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NEU4SST - Neuromorphic Processing for Space Surveillance and Tracking

In this project the authors look at event-based sensing and processing for space situational awareness (SSA). Many advantages exist with the new paradigm of neuromorphic engineering, the authors investigate: If event-based optical data is suitable for the task and if processing with Spiking Neural Networks (SNN) provides advantages in terms of efficiency and efficacy. With the implementation of a novel heterogeneous LIF SNN, authors claim to surpass the state of the art in event-based SSA with a 15% increase in accuracy.

United Kingdom

EISI_I-2022-00363

UNIVERSITY OF STRATHCLYDE
Hybrid Edge-Cloud AI Accelerated Astrometric Reduction Pipeline for Agile Near-Real Time In-Situ Space Surveillance and Tracking

With the “democratisation of space”, and the proliferation of large non-geostationary orbit (NGO) constellations, the pace at which human-made objects are being deployed in orbit demands urgent action in defining and supporting a global Space Traffic Management (STM) plan. Promptly identifying, tracking, and cataloguing of resident space objects (RSOs), in particular after orbit insertion or break-up and fragmentation events, as the recent Russian anti-satellite weapon (ASAT) test put in evidence, is of critical importance to enable a safe and sustainable use of space.

Germany

EISI_I-2022-00365

Vyoma
SPOTS: Spectral properties of submerged and biofouled marine plastic litter

Recent studies have shown that remote sensing of floating marine plastic litter (MPL) is feasible from unmanned aerial systems, aircraft and satellite missions. However, in the infrared (IR) spectrum, water is a strong light absorber which makes the spectral detection and discrimination of plastics challenging. Additionally, MPL is often covered with living organisms and it is unclear what impact different thickness of biofouling may have on the spectral reflectance of floating plastic.

The Netherlands

20-D-S-TEC-01-h

The Ocean Cleanup
D-TACS: on demand data transformations and auto-calibration in-orbit

Many use-cases for insight generated in orbit require the integration and calibration of raw data from multiple instruments and vantage-points - a future vision sometimes referred to as ‘hybrid observation’. Such a capability would be a foundational requirement in the processing chain for rapid follow-up observations especially when we need to estimate quantitative variables (e.g. soil moisture or water pollutants).

United Kingdom

EISI_I-2022-00379

Trillium Technologies
MARLISAT

The objective of the MARLISAT project was to further develop Marine Plastic Monitoring through the use of multiple satellite technologies. In effect, the intention was to use satellite technology to detect Marine litter, track it and forecast it pathways. 4 satellite technologies were combined:

France

20-D-T-TEC-01-m

CLS
Detection and tracking of large marine litter based on high-resolution remote sensing time series, machine learning, and ocean current modelling (TRACE)

The overall goal of the TRACE project was to build a remote sensing based fully automated system for the detection and tracking of large marine litter and accumulation patches of smaller litter items in order to obtain precise and reliable data on floating macro-litter regarding their quantity, position, accumulation zones, material properties, floating depth, and sources. The tracking mechanism has been implemented by coupling the daily satellite-based detections with an oceanographic forecasting system and by a two-step object matching approach.

Germany

20-D-T-TEC-01-b

Helmholtz-Snetrum Potsdam
Plastic Litter Project: Detection and monitoring of artificial plastic targets with satellite imagery and UAV

There is an urgent need to assess the levels of plastic pollution in the ocean, to allow for future monitoring and determine the efficacy of any remediation put into force. As a consequence, parallel to in situ observations, there is a growing interest in detecting marine debris using remote sensing satellite and UAV imagery. The Marine Remote Sensing Group (MRSG) at the University of the Aegean is one of the few groups worldwide with in-deep experience in developing artificial plastic debris target structures, in different configurations and polymer composition.

Greece

20-D-T-TEC-01-h

University of the Aegean
Artificial Intelligence and drones supporting the detection and mapping of floating aquatic plastic litter (AIDMAP)

Remote detection and mapping of floating marine plastic litter (FMPL) from space needs to operate in synergy with clean up actions. This implicates that an observation system is needed combining high revisit and high spatial resolution. Currently there is no single remote sensing technology with sufficient revisit capabilities and spatial/spectral resolution relevant to FMPL dynamics. Therefore a combination of small drones, satellites and High Altitude Pseudo-Satellite (HAPS) is anticipated to provide the long term sustainable technical solution.

Belgium

20-D-T-TEC-01-n

Vito
Novel estimation of shallow water bathymetry using ICESat-2 laser altimetry, signal processing and machine learning and Sentinel-2 optical data in a highly automated approach

The project “Novel estimation of shallow water bathymetry using ICESat-2 laser altimetry, signal processing and machine learning and Sentinel-2 optical data in a highly automated approach” was funded under the European Space Agencies’ Open Space Innovation Platform (OSIP). The overall objective was to build a self-calibrating and efficient bathymetry mapping system by leveraging accurate and consistent ICESat-2 laser altimeter data as a calibration source for high-resolution Copernicus Sentinel-2 based water depth retrievals.

Denmark

ETD 2020-02-c

DHI Gras
A Simulator for Marine Litter Observation from Space

“Remote Sensing for Marine Litter - RESMALI” has identified the ideal parameters to study marine litter (ML) from space, including spectral bands with affordable signal-to-noise ratio (SNR) at top-of-atmosphere (TOA) level, in a range of wavelengths from visible to SWIR. To continue this path towards a dedicated mission, performance assessment against simulated real-case scenarios of ML is key. We want to build a simulator to estimate ML signal at TOA from a set of scenarios representing the various applications already targeted by the community (e.g.

France

20-D-T-TEC-01-k

ARGANS LIMITED

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