Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire
Abstract
:1. Introduction
- A one-dimensional (1D) CNN for detecting wildfires using PRISMA hyperspectral imagery was considered, and promising results are shown for the edge implementation on three different hardware accelerators (i.e., computer hardware designed to perform specific functions more efficiently when compared to software running on a general-purpose central processing unit).
- We demonstrate that AI-on-the-edge paradigms are feasible for future mission concepts using appropriate CNN architectures and mature astrionics technologies to perform time- and power-efficient inferences.
2. Current Detection Methods
3. PRISMA Mission
- Level 1, radiometrically corrected and calibrated top of atmosphere (TOA) data.
- Level 2B, Geolocated at-ground spectral radiance product.
- Level 2C, Geolocated at-surface reflectance product.
- Level 2D, Geocoded version of the Level 2C product.
3.1. Dataset Definition
3.2. Automatic Classification with a 1D CNN Approach
4. Astrionics Implementation
4.1. Description of the hardware accelerators
4.2. Movidius Stick
- Supporting CNN profiling, prototyping and tuning workflow;
- Real-time on-device inference (Cloud connectivity not required);
- Features the Movidius Vision Processing Unit with energy-efficient CNN processing;
- All data and power provided over a single USB type-A port;
- Run multiple devices on the same platform to scale performance.
4.3. Jetson Nano
4.4. Jetson TX2
5. Results
- Results on the Movidius: The results of the deployment on the Movidius indicate that the accuracy did not vary in comparison to the values that were presented in Table 9. At the same time, the inference time was approximately 5.8 milliseconds, and the computing power was 1.4 watts on average.
- Results on the Jetson TX2: The results of the deployment on the Jetson TX2 revealed that the accuracy did not change in comparison to the values that were reported in Table 9. On the other hand, the inference time was approximately 3.0 milliseconds, and the computational power was 4.8 W on average (2.1 W if considering the power consumed by the GPU only). It is important to note that these results are related to the TX2 setup that provided the least inference time and the maximum power consumption. Other configurations can be set up to lower the amount of power that is consumed, so it is important to keep this in mind (and increase the inference time).
- Results on the Jetson Nano: The findings of the deployment on the Jetson Nano demonstrate that the accuracy did not change compared to the values reported in Table 9. On the other hand, the inference time was approximately 3.4 milliseconds, and the computational power was 2.6 watts on average (2.0 W if considering the power consumed by the GPU only). Concerning the Jetson TX2, these findings are associated with the configuration that offers the quickest possible inference time at the expense of the highest possible power consumption.
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Orbit | Altitude | Advantages |
---|---|---|
Geostationary Earth Orbit (GEO) | Circular orbit with an altitude of 35,786 km and zero inclination |
|
Low Earth Orbit (LEO) | Altitude of 2000 km or less |
|
Sun-Synchronous Orbit (SSO) | Nearly polar orbit that passes the equator at the same local time on every pass. Typical Sun-synchronous Earth orbits are 600–800 km. |
|
(Satellite)-Sensor | Spectral Bands | Access to the Data | Spatial Scale | Spatial Resolution | Specs/Advantages/Limitations | Data Coverage | Accuracy Range |
---|---|---|---|---|---|---|---|
Terra/Aqua-MODIS | 36 (0.4–14.4 µm) | Registration Required (NASA) | Global | 0.25 km (bands 1–2) 0.5 km (bands 3–7) 1 km (bands 8–36) | Easily accessible, limited spatial resolution, revisit time: 1–2 days | Earth | 92.75–98.32% |
Himawari-8/9—AHI-8 | 16 (0.4–13.4 µm) | Registration Required/ (Himawari Cloud) | Regional | 0.5 km or 1 km for visible and near-infrared bands and 2 km for infrared bands | Imaging sensors with high radiometric, spectral, and temporal resolution. 10 min (Full disk), revisit time: 5 min for areas in Japan/Australia) | East Asia and Western Pacific | 75–99.5% |
MSG—SEVIRI | 12 (0.4–13.4 µm) | Registration Required (EUMETSAT) | Regional | 1 km for the high-resolution visible channel, 3 km for the infrared and the 3 other visible channels | Low noise in the long-wave IR channels, tracking of dust storms in near-real-time, susceptibility of the larger field of view to contamination by cloud and lack of dual-view capability, revisit time: 5–15 min | Atlantic Ocean, Europe and Africa | 71.1–98% |
GOES-16 and 18 | 16 (0.4–13.4 µm) | Registration Required (NOAA) | Regional | 0.5 km for the 0.64 µm visible channel 1 km for other visible/near-IR 2 km for bands > 2 µm | Infrared resolutions allow the detection of much smaller wildland fires with high temporal resolution but relatively low spatial resolution, and delays in data delivery, revisit time: 5–15 min | Western Hemisphere (North and South America) | 94–98% |
HuanJing (HJ)-1B—WVC (Wide View CCD Camera)/IRMSS (Infrared Multispectral Scanner) | WVC: 4 (0.43–0.9 µm) IRMSS: 4 (0.75–12.5 µm) | Registration Required | Regional | WVC: 30 m IRMSS: 150–300 m | Lack of an onboard calibration system to track HJ-1 sensors’ on-orbit behaviour throughout the life of the mission, revisit time: 4 days | Asian and Pacific Region | 94.45% |
POES/MetOp—AVHRR | 6 (0.58–12.5 µm) | Registration Required (NOAA) | Global | 1.1 km by 4 km at nadir | Coarse spatial resolution, revisit time: 6 h | Earth | 99.6% |
S-NPP/ NOAA-20/NOAA— VIIRS-375 m | 16 M-bands (0.4–12.5 µm) 5 I-bands (0.6–12.4 µm) 1 DNB (0.5–0.9 µm) | Registration Required (NASA) | Global | 0.75 km (M-bands) 0.375 km (I-bands) 0.75 km (DNB) | Increased spatial resolution, improved mapping of large fire perimeters, revisit time: 12 h | Earth | 89–98.8% |
CubeSats (data refer to a specific design from [25]) | 2: MWIR (3–5 µm) and LWIR (8–12 µm) | Commercial access planned | Global | 0.2 km | Small physical size, reduced cost, improved temporal resolution/response time, Revisit time: less than 1 h. | Wide coverage in orbit | - |
Fire Category | Real-Time Measurable Behaviour Parameters | Real-Time Observable Manifestations of Extreme Fire Behaviour ((EFB) | Type of Fire and Capacity of Control | ||||||
---|---|---|---|---|---|---|---|---|---|
Fireline Intensities (FLI) (kWm−1) | Rate Of Spread (ROS) (m/min) | Flame Length (FL) (m) | Pyrocumulonimbus (PyroCb) | Downdrafts | Spotting Activity | Spotting Distance (m) | |||
Normal Fires | 1 | <500 | <5 a <15 b | <1.5 | Absent | Absent | Absent | 0 | Surface fire Fairly easy |
2 | 500–2000 | <15 a <30 b | <2.5 | Absent | Absent | Low | <100 | Surface fire Moderately difficult | |
3 | 2000–4000 | <20 c <50 d | 2.5–3.5 | Absent | Absent | High | ≥100 | Surface fire, torching possible Very difficult | |
4 | 4000–10,000 | <50 c <100 d | 3.5–10 | Unlikely | In some localised cases | Prolific | 500–1000 | Surface fire, crowning likely depending on vegetation type and stand structure Extremely difficult | |
Extreme Wildfire Events | 5 | 10,000–30,000 | <150 c <250 d | 10–50 | Possible | Present | Prolific | >1000 | Crown fire, either wind- or plume-driven Spotting plays a relevant role in fire growth Possible fire breaching across an extended obstacle to local spread Chaotic and unpredictable fire spread Virtually impossible |
6 | 30,000–100,000 | <300 | 50–100 | Probable | Present | Massive Spotting | >2000 | Plume-driven, highly turbulent fire Chaotic and unpredictable fire spread Spotting, including long distance, plays a relevant role in fire growth Possible fire breaching across an extended obstacle to local spread Impossible | |
7 | >100,000 (possible) | >300 (possible) | >100 (possible) | Present | Present | Massive Spotting | >5000 | Plume-driven, highly turbulent fire Area-wide ignition and firestorm development non-organised flame fronts because of extreme turbulence/vorticity and massive spotting Impossible |
Year | Event Name | Affected Area | Burned Area (Approx. Acres) |
---|---|---|---|
1 June 2020–1 June 2021 | 2020–2021 Australian wildfire seasons | Nationwide | 617,763 |
5 September 2019–2 March 2020 | 2019–2020 Australian wildfire season (Black Summer) | Nationwide | 46,030,000 |
February 2019 | Tingha wildfire | New South Wales | 57,870 |
11–14 February 2017 | 2017 New South Wales wildfires | New South Wales | 130,000 |
January 2016 | 2016 Murray Road wildfire (Waroona and Harvey) | Western Australia | 170,910 |
25 November–2 December 2015 | 2015 Pinery wildfire | South Australia | 210,000 |
15–24 November 2015 | Perth Hills wildfire complex–Solus Group | Western Australia | 24,750 |
October–November 2015 | 2015 Esperance wildfires | Western Australia | 490,000 |
29 January–20 February 2015 | 2015 O’Sullivan wildfire (Northcliffe–Windy Harbour) | Western Australia | 244,440 |
2–9 January 2015 | 2015 Sampson Flat wildfires | South Australia | 49,000 |
January 2015 | 2015 Lower Hotham wildfire (Boddington) | Western Australia | 129,420 |
1 August–9 August 2015 | 2015 Wentworth Falls Winter Fire | New South Wales | 2,000 |
17–28 October 2013 | 2013 New South Wales wildfires | New South Wales | 250,000 |
18 January 2013 | Warrumbungle wildfire | New South Wales | 130,000 |
4 January 2013 | Tasmanian wildfires | Tasmania | 49,000 |
27 December 2011–3 February 2012 | Carnarvon wildfire complex | Western Australia | 2,000,000 |
7 February–14 March 2009 | Black Saturday wildfires | Victoria | 1,100,000 |
30 December 2007 | Boorabbin National Park | Western Australia | 99,000 |
Pixels per Classes | |||||||
---|---|---|---|---|---|---|---|
Wildfire Location | Usage | 0 Fire | 1 Smoke | 2 Burned Areas | 3 Vegetation | 4 Bare Soil | Total |
North-East | Train & Val | 58 | 10 | 30 | 50 | 40 | 188 |
South | Test | 11 | 11 | 9 | 10 | 10 | 51 |
North-West | Test | 5 | 0 | 5 | 5 | 5 | 20 |
Parameters | Nvidia Jetson Nano | Google Coral USB | Intel Movidius NCS |
---|---|---|---|
Inference time | ~38 ms | ~70–92.32 ms | ~225–227 ms |
fps | ~25 | ~9–7 | ~4.43–4.39 |
CPU usage | 47–50% | 135% | 87–90% |
Memory usage | 32 % | 8.7% | ~7% |
OS | Ubuntu 18.04 aarch64 | Raspbian GNU/License 10 (Buster) | Raspbian GNU/License 9 (Stretch) |
Precision | Recall | F1 Score | |
---|---|---|---|
0—Fire | 1.00 | 1.00 | 1.00 |
1—Smoke | 1.00 | 1.00 | 1.00 |
2—Burned | 1.00 | 1.00 | 1.00 |
3—Vegetation | 0.92 | 1.00 | 0.96 |
4—Bare soil | 1.00 | 0.92 | 0.96 |
Accuracy | 0.98 | ||
Macro average | 0.98 | 0.98 | 0.98 |
Weighted average | 0.98 | 0.98 | 0.98 |
Wildfire Location | Precision | Recall | F1 Score |
---|---|---|---|
Australia, North-East | 0.98 | 0.98 | 0.98 |
Australia, South | 0.98 | 0.98 | 0.98 |
Australia, North-West | 1.00 | 0.95 | 0.97 |
HW Accelerator | Inference Time (ms) | Power Consumption (W) |
---|---|---|
Movidius | 5.8 | 1.4 |
Jetson TX2 | 3.0 | 4.8 (2.1 GPU only) |
Jetson Nano | 3.4 | 2.6 (2.0 GPU only) |
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Thangavel, K.; Spiller, D.; Sabatini, R.; Amici, S.; Sasidharan, S.T.; Fayek, H.; Marzocca, P. Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire. Remote Sens. 2023, 15, 720. https://doi.org/10.3390/rs15030720
Thangavel K, Spiller D, Sabatini R, Amici S, Sasidharan ST, Fayek H, Marzocca P. Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire. Remote Sensing. 2023; 15(3):720. https://doi.org/10.3390/rs15030720
Chicago/Turabian StyleThangavel, Kathiravan, Dario Spiller, Roberto Sabatini, Stefania Amici, Sarathchandrakumar Thottuchirayil Sasidharan, Haytham Fayek, and Pier Marzocca. 2023. "Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire" Remote Sensing 15, no. 3: 720. https://doi.org/10.3390/rs15030720