In preparations for Multi-Domain Operations and Battles, All-Source and OSINT intelligence analysts gather, assess, and extract relevant information from operational databases as well as publicly available information. This data, often unstructured text documents, is noisy with relevant snippets buried within the document corpus. The costs of exploratory search and exploitive document analysis required to find these hidden snippets of information often drive searches toward a small subset of documents. Additionally, modern search tools may reinforce the confirmation bias of analysts by providing only those documents that closely match their search query. Due to the potentially high tempo of multi-domain battle, the end result is a decision or hypothesis that is ill-considered and substantiated by potentially biased information. An automated information foraging framework can mitigate these challenges by automatically identifying a wide breadth of topics for the user, extracted directly from a document corpus. A semantic network formed from the constituent entities within a document corpus contains inherently valuable topological structures that can be used to generate topics and also guide the analyst?s information exploration. Leveraging a suite of information retrieval and graph analysis algorithms that analyze the semantic network, a framework is defined for assisting analysts in both exploring and exploiting relevant information from a corpus to support the sensemaking process.
We describe an automated remote cyclone detection and tracking approach using heterogeneous data from multiple
satellites. Single Earth orbiting satellite has been used in the past to detect and track events such as cyclones but suffer from
major drawbacks due to limited spatio-temporal coverage. Our novel approach addresses the challenges in using
heterogeneous data from multiple data sources for knowledge discovery, tracking and mining of cyclones. Moreover, it offers
better detection performance and spatio-temporal resolutions. Our solution is sufficiently powerful that it generalizes to
multiple sensor measurement modalities. Our approach consists of: (i) feature extraction from each sensor measurement, (ii)
an ensemble classifier for cyclone detection, and (iii) knowledge sharing between the different remote sensor measurements.
Our extensive experimental results demonstrate (i) the superior performance of our cyclone detector compared to previous
work on preprocessed historical data, (ii) stable performance of our cyclone detector when it is applied on different
geographical regions (Western Pacific Ocean and the North Atlantic Ocean), (iii) meaningful knowledge derived from the
cyclone detector output, and (iv) the performance quality of our automated cyclone detection and tracking solution closely
match the cyclone best track information from the National Hurricane Center.