimed.

Abstract: A method of augmenting camera devices with neural networks to control framerate, time synchronization, image stitching and resolution, the method comprising: creating synthetic camera frames by using neural networks to interpolate between actual frame captures; utilizing frame interpolation to align camera images when the frames are misaligned in time, with additional hardware; retroactively processing recorded sensor data to achieve time synchronization from multiple camera sensors; stitching temporally misaligned camera recordings together to create spherical or panoramic images with vision pipelines augmented by neural networks; and enhance images by utilizing neural networks to adjust optimize resolution.