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Activity title

RFT-OT Data and Sharing Hub (DASH)

Activity Reference

SET-335

Panel

SET

Security Classification

NATO UNCLASSIFIED

Status

Active

Activity type

RTG

Start date

2024-03-04T00:00:00Z

End date

2027-03-04T00:00:00Z

Keywords

data fusion, data sharing, database, deep learning, education, EOIR data exploitation, machine learning, open data, pattern of life, Radar data exploitation, SET, STEM, training, young scientists

Background

Many NATO SET RTGs conducted measurements and trials, and the datasets collected in these trials are unique and invaluable. To some degree, parts of the datasets are shared for collaborative analysis internally the respective RTG. However, often times they are forgotten once they have served their initial purpose and are not shared beyond the RTG that collected them. Additionally, only a small circle of entities, even in the closed circle of NATO SETs, is aware of the existence and potential availability of the collected data. Last but not least, obtaining a copy of the dataset with all the relevant associated information is not straightforward in the majority of the cases due to the absence of a procedure or infrastructure to support data exchange: there is no organized framework that enables the seamless sharing of data across NATO Panels and Groups. Although it would strongly benefit cross-panel activities, there has not yet been an effort inside NATO STO / CSO to create a common database for enabling data sharing outside of individual SET RTGs. For these reasons, the full exploitation of datasets is considerably constrained. Additionally, the involvement of young scientists in analyzing the data as well as their engagement in topics that are relevant to NATO is significantly limited. This is of concern to NATO as in the upcoming years many scientists will retire, and young scientists are underrepresented in its activities. This RTG is an attempt to leverage from the efforts of previous RTGs to create new opportunities. It aims at identifying and gathering shareable quality datasets and making them available to young scientists and the research community from NATO nations to raise their interest in NATO related activities. Additionally, it aims to stimulate innovation and to foster collaboration in and among research institutions from the members of the alliance.

Objectives

This TG aims at creating a NATO-internal database with different access possibilities. It should contain diverse, high-quality, shareable datasets consisting of sensor data, such as EO/IR and radar data to support activities at different levels of security and engagement, such as restricted access, open access, etc. The creation of the database provides the much-needed support that other follow-on NATO scientific activities require. That is, this TG will allow for an increase in collaborative research and a path towards standardization for comparison of novel processing algorithms. In addition, it would increase the reuse of data acquired in NATO (and non-NATO) activities. Furthermore, it will facilitate the access to valuable sensor data and/or processing tools to members of the alliance with different capabilities and constraints in resources, such as time, budget and/or hardware. This cooperative research effort aims at the following deliverables and end-products: 1) concept for data-sharing including either upload / download interfaces or data access interfaces 2) legally binding documents to be filled in by data provider and data user, incl. documents about data exploitation possibilities (e.g. Non-Disclosure Agreement, Memorandum of Understanding, etc.) 3) shareable database with quality datasets, incl. data description and ground truth data.

Topics

The datasets and related applications cover topics of radar and EO/IR data exploitation, e.g., sensor characterization, signatures modeling and exploitation, data fusion, target/anomaly detection, pattern of life, scene characterization, decision aids, autonomous systems (detection, navigation, learning), machine learning, deep learning, etc. The efforts of this TG will support the growth of the research output in the above-mentioned areas, especially in artificial intelligence, a crucial and data-demanding field and in other related activities, where data is a scarce and costly commodity.

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