4/16/2024 0 Comments Google satellite maps real timeFinally, we compute the relative angles of the sun and the satellites, and provide these as additional input to the model.Īll inputs are resampled to a uniform 1 km–square grid and fed into a convolutional neural network (CNN). In North America, we also supply the aforementioned NOAA fire product as input. Additionally, the model receives inputs from two geostationary satellites, achieving a super-resolution effect whereby the detection accuracy improves upon the pixel size of either satellite. The model receives a sequence of the three most recent images from each band so as to compensate for temporary obstructions such as cloud cover. In our wildfire tracker, the model is trained on all satellite inputs, allowing it to learn the relative importance of different frequency bands. For example, the National Oceanic and Atmospheric Administration (NOAA) fire product identifies potential wildfire pixels in each of the GOES satellites, primarily by relying on the 3.9 μm and 11.2 μm frequencies (with auxiliary information from two other frequency bands). Prior work on fire detection from satellite imagery is typically based on physics-based algorithms for identifying hotspots from multispectral imagery. Note the smoke plume in the visible channels (blue, green, and red), and the ring indicating the current burn area in the 3.85μm band. Himawari-8 hyperspectral image of a wildfire. Even with the added information from the IR bands, however, determining the exact extent of the fire remains challenging, as the fire has variable emission strength, and multiple other phenomena emit or reflect IR radiation. The visible channels (blue, green, and red) mostly show the triangular smoke plume, while the 3.85 μm IR channel shows the ring-shaped burn pattern of the fire itself. This is illustrated in the figure below, which shows a multispectral image of a wildfire in Australia. This is because wildfires (and similar hot surfaces) radiate considerably at this frequency band, and these emissions diffract with relatively minor distortions through smoke and other particulates in the atmosphere. To overcome these challenges, it is common to rely on infrared (IR) frequencies, particularly in the 3–4 μm wavelength range. Clouds and other meteorological phenomena further obscure the underlying fire. ĭetermining the precise extent of a wildfire is nontrivial, since fires emit massive smoke plumes, which can spread far from the burn area and obscure the flames. Smoke plumes obscuring the 2018 Camp Fire in California. The goal here is to provide people with warnings as soon as possible, and refer them to authoritative sources for spatially precise, on-the-ground data, as necessary. The spatial resolution is 2km at nadir (the point directly below the satellite), and lower as one moves away from nadir. These provide continent-scale images every 10 minutes. Specifically, our wildfire tracker models use the GOES-16 and GOES-18 satellites to cover North America, and the Himawari-9 and GK2A satellites to cover Australia. These satellites remain at a fixed point above Earth, providing continual coverage of the area surrounding that point. The most scalable method to obtain frequent boundary updates is to use geostationary satellites, i.e., satellites that orbit the earth once every 24 hours. Wildfire boundary tracking requires balancing spatial resolution and update frequency. Real-time boundary tracking of the 2021-2022 Wrattonbully bushfire, shown as a red polygon in Google Maps. They are displayed, with additional information from local authorities, on Google Search and Google Maps, allowing people to keep safe and stay informed about potential dangers near them, their homes or loved ones. These boundaries are shown for large fires in the continental US, Mexico, and most of Canada and Australia. It provides updated fire boundary information every 10–15 minutes, is more accurate than similar satellite products, and improves on our previous work. Our wildfire tracker was recently expanded. Their effects are felt by many communities as people evacuate their homes or suffer harm even from proximity to the fire and smoke.Īs part of Google’s mission to help people access trusted information in critical moments, we use satellite imagery and machine learning (ML) to track wildfires and inform affected communities. Posted by Zvika Ben-Haim and Omer Nevo, Software Engineers, Google ResearchĪs global temperatures rise, wildfires around the world are becoming more frequent and more dangerous.
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