Abstract: A geospatial modeling system may include a memory and a processor cooperating therewith to: (a) generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images; (b) select an isolated geospatial image from among the plurality of geospatial images; (c) determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the isolated geospatial image; (d) align the isolated geospatial image and the reference geospatial image to generate a predictively registered image; (e) update the 3D geospatial model based upon the predictively registered image; and (f) iteratively repeat (b)-(e) for successive isolated geospatial images.

Abstract: A geospatial modeling system may include a memory and a processor cooperating therewith to generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images, obtain a newly collected geospatial image, and determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the newly collected geospatial image. The processor may further align the newly collected geospatial image and the reference geospatial image to generate a predictively registered image, and update the 3D geospatial model based upon the predictively registered image.

Abstract: An artificial intelligence (AI) system for geospatial height estimation may include a memory and a processor cooperating therewith to store a plurality of labeled predicted electro-optic (EO) image classified objects having respective elevation values associated therewith in a semantic label database, and train a model using trained EO imagery and the semantic label database. The processor may further estimate height values within new EO imagery for a geographic area based upon the trained model, and generate an estimated height map for the geographic area from the estimated height values and output the estimated height map on a display.

Abstract: A geospatial modeling system may include a memory and a processor cooperating therewith to generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images, obtain a newly collected geospatial image, and determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the newly collected geospatial image. The processor may further align the newly collected geospatial image and the reference geospatial image to generate a predictively registered image, and update the 3D geospatial model based upon the predictively registered image.

Abstract: A geospatial modeling system may include a memory and a processor cooperating therewith to: (a) generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images; (b) select an isolated geospatial image from among the plurality of geospatial images; (c) determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the isolated geospatial image; (d) align the isolated geospatial image and the reference geospatial image to generate a predictively registered image; (e) update the 3D geospatial model based upon the predictively registered image; and (f) iteratively repeat (b)-(e) for successive isolated geospatial images.

Abstract: An artificial intelligence (AI) system for generating a digital surface model (DSM) may include a memory and a processor cooperating therewith to determine an estimated height map from electro-optic (EO) imagery of a geographic area using artificial intelligence. The processor may further generate cost coefficients for a three-dimensional (3D) cost cube based upon stereo-geographic image data and height value seeding using the estimated height map, and generate a DSM for the geographic area based upon the 3D cost cube and outputting the DSM to a display.

Abstract: An artificial intelligence (AI) system for geospatial height estimation may include a memory and a processor cooperating therewith to store a plurality of labeled predicted electro-optic (EO) image classified objects having respective elevation values associated therewith in a semantic label database, and train a model using trained EO imagery and the semantic label database. The processor may further estimate height values within new EO imagery for a geographic area based upon the trained model, and generate an estimated height map for the geographic area from the estimated height values and output the estimated height map on a display.

Abstract: An artificial intelligence (AI) system for generating a digital surface model (DSM) may include a memory and a processor cooperating therewith to determine an estimated height map from electro-optic (EO) imagery of a geographic area using artificial intelligence. The processor may further generate cost coefficients for a three-dimensional (3D) cost cube based upon stereo-geographic image data and height value seeding using the estimated height map, and generate a DSM for the geographic area based upon the 3D cost cube and outputting the DSM to a display.

Abstract: A system may include a memory and a processor cooperating therewith to obtain geospatial image data from a plurality of different types of sensors and generate a three-dimensional (3D) geospatial model therefrom. The processor may further determine a reference image within the 3D geospatial model based upon synthetically positioning an image sensor within the 3D geospatial model, and perform change detection between a collected image and the reference image based upon semantic change detection using deep learning.

Abstract: A system is for monitoring marine vessels that generate automatic identification system (AIS) message data. The system may include a memory and a processor cooperating therewith to train a maritime scenario topic model using training textual data having known corresponding maritime scenarios associated therewith. The training textual data may include AIS message data. The processor operate the trained maritime scenario topic model on new textual data having unknown corresponding maritime scenarios to determine probabilistic maritime scenarios corresponding to the new textual data, where the new textual data includes new AIS message data. The processor may further generate an alert based upon the probabilistic maritime scenarios.

Abstract: A system for monitoring marine vessels using a satellite network may include a plurality of satellite payloads to be carried by respective satellites. Each satellite payload may include a communications interface configured to communicate via the satellite network, a radio frequency (RF) transceiver configured to communicate with the plurality of marine vessels, and a controller cooperating with the communications interface and the RF transceiver. The controller may obtain automatic identification system (AIS) data including vessel position and call sign information via the transceiver, and communicate the AIS system data via the communications interface. The system may further include a terrestrial station configured to receive the AIS system data and determine therefrom a rendezvous between marine vessels which pass within a threshold distance for greater than a threshold time.

Abstract: A system for monitoring marine vessels using a satellite network may include a plurality of satellite payloads to be carried by respective satellites. Each satellite payload may include a communications interface configured to communicate via the satellite network, a radio frequency (RF) transceiver configured to communicate with the plurality of marine vessels, and a controller cooperating with the communications interface and the RF transceiver. The controller may obtain automatic identification system (AIS) data including vessel position and call sign information via the RF transceiver, and communicate the AIS system data via the communications interface. The system may further include a terrestrial station configured to receive the AIS data, process aerial marine imagery using a fractal algorithm to assign respective vessel classifications to a plurality of marine vessels, and correlate the vessel classifications with AIS data.

Abstract: A system for monitoring marine vessels using a satellite network may include a plurality of satellite payloads to be carried by respective satellites. Each satellite payload may include a communications interface configured to communicate via the satellite network, a radio frequency (RF) transceiver configured to communicate with the plurality of marine vessels, and a controller cooperating with the communications interface and the RF transceiver. The controller may be configured to obtain automatic identification system (AIS) data including vessel position and call sign information via the RF transceiver, and communicate the AIS system data via the communications interface. The system may further include a terrestrial station configured to receive the AIS system data, receive social media data, and determine expected passenger lists for the marine vessels based upon the AIS data and social media data.

Abstract: A system for monitoring marine vessels using a satellite network may include a plurality of satellite payloads to be carried by respective satellites. Each satellite payload may include a communications interface configured to communicate via the satellite network, a radio frequency (RF) transceiver configured to communicate with the plurality of marine vessels, and a controller cooperating with the communications interface and the RF transceiver. The controller may obtain automatic identification system (AIS) data including vessel position and call sign information via the RF transceiver, and communicate the AIS system data via the communications interface. The system may further include a terrestrial station configured to receive the AIS data, process aerial marine imagery using a fractal algorithm to assign respective vessel classifications to a plurality of marine vessels, and correlate the vessel classifications with AIS data.

Abstract: A system for monitoring marine vessels using a satellite network may include a plurality of satellite payloads to be carried by respective satellites. Each satellite payload may include a communications interface configured to communicate via the satellite network, a radio frequency (RF) transceiver configured to communicate with the plurality of marine vessels, and a controller cooperating with the communications interface and the RF transceiver. The controller may obtain automatic identification system (AIS) data including vessel position and call sign information via the transceiver, and communicate the AIS system data via the communications interface. The system may further include a terrestrial station configured to receive the AIS system data and determine therefrom a rendezvous between marine vessels which pass within a threshold distance for greater than a threshold time.

Abstract: A system for monitoring marine vessels using a satellite network may include a plurality of satellite payloads to be carried by respective satellites. Each satellite payload may include a communications interface configured to communicate via the satellite network, a radio frequency (RF) transceiver configured to communicate with the plurality of marine vessels, and a controller cooperating with the communications interface and the RF transceiver. The controller may be configured to obtain automatic identification system (AIS) data including vessel position and call sign information via the RF transceiver, and communicate the AIS system data via the communications interface. The system may further include a terrestrial station configured to receive the AIS system data, receive social media data, and determine expected passenger lists for the marine vessels based upon the AIS data and social media data.

Abstract: Systems and/or methods are disclosed herein to identify a user interacting with a content presentation system, adaptively learn, in a passive manner, a user's pattern of accessing content over time, and globally store the user's identifying and use information. Specifically, systems and/or methods for creating a preferred channel list, including determining an identity of a user interacting with a content presentation system, collecting channel use information for each of a plurality of channels from the user's interaction with the content presentation system, storing the channel use information, determining a value for each of the plural channels as a function of the channel use information, and creating a preferred channel list for the plural channels as a function of the determined value.

Abstract: Systems and/or methods are disclosed herein to identify a user interacting with a content presentation system, adaptively learn, in a passive manner, a user's pattern of accessing content over time, and globally store the user's identifying and use information. Specifically, disclosed are systems and/or methods for creating a smart electronic programming guide, including determining an identity of a user interacting with a content presentation system, collecting channel use information for each of a plurality of channels from the user's interaction with the content presentation system, storing the channel use information, determining a value for each of the plural channels as a function of the channel use information, and creating a smart electronic programming guide for the plural channels as a function of the determined value.

Abstract: A system to route media information content may include a router that analyzes predetermined content of a plurality of data packets of the media information content and prioritizes forwarding the plurality of data packets from the router based on applying at least one rule to the predetermined content.

Abstract: Systems and/or methods are disclosed herein to identify a user interacting with a content presentation system, adaptively learn, in a passive manner, a user's pattern of accessing content over time, and globally store the user's identifying and use information. Specifically, disclosed are systems and/or methods for creating a smart channel tuner list, including determining an identity of a user interacting with a content presentation system, collecting channel switching information from the first channel to each of a plurality of second channels from the user's interaction with the content presentation system, determining a value for each of the plural second channels as a function of the channel switching information, and creating a smart channel tuner list as a function of the determined value.