References

hbz

Хвойные бореальной зоны

Conifers of the boreal area

1993-0135

СибГУ

10.64430/1993-0135-2026-2-22-51

hbz-412

Research Article

БИОЛОГИЯ И ЭКОЛОГИЯ, ЛЕСНОЕ ХОЗЯЙСТВО

BIOLOGY AND ECOLOGY, FORESTRY

Обнаружение лесных пожаров с использованием технологий глубокого обучения (обзор зарубежной литературы)

Forest fire detection using deep learning technologies (review of foreign literature)

https://orcid.org/0000-0003-4587-8952

Усольцев

В. А.

Usoltsev

V. A.

620100, Екатеринбург, Сибирский тракт, 37

37, Siberian tract, Yekaterinburg, 620100

Usoltsev50@mail.ru

https://orcid.org/0000-0002-9454-9824

Часовских

В. П.

Chasovskikh

V. P.

620144, Екатеринбург, ул. 8 Марта/Народной Воли, 62/45

62/45, 8 Marta str./ Narodnaya Volya, Yekaterinburg, 620144

u2007u@ya.ru

Уральский государственный лесотехнический университетРоссияUral State Forest Engineering UniversityRussian Federation

Уральский государственный экономический университетРоссияUral State University of EconomicsRussian Federation

2026

17062026

4422251

2026

Усольцев В.А., Часовских В.П.

Usoltsev V.A., Chasovskikh V.P.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://hbz.elpub.ru/jour/article/view/412

По мере увеличения объема новых научных данных, новых идей и практических разработок возрастает риск отставания исследователей от текущего состояния науки и ее практических реализаций. Синтез информации в форме систематических обзоров направлен на решение этой проблемы и может значительно облегчить исследователям ориентацию в современном состоянии научной сферы. Стремительный прогресс в области получения научных данных привел к появлению огромного и постоянно растущего объема информации, что исключает возможность какой-либо «ручной» ее интерпретации. Извлекать ценные и представляющие интерес модели из пространственных баз данных сравнительно сложно из-за множества их типов, пространственных связей и автокорреляций по сравнению с обычными числовыми и категориальными данными. В связи с изменением климата по всему миру учащаются лесные пожары, обнаружение которых на ранних стадиях является эффективным способом минимизировать причиняемый ими ущерб. Современные технологии глубокого машинного обучения открывают новые возможности для раннего обнаружения лесных пожаров. Авторы подчеркивают сложность обработки пространственных данных традиционными методами из-за их многообразия и взаимосвязей, и показывают, что алгоритмы глубокого обучения могут эффективно решать эту задачу, помогая минимизировать экономический и экологический ущерб и угрозу человеческим жизням.

As the volume of new scientific data, new ideas and practical developments increases, the risk of researchers lagging behind the current state of science and its practical implementations increases. The synthesis of information in the form of systematic reviews is aimed at solving this problem and can significantly facilitate researchers' orientation in the current state of the scientific field. Rapid progress in the field of obtaining scientific data has led to the emergence of a huge and ever-growing amount of information, which excludes the possibility of any “manual” interpretation. It is relatively difficult to extract valuable and interesting models from spatial databases due to their many types, spatial relationships, and autocorrelations compared to conventional numerical and categorical data. Due to climate change, wildfires are increasing in frequency around the world, and detecting them at an early stage is an effective way to minimize the damage they cause. Modern deep machine learning technologies open up new opportunities for early detection of forest fires. The authors emphasize the complexity of processing spatial data using traditional methods due to their diversity and interrelationships, and show that deep learning algorithms can effectively solve this problem, helping to minimize economic and environmental damage and threat to human lives.

лесные пожарыраннее обнаружениеметодыалгоритмы и моделиглубокое машинное обучение

forest firesearly detectionmethodsalgorithms and modelsdeep machine learning

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The authors declare that there are no conflicts of interest present.