The Challenge  

For a common integrated Recognized Environmental Picture a multitude of disparate, heterogeneous information needs to be provided and evaluated. As most of the realtime or near-realtime data come from sensors delivering discretized pixels etc. these often represent spatio-temporal raster data.

Additionally, exploitation of such data requires a multitude of different analysis methods, often in an ad-hoc combination, for the variety of end devices and their users.

Therefore, edge devices must be integrated efficiently into the mash-up of data sources and sinks for "any query, any time, on any device".


  Capability Demonstration  

  • DLR Vector drone with MACSnano camera on board flies over the Lt General Best Barracks airfield in the Netherlands, as part of the NATO C-UAS Exercise 2022.
  • Drone flies various courses for different tasks, transmitting down a 2cm accurately georeferenced image via 5G to the DLR image server.
  • DLR image server forwards data into the Internet, specifically: a rasdaman datacube server where the images incrementally build up the map.
  • The rasdaman server, once ingested, offers the complete drone imagery via standard WMS and WMTS services.
  • For presentation in the exercise tent, a WMS delivers continuous progress of the data received - in both RGB and, to show some processing on the fly, as GRVI (Green-Red Vegetation Index).
  • In addition to the drone data further layers of 10m Sentinel-2 hyperspectral data and ICEYE SAR imagery were overlaid, showing the capability of merging data from different sources in realtime.
  • The complete pipeline from drone to server to presentation screen in the tent incurred a latency of few seconds.

  User Benefit  

  • enhanced availability of geo data for C4ISR
  • access to any data, at any time, from anywhere - fixed or moving
  • ad-hoc data fusion
  • efficient fog computing: balanced integration of edge devices and data centers for mixed processing and lowest latency

  Exercise Impressions  


  Client Screenshots  



Drone path growth showing RGB
Drone path growth showing on-the-fly computed GRVI
Drone path overlaid on Sentinel-2 and ICEYE SAR imagery

  Teams Engaged  



Image Credits: DLR, rasdaman

This information is provided as part of the Cube4EnvSec project whose Terms of Reference apply in full.