Actual-time monitoring of wildfire boundaries utilizing satellite tv for pc imagery – Google AI Weblog


As world temperatures rise, wildfires around the globe have gotten extra frequent and extra harmful. Their results are felt by many communities as individuals evacuate their houses or undergo hurt even from proximity to the hearth and smoke.

As a part of Google’s mission to assist individuals entry trusted info in crucial moments, we use satellite tv for pc imagery and machine studying (ML) to monitor wildfires and inform affected communities. Our wildfire tracker was not too long ago expanded. It supplies up to date fireplace boundary info each 10–quarter-hour, is extra correct than comparable satellite tv for pc merchandise, and improves on our earlier work. These boundaries are proven for big fires within the continental US, Mexico, and most of Canada and Australia. They’re displayed, with extra info from native authorities, on Google Search and Google Maps, permitting individuals to maintain protected and keep knowledgeable about potential risks close to them, their houses or family members.


Wildfire boundary monitoring requires balancing spatial decision and replace frequency. Essentially the most scalable methodology to acquire frequent boundary updates is to make use of geostationary satellites, i.e., satellites that orbit the earth as soon as each 24 hours. These satellites stay at a hard and fast level above Earth, offering continuous protection of the realm surrounding that time. Particularly, our wildfire tracker fashions use the GOES-16 and GOES-18 satellites to cowl North America, and the Himawari-9 and GK2A satellites to cowl Australia. These present continent-scale pictures each 10 minutes. The spatial decision is 2km at nadir (the purpose straight beneath the satellite tv for pc), and decrease as one strikes away from nadir. The aim right here is to supply individuals with warnings as quickly as doable, and refer them to authoritative sources for spatially exact, on-the-ground knowledge, as obligatory.

Smoke plumes obscuring the 2018 Camp Hearth in California. [Image from NASA Worldview]

Figuring out the exact extent of a wildfire is nontrivial, since fires emit large smoke plumes, which might unfold removed from the burn space and obscure the flames. Clouds and different meteorological phenomena additional obscure the underlying fireplace. To beat these challenges, it is not uncommon to depend on infrared (IR) frequencies, notably within the 3–4 μm wavelength vary. It’s because wildfires (and comparable scorching surfaces) radiate significantly at this frequency band, and these emissions diffract with comparatively minor distortions by way of smoke and different particulates within the environment. That is illustrated within the determine beneath, which exhibits a multispectral picture of a wildfire in Australia. The seen channels (blue, inexperienced, and crimson) principally present the triangular smoke plume, whereas the three.85 μm IR channel exhibits the ring-shaped burn sample of the hearth itself. Even with the added info from the IR bands, nevertheless, figuring out the precise extent of the hearth stays difficult, as the hearth has variable emission energy, and a number of different phenomena emit or replicate IR radiation.

Himawari-8 hyperspectral picture of a wildfire. Observe the smoke plume within the seen channels (blue, inexperienced, and crimson), and the ring indicating the present burn space within the 3.85μm band.


Prior work on fireplace detection from satellite tv for pc imagery is often primarily based on physics-based algorithms for figuring out hotspots from multispectral imagery. For instance, the Nationwide Oceanic and Atmospheric Administration (NOAA) fireplace product identifies potential wildfire pixels in every of the GOES satellites, primarily by counting on the three.9 μm and 11.2 μm frequencies (with auxiliary info from two different frequency bands).

In our wildfire tracker, the mannequin is skilled on all satellite tv for pc inputs, permitting it to study the relative significance of various frequency bands. The mannequin receives a sequence of the three most up-to-date pictures from every band in order to compensate for non permanent obstructions comparable to cloud cowl. Moreover, the mannequin receives inputs from two geostationary satellites, attaining a super-resolution impact whereby the detection accuracy improves upon the pixel measurement of both satellite tv for pc. In North America, we additionally provide the aforementioned NOAA fireplace product as enter. Lastly, we compute the relative angles of the solar and the satellites, and supply these as extra enter to the mannequin.

All inputs are resampled to a uniform 1 km–sq. grid and fed right into a convolutional neural community (CNN). We experimented with a number of architectures and settled on a CNN adopted by a 1×1 convolutional layer to yield separate classification heads for fireplace and cloud pixels (proven beneath). The variety of layers and their sizes are hyperparameters, that are optimized individually for Australia and North America. When a pixel is recognized as a cloud, we override any fireplace detection since heavy clouds obscure underlying fires. Even so, separating the cloud classification process improves the efficiency of fireside detection as we incentivize the system to higher establish these edge instances.

CNN structure for the Australia mannequin; the same structure was used for North America. Including a cloud classification head improves fireplace classification efficiency.

To coach the community, we used thermal anomalies knowledge from the MODIS and VIIRS polar-orbiting satellites as labels. MODIS and VIIRS have increased spatial accuracy (750–1000 meters) than the geostationary satellites we use as inputs. Nonetheless, they cowl a given location solely as soon as each few hours, which often causes them to overlook rapidly-advancing fires. Due to this fact, we use MODIS and VIIRS to assemble a coaching set, however at inference time we depend on the high-frequency imagery from geostationary satellites.

Even when limiting consideration to lively fires, most pixels in a picture should not at the moment burning. To scale back the mannequin’s bias in the direction of non-burning pixels, we upsampled fireplace pixels within the coaching set and utilized focal loss to encourage enhancements within the uncommon misclassified fireplace pixels.

The progressing boundary of the 2022 McKinney fireplace, and a smaller close by fireplace.


Excessive-resolution fireplace alerts from polar-orbiting satellites are a plentiful supply for coaching knowledge. Nonetheless, such satellites use sensors which are much like geostationary satellites, which will increase the chance of systemic labeling errors (e.g., cloud-related misdetections) being integrated into the mannequin. To judge our wildfire tracker mannequin with out such bias, we in contrast it towards fireplace scars (i.e., the form of the overall burnt space) measured by native authorities. Hearth scars are obtained after a fireplace has been contained and are extra dependable than real-time fireplace detection methods. We examine every fireplace scar to the union of all fireplace pixels detected in actual time through the wildfire to acquire a picture such because the one proven beneath. On this picture, inexperienced represents accurately recognized burn areas (true optimistic), yellow represents unburned areas detected as burn areas (false optimistic), and crimson represents burn areas that weren’t detected (false unfavorable).

Instance analysis for a single fireplace. Pixel measurement is 1km x 1km.

We examine our fashions to official fireplace scars utilizing the precision and recall metrics. To quantify the spatial severity of classification errors, we take the utmost distance between a false optimistic or false unfavorable pixel and the closest true optimistic fireplace pixel. We then common every metric throughout all fires. The outcomes of the analysis are summarized beneath. Most extreme misdetections had been discovered to be a results of errors within the official knowledge, comparable to a lacking scar for a close-by fireplace.

Check set metrics evaluating our fashions to official fireplace scars.

We carried out two extra experiments on wildfires in the USA (see desk beneath). First, we evaluated an earlier mannequin that depends solely on NOAA’s GOES-16 and GOES-17 fireplace merchandise. Our mannequin outperforms this method in all metrics thought of, demonstrating that the uncooked satellite tv for pc measurements can be utilized to boost the prevailing NOAA fireplace product.

Subsequent, we collected a brand new take a look at set consisting of all giant fires in the USA in 2022. This take a look at set was not obtainable throughout coaching as a result of the mannequin launched earlier than the hearth season started. Evaluating the efficiency on this take a look at set exhibits efficiency in keeping with expectations from the unique take a look at set.

Comparability between fashions on fires in the USA.


Boundary monitoring is a part of Google’s wider dedication to convey correct and up-to-date info to individuals in crucial moments. This demonstrates how we use satellite tv for pc imagery and ML to trace wildfires, and supply actual time help to affected individuals in instances of disaster. Sooner or later, we plan to maintain enhancing the standard of our wildfire boundary monitoring, to broaden this service to extra nations and proceed our work serving to fireplace authorities entry crucial info in actual time.


This work is a collaboration between groups from Google Analysis, Google Maps and Disaster Response, with help from our partnerships and coverage groups. We might additionally prefer to thank the hearth authorities whom we accomplice with around the globe.


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