SmartSat is integrating the capabilities from the priority areas set in SmartSat’s Technology Roadmap across three primary research program areas as below.

Advance communications and more intelligent satellites will give us the ability to generate higher resolution, higher frequency data.  This will enable us to generate image data from sensors and high resolution real-time video so that we can monitor our land, oceans and our environment in real time. AI, deep learning and other advanced analytics techniques will enable us to build analytical models and AI systems that can analyse satellite generated data and provide insights or trigger actions in real time. See projects in Next Generation Earth Observation Data Services projects below:
Topic of this theme may include:
  • Secure & integrated data intensive space systems
  • Customer-centric EO Analytics
  • EO data fusion architectures
  • EO data visualization
  • Hyperspectral sensing
  • Design methodologies for mission specific services
  • Integration Test bed for rapid product development

Towards effective adaptive monitoring of UN SDG #15: Protect and Sustain Terrestrial Ecosystems using EO Data, Products and Services

Next Generation Earth Observation Data Services

To develop and assess operational approaches for mapping and monitoring changes in vegetation structure and condition in coastal ecosystems to enable achievement of UN SDG #15 Protect and Sustain Terrestrial Ecosystems.

P3.06s

Project Leader:
Professor Stuart Phinn, University of Queensland

PhD Student:
Jason Dail, University of Queensland

Participants:

Satellite image-based smoke detection for bush fire detection

Next Generation Earth Observation Data Services

Early detection of wildfires is vital to reduce fire caused deaths and property loss, and to prevent disastrous impact on wildlife and the environment, as well as to minimize the economic loss of the government on firefighting. Traditional approaches to wildfire detection often have delays, for example, fire observation towers take time to install and run but they only cover a small area; others may tend to be uneconomical, such as spotter aircrafts which are expensive to run but only operable for a short time.

In recent years, more satellite data on earth surface observation become available and this opens the door for new methods to be developed, which are expected to be prompt in time when fires are still small. However, the current satellite image-based detection methods are still ineffective for early fire detection due to low spatial or temporal resolution of the sensors. That says, wildfires usually cannot be detected directly by the satellites until they have burnt for a relatively long time and to a relatively large scale.

Instead of direct fire detection, smoke detection is expected to an alternative method since smoke disperses very fast into the air and can be visibly detected by the satellites quicker than fire. However, there remains many challenges. First, smoke has similar characteristics with cloud, dust, haze, which are difficult to be visually distinguished most of the time. Due to this, the false positive rate of smoke detection remains high for most developed models. Second, smoke does not have fixed shapes, which usually change very fast especially when there are winds. This will dramatically increase the complexity of the smoke detection models. Furthermore, the characteristics of smoke are closely associated with the fuels and are subject to the local environmental conditions, which makes the detection models hard to be applied to a different area without further adjustment. The differences between the sensor specifications also raise challenges for model reapplication and integration.

This project aims to develop practical machine learning technologies which can address the above challenges for smoke detection based on satellite imagery. The technologies are expected to become a new way to address the fire detection problems in vast remote areas. The application of the technologies is hoped to reduce the cost of running remote fire towers, to mitigate the risks on people working at the towers, and to shorten the decision time between the start of the fire and proper reactions taken.

P3.07s

Project Leader:
Associate Professor Jixue Liu, University of South Australia

PhD Student:
Liang Zhai, University of South Australia

Participants:

SatCom IoT-enabled Automatic Ground Water Collection and Aggregation Pilot (SIG Water)

Next Generation Earth Observation Data Services

In this project we propose the development of a pilot to demonstrate the use of an Internet-of-Things (IoT) low cost satellite telecommunications solution as an end-to-end cost-effective means to transmit and aggregate, in near real time, automatically collected information from ground water bores in rural and regional areas, with a focus on environmental water monitoring.

To this end, the project will test the technical feasibility, reliability and cost-effectiveness of deploying an end-to-end IoT satcoms solution in a remote and harsh environment – including evaluating, procuring and deploying sensor technologies to measure water levels, pressure, salinity, temperature and flow in groundwater stations/bores and assessing the feasibility of these to operate autonomously; in addition the project will define and develop a prototype to provide a “universal” connectivity module for ground water markets. This approach will be compared to current scenarios where information is collected manually and sparsely in time.

P3.01

Project Leader:
Phil Delaney, FrontierSI

Participants:

Real-time Fire Analytics

Next Generation Earth Observation Data Services

Australia urgently requires verified, high quality, real time information on wildfire location and intensity.

This project proposes a satellite system of systems encompassing geostationary, polar orbiting and aerial based sensors for real time fire landscape attribution. The project will design and deliver a data and platform ecosystem to allow autonomous real time information on fire to be detected, processed and delivered to end users.

We present this project as two elements: 1) design and implementation of a data and platform ecosystem that will enable fire surveillance in real time from geostationary, polar-orbiting and aerial platformed sensors. This system of systems approach will allow for best available information to always be provided. 2) derivation of autonomous AI algorithms for the real time surveillance of fires and its attribution (e.g. burn severity, FRP etc.).

Satellite sensors in the ecosystem of systems will include JAXA’s Himawari-8, MODIS, VIIRS, Sentinel-2, DLR’s Firebird constellation, as well as in situ and drone- and high-altitude-platform based observations for calibration, validation and accuracy assessment.

P3.04

Project Leader:
Professor Simon Jones, Royal Melbourne Institute of Technology (RMIT)

Participants:

Phase-0 AquaWatch Australia

Next Generation Earth Observation Data Services

This project is a partnership between SmartSat and CSIRO to develop ground-to-space 24/7 water quality monitoring technology for Australia’s waterways, reservoirs and coastal environments.

This Phase-0 project, 12 months in duration, addresses in-situ sensor technology readiness and a space-based concepts of operations through end user consultation (in coordination with Know the Market to Grow the Market project), requirements analysis, market analysis, data analytics processing with existing data sets, and a preliminary satellite mission design, for the development of a detailed business case and investment options analysis.

Water quality monitoring is an essential input to effective management of a key national resource that is distributed across the continent requiring new approaches to address the current limited capability. The SmartSat/CSIRO Aquawatch program will address remote sensing, and in-situ capabilities, with this proposal focusing on the former and the TRL levels of in-situ sensing technologies.

This project will provide options to coordinate across the current state-based approach and has the potential to position Australia as a global leader in water quality monitoring and management.

The space-based component of AquaWatch will be a world-first, custom water quality monitoring-focused satellite earth observation mission/constellation with a global footprint.

Learn more about CSIRO research missions.

P3.02

Project Leader:
Alex Held, CSIRO

Participants:

OzFuel (Australian Fuel Monitoring from Space) Phase A

Next Generation Earth Observation Data Services

The spectral and radiometric resolution in existing satellite data is insufficient for monitoring fuel conditions in Australia’s eucalypt-dominant bushland. The OzFuel (Australian Fuel Monitoring from Space) satellite mission will make use of sovereign technologies to deliver fuel hazard data, with the goal of improving Australia’s pre-fire monitoring, prediction, preparation, response and resilience.

Led by ANU, the 12-month project will deliver OzFuel Phase A with partners UNSW Canberra, Skykraft, Spiral Blue and LatConnect 60. The project involves end-to-end mission design at the ANCDF, verification of fuel biochemical properties to be sensed from space, and market analysis on the commercial potential of shortwave infrared data in parallel industry sectors.

The project will accelerate development of critical sovereign Earth observation space technologies including advanced sensors, edge processing and small satellite capabilities.

P3.24

Project Leader:
Associate Professor Marta Yebra, The Australian National University

Participants: