Abstract: A system and method for edge-deployed automated target recognition with distributed processing architecture. The system processes satellite sensor data directly at the edge using coordinated services for object detection and tracking. Detection services identify objects with geodetic localization while tracking services maintain persistent tracks across multiple frames. A message broker enables asynchronous communication between components and manages data flow for both real-time streaming and historical queries. The architecture supports track custody transfer between distributed nodes, allowing continuous object tracking across multiple satellite systems. This edge-based approach eliminates cloud processing dependencies and reduces latency from minutes to seconds. The system's distributed design enables autonomous operation at the sensor edge while maintaining coordination capabilities with other nodes, making it suitable for time-sensitive applications requiring real-time object recognition and tracking.

Abstract: A system and method for object monitoring, detection, and segmentation in electro-optical (EO) satellite imagery data comprising an image processing engine configured to prepare EO data for analysis by an inference engine which utilizes one or more trained models to perform object detection or image segmentation. The workflow begins with ingesting satellite data, followed by de-hazing to remove atmospheric interference. Image enhancement improves resolution and geo-registration ensures precise spatial alignment. The processed image is then fed into a machine learning-based object detection or image segmentation network, trained to identify specific objects of interest. This integrated approach leverages advanced technologies to extract actionable insights from satellite data, enabling efficient and precise object monitoring, detection, and segmentation.

Abstract: A target custody platform comprising a data acquisition engine, a data analysis engine, a machine learning engine, and a data presentation layer configured to task a plurality of satellites for imagery data wherein the imagery data and metadata is used in conjunction with other types of data including identification data and weather data as inputs into a one or more machine and/or deep learning algorithms configured to predict a the likelihood a target of interest will travel along a project path.