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.

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.

A method for in-situ and real-time collection and processing of geometric parameters of railway lines, in a particular but non-limiting manner to those related to the height and stagger of the contact wire in electrified lines and the gauges to specific elements of the infrastructure in any line, generated based on static measurements starting from two-dimensional scenes perpendicular to the track axis, by determining the number of angular positions per scene, determining the minimum number of passes in each position, obtaining the raw coordinates, applying an averaging algorithm, applying offset corrections, transforming coordinates and applying either the steps to salve for height and stagger of the overhead contact line, or applying the steps to salve for gauges to specific elements of the infrastructure. An optimized, efficient and simple method is achieved which enables the real-time management and processing of the data obtained from the railway infrastructure.

A measurement control apparatus (10) according to the present disclosure includes: a detection unit (11) configured to detect an abnormal part of point group data acquired from a three-dimensional optical sensor; a control unit (12) configured to control the orientation of the three-dimensional optical sensor in accordance with the abnormal part detected by the detection unit (11); and a determination unit (13) configured to determine the case of the abnormality of the abnormal part detected by the detection unit (11) based on the point group data measured by the three-dimensional optical sensor in the orientation controlled by the control unit (12).

Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.

A method for optimizing image data for generating orthorectified image(s) related to an area of interest in an environment. The method includes receiving a first image dataset of the area of interest captured therein, identifying each of multiple objects in the area of interest, receiving attribute information related to each of the multiple identified objects, determining if one or more of the multiple identified objects satisfy at least one of a risk criteria based on the attribute information therefor, identifying a maximum relevant second area including at least the area of interest and each of the one or more of the multiple identified objects satisfying the at least one of risk criteria, and processing the first image dataset to either discard or down-sample areas other than the maximum relevant second area captured therein.

An imaging system can include a first and second camera configured to capture first and second sets of oblique images along first and second scan paths, respectively, on an object area. A drive is coupled to a scanning mirror structure, having at least one mirror surface, and configured to rotate the structure about a scan axis based on a scan angle. The first and second cameras each have an optical axis set at an oblique angle to the scan axis and include a respective lens to focus first and second imaging beams reflected from the mirror surface to an image sensor located in each of the cameras. The first and second imaging beams captured by their respective cameras can vary according to the scan angle. Each of the image sensors captures respective sets of oblique images by sampling the imaging beams at first and second values of the scan angle.

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 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.

In an aspect, an image processing apparatus includes a processor and a memory in which a plurality of images obtained by capturing images of a building and a three-dimensional model of the building are stored in association with each other. The processor is configured to perform a development process to perform development of the three-dimensional model into a two-dimensional image, an extraction process to extract defect information of the building on the basis of the plurality of images, a mapping process to perform mapping of the defect information to the two-dimensional image, and an output process to output the two-dimensional image to which the mapping is performed.