Type of Publication: Article in Journal

Artificial Intelligence for Wildfire Detection and Management

Author(s):

Karger, Erik; Jeppe, Arne; Ziolkowski, Rafael; Korn, Falco; Hackel, Tobias; Harr, Michael Dominic; Brée, Tim; Ahlemann, Frederik

Title of Journal:

Discover Artificial Intelligence

Volume (Publication Date):

6 (2026)

Number of Issue:

304

Digital Object Identifier (DOI):

doi:10.1007/s44163-026-01087-5

Citation:

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Abstract

Motivated by the historical use and increasing relevance of artificial intelligence (AI)in wildfire management, this study reviews the role of AI in wildfire managementthrough a bibliometric study using a dataset of 1,985 peer-reviewed publicationssourced from Scopus. The analysis identifies four thematic clusters: (1) geospatial andclimatic analysis of wildfires using remote sensing and prediction, (2) technologicaland algorithmic advancements for wildfire detection and monitoring, (3) machinelearning–driven wildfire prediction, risk assessment, and behavior modeling. Ourfindings show a multidisciplinary and application-oriented research field withincreasing relevance due to climate change and escalating fire events. Based onour findings, we propose future research directions, including multimodal dataintegration, explainable AI, and real-time human-AI collaboration. This studycontributes to a systematic understanding of current AI approaches in wildfireresearch and supports the development of a targeted research agenda for advancingtechnological and scientific responses to wildland fire challenges.