Искусственный интеллект Помогает обнаруживать Лесные пожары из Космоса До Того, как Они Выйдут из-под контроля

20 июля спутник немецкой компании OroraTech обнаружил горячую точку недалеко от Абердина, штат Калифорния, к востоку от национального парка Кингс-Каньон. Это было первое обнаружение пожара в Скале, который вызвал эвакуацию и закрытие дорог и разросся до более чем 12 000 акров в жарких и ветреных условиях, прежде чем был локализован пожарными.

Каменный пожар является одним из небольшого, но растущего числа первых обнаружений, связанных с системой OTC-P1 компании OroraTech, которая является частью нового поколения спутников, разработанных специально для обнаружения и мониторинга лесных пожаров. Появление этих спутников связано с тем, что лесные пожары становятся все более частыми и интенсивными из-за антропогенного изменения климата. Раннее обнаружение обычно помогает, позволяя пожарным службам локализовать потенциально разрушительные пожары до того, как они выйдут из-под контроля. Спутники, которые уже давно используются для обнаружения пожаров в очень отдаленных районах, готовы играть более важную роль в раннем обнаружении, дополняя устаревшие системы наблюдения за пожарами, наземные камеры и звонки граждан в службу 911.

Запатентованная OroraTech тепловизионная инфракрасная камера оснащена датчиками, которые обнаруживают характерные для пожара температурные характеристики.OroraTech

Однако обнаружение лесных пожаров из космоса - непростая задача. Точно так же, как наблюдатели-люди могут не заметить нового возгорания или принять пыль за дым, алгоритмы обнаружения, используемые для идентификации пожаров по спутниковым данным, могут не заметить небольших или тлеющих очагов возгорания и могут быть обмануты солнечными бликами и промышленными выбросами. Для повышения точности и своевременности дистанционного обнаружения лесных пожаров требуются специализированные датчики, а также надежные алгоритмы, и исследователи все чаще обращаются к машинному обучению для улучшения последних.

Современные пожарные спутники основаны на почти полувековом опыте. В 1980 году Джефф Дозье и Майкл Мэтсон, работавшие вместе в Национальном управлении океанических и атмосферных исследований, обнаружили крошечные яркие пятна — вспышки газа из нефтяных скважин на Ближнем Востоке — на изображении, полученном радиометром со спутника NOAA-6. В следующем году Дозьеpublished a mathematical method to identify high-temperature areas in satellite data, which became the basis for most classical detection algorithms.

When vegetation burns, a portion of the energy released takes the form of infrared radiation. The burning area releases much more radiation, particularly in the mid-wave band, than does the surrounding area. In satellite imagery, the pixel that contains the burning area registers an increase in "brightness temperature," a measure of the intensity of electromagnetic energy coming from a source. The first wildfire-detection algorithms used fixed thresholds for brightness temperature data to identify likely fires, although these quickly gave way to "contextual" algorithms that adjust their thresholds based on local conditions—compensating for regional and seasonal differences in radiation. Global databases of known hot spots, like gas flares and steel plants, help filter out false detections.

NASA engineers used the experiences with the NOAA-6 satellite to design subsequent instruments used to detect fires from space, including NASA’s Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite, which are deployed on low Earth orbit satellites and typically pass over a given location several times a day. They are complemented by NOAA’s Geostationary Operational Environmental Satellites (GOES), which provide continuous updates at a coarser resolution.

Purpose-built fire satellites like the three launched by SpaceX in July for the California-based nonprofit Earth Fire Alliance (EFA) are further optimized for fire detection. EFA’s "FireSats" have a multiband suite of sensors including two sensitive to mid-wave infrared bands, one of which is attenuated for better detection of extremely hot fires that can saturate other sensors. The suite also includes sensors for visible, near-infrared, and short- and long-wave infrared radiation, allowing fire managers to accurately characterize the entire temperature profile of a fire throughout its life cycle.

Michael Falkowski joined Earth Fire Alliance from NASA, where he served as program manager for the agency’s Wildland Fire Program and led its FireSense project.Earth Fire Alliance

The alliance ultimately aims to deploy more than 50 of its low Earth orbit satellites by the 2030s, which will allow it to image any location on the globe at 20-minute intervals. With an average image resolution of 80 meters per pixel compared to 500 meters for NASA’s Visible Infrared Imaging Radiometer Suite, EFA’s satellites will be able to detect much smaller fires than legacy satellites can—down to around 25 square meters, about the size of a standard shipping container.

"From space, for decades, we’ve been really blind to where these small fires are, and we’re probably drastically underestimating the total amount of global burned area and the total amount of carbon emissions from fires because we don’t have a handle on where these small fires are," says Michael Falkowski, EFA’s lead scientist. "One of the things that our system will enable is the ability to catch these small fires, view them more frequently, and not only improve fire operations but also improve global fire science."

EFA is currently working with a few early adopters who are helping refine its data products and delivery mechanisms; it plans to make those products more widely available to fire agencies and scientific researchers starting next year. Its first three operational satellites broadcast the images they acquire to dedicated ground stations, which then upload them into the cloud for processing and delivery to end users—a sequence of events that takes about 20 minutes from start to finish, according to Falkowski. EFA’s next satellites will be able to transmit images via Starlink to any ground node, cutting delivery times even further, he says.

Because EFA wants to distribute FireSat data to anyone with a legitimate need for it, images from its satellites can be used with both classical fire-detection algorithms and novel machine learning approaches, which have become increasingly popular in recent years for complex vision tasks. The alliance already has a partnership with Google Research to support development of new detection algorithms. These will use AI to compare operational FireSat data with historical images of the same location to detect small fires while minimizing false-positive rates.

OroraTech says one of its wildfire satellites made the first detection of the Rock Fire in an area not visible to ground fire cameras.OroraTech

OroraTech, which has a partnership with EFA to expand access to wildfire data for nongovernmental organizations, is already using AI models in addition to classical detection algorithms. Processing aboard its satellites allows the company to detect fires rapidly, downloading essential details about new detections to ground stations in advance of full images. "It’s important for us to be as fast as possible—we’re talking about minutes," said Dima Rashkovetsky, OroraTech’s team lead for data engineering. "We know from talking to multiple customers that information after an hour is borderline useless for first responders."

Rashkovetsky says OroraTech developed its fire-detection models using supervised machine learning, which entailed manually labeling fires in the images used to train the model. "In the beginning, we prioritized precision, so we wanted to make sure that if we say something is a fire, it is really a fire, even at the expense of sometimes missing some of the smaller fires," he said. The company iterated from there until it could reliably detect more of the smaller fires, while also compressing its machine learning model enough to run on the Nvidia Jetson Xavier NX GPU modules installed in its satellites.

Compared to classical detection algorithms, Rashkovetsky says, AI makes it easier to incorporate contextual information like weather and site history to reduce the rate of false positives. Even so, all fire detections come with some degree of uncertainty, which fire agencies handle in different ways.

"Different customers have different costs of missing a fire," he said, explaining that some agencies would rather deal with false positives than run the risk of missing a fire, while others simply don’t have the resources to chase every alarm, including the occasional false one. OroraTech manages these divergent expectations by developing a confidence score for its detections, based on the confidence of its AI model as well as such factors as fire-weather indices, vegetation data, and the persistence of the detection. Users develop an "intuition" for these confidence scores and can filter their notifications accordingly, says Rashkovetsky.

Most importantly, he adds, OroraTech tries to be as transparent as possible about how the confidence scores were derived, which he sees as essential for building trust in the technology.

"Trust is a major issue in everything remote-sensing related, but specifically also with AI," he said. "People are rightfully not willing to make a decision based on just a black box."

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