Systems and methods for detection and display of marine objects for an aircraft. One example system includes a transceiver configured to communicate with an Automatic Identification System (AIS) server and an electronic controller located within an aircraft. The electronic controller is configured to provide on a display an interface comprising a map representing a travel area. The electronic controller is configured to provide, on the map, a first graphical representation of the aircraft within the travel area. The electronic controller is configured to receive, via the transceiver, marine object data from the AIS server. The electronic controller is configured to periodically update, on the map, a second graphical representation of a first marine object within the travel area based on the marine object data.

A system for proximity sensing on a marine vessel includes a main inertial measurement unit (IMU) positioned on the marine vessel at a main installation attitude and a main location, a first proximity sensor configured to measure proximity of objects from a first sensor location on the marine vessel, and a first sensor IMU positioned on the marine vessel at the first sensor location and at a first installation attitude. A processor is configured to receive main IMU data from the main IMU and first IMU data from the first sensor IMU, wherein the main location of the main IMU on the marine vessel is known and at least one of the first sensor location and the first installation attitude of the first sensor IMU are initially unknown, calibrate the proximity measurements based on the main IMU data and the first IMU data, and output a calibration completion alert.

Various embodiments of the present disclosure provide an augmented reality (AR) vessel maneuvering system and method capable of intuitively and easily setting at least one of: a target position and an attitude of a vessel. The AR vessel maneuvering system includes processing circuitry configured to generate an image including a vessel object representing a vessel in a region corresponding to a viewpoint position and a line-of-sight direction, superimpose and display the image including the vessel object on an outside scene of the region corresponding to the viewpoint position and the line-of-sight direction, detect an operation on the vessel object displayed in the image, and output a command to a navigation device used for navigating the vessel to execute a navigation operation corresponding to the operation on the vessel object. The navigation device is a marine navigation device.

The optimized dynamic scheduling of barges in inland waterways includes geolocating a set of barges positioned on an inland waterway and receiving current data for the inland waterway at each geolocation of each barge in the set. An estimated time of arrival (ETA) for each barge is retrieved from a table at a location for each barge along the inland waterway at which point the barge unloads onboard freight rendering the barge available to receive transport of new freight. Each ETA is modified for each barge according to the received current data at multiple different positions along a route in the inland waterway. Finally, an availability table for the barges in the set is constructed based upon each modified ETA so that barge availability queries received from over a computer communications network can be responded to with availability information stored in the availability table.

An autonomous cruising system is capable of discriminating a port sign and a starboard sign without depending on a position of a ship. The system includes processing circuitry that acquires an image generated by a camera installed in a ship. The processing circuitry identifies whether a mode of a lateral buoy included in the image is either a first mode or a second mode. The processing circuitry determines a country to which a position of the ship detected by a position detector belongs. The processing circuitry determines whether description of a sign of the lateral buoy is the port sign or the starboard sign, from the mode of the lateral buoy and the determined country, based on a given correspondence relationship indicative of whether each of the first mode and the second mode corresponds to either one of the port sign and the starboard sign in each country.

Generative AI systems and methods are provided to produce leading indicators of economic activity based on, for example, agricultural, fishing, mining, lumber harvesting, environmental, or ecological attributes and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Generative AI systems and methods are provided to produce leading indicators of economic activity based on, for example, agricultural, fishing, mining, lumber harvesting, environmental, or ecological attributes and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Systems and methods for determining object location may include a memory and a processor. The processor may be configured to collect seismic data and geophysical data to determine object location. The processor may be configured to determine one or more seismic attributes associated with a plurality types of noises based on the seismic data and the geophysical data using one or more machine learning algorithms. The processor may be configured to eliminate unwanted noises from noise classifications based on the one or more seismic attributes. The processor may be configured to predict the object location by comparing time and velocity data of the object with recorded timing and velocity data. The processor may be configured to validate the object location by comparing the determined noise with image data. The systems and methods may be used in, for example, detecting missing planes such as Malaysian Airlines Flight 370.

A computer implemented method of tracking a travelling vessel, comprising obtaining a list of plurality of satellites capable of detecting the vessel at location(s) along predicted path(s) of the vessel. For each of the location(s) the following is performed: (a) Predicting vessel's possible future location(s) according to estimated movement vectors derived from a movement graph generated based on historical movement path(s), a recent movement path and a current location of the vessel. (b) Estimating satellites observation windows to identify candidate observation window(s) in which the satellite(s) have visual coverage of the possible future location(s). (c) Calculating detection score for each candidate observation window according to location probability score assigned to the possible future locations and view probability score assigned to the candidate observation windows. (d) Selecting preferred observation window presenting highest detection score. (e) Repeating (a)-(d) in case the vessel not detected in the selected observation window.

A computer implemented method of tracking a travelling vessel, comprising obtaining a list of plurality of satellites capable of detecting the vessel at location(s) along predicted path(s) of the vessel. For each of the location(s) the following is performed: (a) Predicting vessel's possible future location(s) according to estimated movement vectors derived from a movement graph generated based on historical movement path(s), a recent movement path and a current location of the vessel. (b) Estimating satellites observation windows to identify candidate observation window(s) in which the satellite(s) have visual coverage of the possible future location(s). (c) Calculating detection score for each candidate observation window according to location probability score assigned to the possible future locations and view probability score assigned to the candidate observation windows. (d) Selecting preferred observation window presenting highest detection score. (e) Repeating (a)-(d) in case the vessel not detected in the selected observation window.