According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a 3D building modeling module; a memory for storing program instructions; a 3D building modeling processor, coupled to the memory, and in communication with the 3D building modeling module and operative to execute program instructions to: receive a region of interest; receive an image of the region of image from a data source; generate a surface model based on the received image including one or more buildings; generate a digital height model; decompose each building into a set of shapes; apply a correction process to the set of shapes; execute a primitive classification process to each shape; execute a fitting process to each classified shape; select a best fitting model; and generate a 3D model of each building. Numerous other aspects are provided.

Computer-based method and system for urban planning of a facility are described herein. The method and system facilitate a user, without any hard knowledge of geographic information system (GIS) to perform quick geo-spatial analysis with a fully automated, single step, and single input process including automatically determining one or more criteria corresponding to the facility based on the facility type defined by the user, automatically performing data analysis on a plurality of data-sets, automatically determining and presenting at least one suitable site within the geographic area for the facility based on the data analysis.

A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

Systems and methods for creating a digital twin of an infrastructure component. The digital twin is a computerized, three-dimensional model of the component, typically a pipe, created after manufacture but before installation. The digital twin can be saved on a computer-readable storage medium for later retrieval, and can be loaded into three-dimensional modeling software for manipulation and viewing from various angles and perspectives. The twin is created from a plurality of imaging systems capturing different surfaces or different aspects, whose measurements are mapped to a uniform coordinate system to generate a three-dimensional model. Other data may also be added to or stored with the digital twin, such as manufacturing specifications, photographic data, and current or historical inspection data. The digital twin may be viewed on a mobile device programmed to receive, display, and allow the user to view and manipulate the digital twin.

Systems and methods for generating ground-level terrain elevation models, preparing vector street data to assist in generating such models, and finding approximate elevation of any point using such terrain models are provided. Lidar data can be analyzed, and Lidar elevation values at roadway/street intersections can be used to determine a model of the ground-level elevation in an area or region. Outliers can be removed. The ground-level elevation at any point in the mapped area can be determined using elevation levels for nearby roadway intersections.

An example method performed by a processing system includes obtaining a light detecting and ranging point cloud of a building, where the point cloud includes a plurality of points, and where each point is associated with a set of (x,y,z) coordinates. A first point of the plurality of points is assigned to a subset of the plurality of points that is associated with the building, where the subset includes points whose (x,y) coordinates fall within a footprint of the building. The first point is grouped into a first cluster according to at least one of: a (z) coordinate of the first point and a gradient to which the first point belongs. A first prism formed by the first cluster is constructed. A model of the building is stored as a plurality of connected prisms, where the plurality of connected prisms includes the first prism.

Systems and methods of the present disclosure include receiving an aerial image that includes a view of property (e.g., real property, personal property, or other types of property), automatically identifying the property included in the aerial image, automatically determining one or more characteristics of the property based at least in part on the aerial image, and automatically adjusting an insurance policy term for an insurance policy relating to the property based at least in part on the one or more characteristics of the property. Certain embodiments include automatically determining the one or more characteristics of the property also based at least in part on data received from one or more smart home devices associated with the property, one or more public records relating to the property, a supplemental image accessed from a camera located in or around the property, and so forth.

A computer-implemented method for generating labeled training data for an artificial intelligence machine is provided. The method comprises at least the steps of: defining parameters for a scenario of an aircraft approaching a runway; using the parameters of the scenario in a flight simulator to generate simulated flight data, the flight simulator being configured so as to simulate the aircraft in the approach phase and an associated autopilot; using the simulated flight data in a sensor simulator to generate simulated sensor data, the sensor simulator being configured so as to simulate a forward-facing sensor on board an aircraft and able to provide sensor data representative of information of interest of a runway, the simulated sensor data that are generated being representative of information of interest of the runway; and using the simulated flight data and the simulated sensor data to generate a ground truth, the ground truth associated with the simulated sensor data forming a pair of simulated labeled training data.

Neural stimulation is provided according to a closed loop algorithm to treat sleep disordered breathing (SOB), including obstructive sleep apnea (OSA). The closed loop algorithm is executed by a system comprising a processor (which can be within the neural stimulator). The closed loop algorithm includes monitoring physiological data (e.g., EMG data) recorded by a sensor implanted adjacent to an anterior lingual muscle; identifying a trigger within the physiological data, wherein the trigger is identified as a biomarker for a condition related to sleep (e.g., inspiration); and applying a rule-based classification (which can learn) to the trigger to determine whether one or more parameters of a stimulation should be altered based on the biomarker.

A method for analyzing one or more conditions of a transportation pathway includes obtaining, using an imaging device of an inspection system, image data reproducible as a plurality of images of the transportation pathway, each of the plurality of images being reproducible as an image of a portion of the transportation pathway, each portion of the transportation pathway having an associated location along a length of the transportation pathway, analyzing, using one or more processors of the inspection system, the image data to determine a first plurality of metrics indicative of a condition of the transportation pathway at each of the associated locations, and generating a first graph, using the determined first plurality of metrics, that is indicative of the condition of the transportation pathway at each of the associated locations.