A method and a system for object detection over water are provided. The method includes acquiring, from a storage system, image data associated with a target area. The image data includes radiance of a plurality of pixels in a first spectral band and in a second spectral band. The method also includes determining a metric corresponding to a pixel of the plurality of pixels as a function of the radiance in the first spectral band and the radiance in the second spectral band and detecting an object in the target area in response to a determination that the metric satisfies a criteria.

An analytical recognition system includes a video camera, an antenna, and a data analytics module. The video camera is configured to capture video data. In some embodiments, the video camera is at least one of a traffic camera or an aerial drone camera. The antenna is configured to capture mobile communication device data. The data analytics module is configured to correlate the video data and the mobile communication device data to generate a profile of a person associated with the video data and the mobile communication device data. The profile has profile data including any one or a combination of the captured video data and the captured mobile communication device data.

An analytical recognition system includes a video camera, an antenna, and a data analytics module. The video camera is configured to capture video data. In some embodiments, the video camera is at least one of a traffic camera or an aerial drone camera. The antenna is configured to capture mobile communication device data. The data analytics module is configured to correlate the video data and the mobile communication device data to generate a profile of a person associated with the video data and the mobile communication device data. The profile has profile data including any one or a combination of the captured video data and the captured mobile communication device data.

Computing devices and methods for reading gauges are disclosed. A gauge reading method includes capturing image data corresponding to a captured image of one or more gauges, detecting one or more gauges in the captured image, cropping a detected gauge in the captured image to provide a use image including the detected gauge, and classifying the detected gauge to correlate the detected gauge with a template image. The gauge reading method also includes attempting to perform feature detection rectification on the use image to produce a rectified image of the detected gauge, performing template matching rectification on the use image to produce the rectified image responsive to a failure to perform the feature detection rectification, and estimating a gauge reading responsive to the rectified image. A computing device may implement at least a portion of a gauge reading method.

Computing devices and methods for reading gauges are disclosed. A gauge reading method includes capturing image data corresponding to a captured image of one or more gauges, detecting one or more gauges in the captured image, cropping a detected gauge in the captured image to provide a use image including the detected gauge, and classifying the detected gauge to correlate the detected gauge with a template image. The gauge reading method also includes attempting to perform feature detection rectification on the use image to produce a rectified image of the detected gauge, performing template matching rectification on the use image to produce the rectified image responsive to a failure to perform the feature detection rectification, and estimating a gauge reading responsive to the rectified image. A computing device may implement at least a portion of a gauge reading method.

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.

Systems and methods are described to systems and methods for training a machine learning model to categorize each pixel of an input overhead image using received overhead images, and using a trained machine learning model to determine, for each pixel of input overhead images, to which land use or land cover mapping category each pixel of each overhead image belongs. The provided systems and methods may generate a map of a geographic area associated with the plurality of overhead images based on the plurality of overhead images and on the determined categories.

Systems and methods are described to systems and methods for training a machine learning model to categorize each pixel of an input overhead image using received overhead images, and using a trained machine learning model to determine, for each pixel of input overhead images, to which land use or land cover mapping category each pixel of each overhead image belongs. The provided systems and methods may generate a map of a geographic area associated with the plurality of overhead images based on the plurality of overhead images and on the determined categories.

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.

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations to accept ecosystem data and detect, analyze, and notify a user about a species in an environment. The system analysis and notification may include impact determination of the species on the environment and shall learn from the received and analyzed data, bringing intelligence to the system.