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

  Gallery  

  Exercise Impressions  

The DLR drone pilot

DLR Vector drone with MACS-nano camera

DLR drone gets prepared for flight

VTOL drone lifting off

Drone switching to horizontal flight

Drone flying to first waypoint

DLR drone touching down

Single drone shot

Hardware/software setup in the garage

  Client Screenshots  

Realtime Green/Red Vegetation Index from DLR Vector drone, over Sentinel-2 satellite image

Realtime Green/Red Vegetation Index from DLR Vector drone, over Sentinel-2 satellite image

Realtime image path from DLR Vector drone

Realtime GRVI from DLR Vector drone flightpath, shown over ICEYE SAR image

Screen photo taken in exercise observer tent, with realtime capture progress

  Videos  

  Teams Engaged  

• Constructor University L-SIS research group (lead)
• DLR Institute of Optical Sensor Systems, Department Security Research and Applications: Vector drone (piloted), MACSnano camera, downlink
• rasdaman GmbH: rasdaman service with realtime feed-and-extract
• ICEYE: SAR image

  Terms  

Image Credits: DLR, rasdaman

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