Highlights What are the main findings? A comprehensive three-decade GeoAI synthesis covering 449 peer-reviewed wildfire studies across 14 wildfire-related tasks. Dominant reliance on RF and CNN-based methods, with limited adoption of transfer learning, transformers, Geo-foundation models, and XAI. What is the implication of the main finding? Major gaps remain in fuel mapping, post-fire recovery, vulnerability assessment, and spatio-temporal wildfire modeling. Future progress requires scalable GeoAI systems, improved transparency, broader geographic representation, and stronger integration of advanced AI methods. Abstract Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-related challenges. However, existing review studies typically focus on specific wildfire tasks and lack a comprehensive synthesis of how geospatial data and supervised AI techniques interact across the full wildfire management cycle. Therefore, this study aims to provide a meta-analysis review of the integration of RS, GIS, and supervised AI methods in wildfire science. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 449 peer-reviewed journal articles published between 1994 and 2024. The review examines various wildfire-related tasks, data sources, algorithmic approaches, spatial scales, performance metrics, and other aspects used in wildfire geospatial AI (GeoAI) studies. The results reveal a strong concentration of research on tasks such as burned area mapping (BAM), wildfire detection, and susceptibility mapping, while critical areas, such as fuel mapping, wildfire vulnerability, and post-fire recovery, remain underexplored. The analysis also identifies a dominant use of traditional machine learning (ML) algorithms, such as Random Forest (RF), and an increasing adoption of deep learning (DL) models, particularly convolutional neural networks (CNNs). Furthermore, the geographic distribution of studies highlights significant global disparities, with most research conducted in high-income regions, while wildfire-prone areas in developing regions remain underrepresented. The review also reveals limited adoption of advanced AI techniques, including transfer learning, transformer architectures, Geo-foundation AI models, and explainable AI (XAI). These findings provide a comprehensive synthesis of GeoAI applications in wildfire management and highlight critical methodological, geographic, and application-level gaps. Addressing these gaps through improved data accessibility, adoption of advanced AI methods, and increased research focus on underrepresented wildfire tasks and regions will be essential for developing scalable, interpretable, and globally applicable wildfire management systems. 1. Introduction Forests are invaluable natural resources that serve as habitats for countless organisms while offering various ecological, economic, and societal benefits [ 1]. These ecosystems are essential in carbon sequestration, air quality enhancement, water regulation, soil stabilization, and biodiversity conservation [ 2, 3]. Additionally, forests contribute to economic development by providing timber and non-timber products, generating employment opportunities, and supporting essential ecosystem services, such as pollination and soil health maintenance [ 4]. They are also vital in mitigating climate change, regulating local temperatures, and safeguarding watersheds [ 2]. Beyond their ecological and economic importance, forests provide spaces for recreation, cultural significance for numerous communities, and mental and physical health benefits [ 5]. Furthermore, they support diverse wildlife and are fundamental to maintaining ecological balance and sustainability [ 1]. Despite their importance, forests are increasingly threatened by wildfires and deforestation, posing significant challenges to their preservation and highlighting the need for advanced monitoring, prediction, and management strategies [ 6]. Wildfires, also known as forest fires, bushfires, or wildland fires, are a significant component of the Earth’s system, occurring year-round globally [ 7]. While they are considered natural disturbances, wildfires are often classified as uncontrolled disasters due to their potential to inflict substantial economic damage, annually destroying millions of acres of land and causing immense losses in human lives, vegetation, and forest resources [ 4, 8, 9]. Natural factors, such as lightning or human activities, including campfires, discarded cigarettes, and deliberate land clearing, can ignite wildfires [ 10]. These fires are fueled by dry vegetation, high temperatures, and strong winds, enabling rapid spread over vast areas [ 11]. Despite their destructive nature, leading to the loss of lives, property, and wildlife habitats, wildfires also serve essential ecological functions in specific ecosystems [ 12]. They clear out dead vegetation, return nutrients to the soil, and stimulate new plant growth [ 13]. However, the increasing frequency and severity of wildfires, driven by climate change, threaten ecosystems, air quality, and human health [ 14]. These challenges highlight the urgent need for effective wildfire management and prevention strategies [ 15]. Moreover, these challenges have accelerated the adoption of advanced geospatial technologies and artificial intelligence (AI) models to improve wildfire monitoring, prediction, and mitigation [ 16]. Wildfires are complex phenomena influenced by five major factors: climate conditions, forest fuels, ignition points, topography, and human activities [ 17]. According to [ 18], the estimated global annual burned area is approximately 420 million hectares, exceeding India’s land area. Moreover, the economic losses caused by wildfires are substantial [ 19]. For example, a single catastrophic wildfire in California claimed 88 lives and affected 18,500 structures, and the total financial cost of this event reached $24 billion [ 20]. This estimate excludes the hidden economic impacts on human health, which translates to a cost of $9.50 per person per day during the wildfire event [ 21]. Therefore, wildfires should not be underestimated, and the adoption of advanced technologies is essential to enhance their management and mitigation. Wildfire management using RS and GIS technologies can be categorized into three phases: “Before,” “During,” and “After” wildfire events. The “Before” phase focuses on wildfire prediction, which involves forecasting the likelihood of wildfire occurrence before ignition [ 26]. This process includes modeling the relationship between wildfire risk and influential factors such as weather conditions, fuel content, and topography [ 27]. For example, [ 28] generated a wildfire risk map to identify areas at greater risk. Similarly, [ 20] identified high-risk zones in Chile, demonstrating the importance of regional-scale risk assessments. Moreover, [ 29] developed a fuel map as part of preparedness efforts to raise awareness about fuel availability within forested areas. The primary goal of these initiatives is to predict when and where wildfires might occur to prevent ignition and limit their spread. The “During” phase focuses on predicting wildfire behavior, including the spatial and temporal evolution of wildfire spread, active fire detection, and smoke detection. Wildfire spread involves understanding how environmental factors such as weather conditions, moisture levels, fuel availability, and human activity influence wildfire behavior [ 30]. Several studies have been conducted in this field, including predicting the rate of spread in response to wind speed [ 31, 32, 33, 34, 35], predicting the spatial progression of the wildfire [ 1, 14, 15, 36, 37, 38, 39, 40], and classifying wildfires based on the extent of land burned [ 41, 42, 43, 44, 45], such as small or large wildfires. Similarly, active fire detection uses space-, aerial-, or ground-based sensors to locate active wildfire hotspots in real time. Several researchers have conducted research in this field [ 46, 47, 48]. These systems can identify high-temperature areas, allowing responders to prioritize containment efforts. On the other hand, smoke detection uses optical sensors and atmospheric monitoring to track smoke plumes, estimate their spread, and predict their impact on air quality and visibility [ 10]. Previous studies in this area have developed methodologies to improve early smoke detection and its integration with fire spread models [ 49, 50, 51, 52, 53, 54]. Although advancements in RS and GIS technologies had revolutionized wildfire-related tasks, a pressing need remained for more accurate and reliable algorithms. This necessity drove significant interest in AI methods, particularly machine learning (ML) and deep learning (DL) algorithms [ 72, 73]. RS data provides extensive observations of forested areas, offering a valuable understanding of every phase of wildfire management [ 74, 75]. On the other hand, AI algorithms require high-quality, large-scale datasets for training, creating a synergy among AI, RS, and GIS. The motivations for employing AI algorithms in forest ecosystem research, including disturbances caused by wildfires, were explored in earlier studies [ 76]. Reference [ 77] further emphasized the potential of ML techniques to model complex ecological problems. With the advent of DL architectures, particularly convolutional neural networks (CNNs) [ 11], these algorithms have become powerful tools for geospatial tasks, especially in wildfire-related domains. Recent advancements, such as transformers [ 78] and attention mechanisms [ 79], further enhanced the capabilities of AI in addressing wildfire challenges. Researchers have continued introducing innovative architectures and intelligent solutions to improve wildfire management by proposing various accurate AI models [ 39, 80, 81, 82]. As a result, geospatial AI (GeoAI), which integrates RS, GIS, and AI, has emerged as a critical framework for addressing complex wildfire challenges across spatial and temporal scales. Therefore, this review paper aims to address these critical gaps by providing a comprehensive and systematic analysis of the integration of RS, GIS, and supervised AI algorithms, including traditional ML and DL, across diverse wildfire management applications. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this study systematically evaluates the current state of GeoAI in wildfire science and identifies areas where these technologies have demonstrated strong performance, as well as domains that remain underexplored. Specifically, this study makes the following contributions: (1) providing a comprehensive synthesis of RS, GIS, and supervised AI applications across all wildfire management phases; (2) conducting a rigorous meta-analysis of 449 peer-reviewed studies published between 1994 and 2024 to identify research trends, methodological developments, and existing gaps; (3) highlighting understudied wildfire applications, including post-fire recovery and vulnerability assessments, and underutilized AI approaches such as transfer learning and explainable AI (XAI); (4) analyzing the global distribution of wildfire studies to reveal geographic disparities and research priorities; (5) examining the most commonly used data sources, features, and algorithmic approaches across wildfire-related tasks; and (6) providing methodological insights and recommendations to support the development of more accurate, scalable, and interpretable GeoAI-based wildfire management systems. Table 1. Summary of existing literature review studies covering wildfire management using geospatial science and supervised AI algorithms. Table 1. Summary of existing literature review studies covering wildfire management using geospatial science and supervised AI algorithms. #Num Title Addressed Issue Gap Ref 2. Literature Review Methodology This systematic review was conducted in accordance with the PRISMA 2020 guidelines [ 93] to improve methodological transparency, reproducibility, and reporting consistency. The review process involved systematic literature identification, screening, eligibility assessment, and inclusion of studies related to GeoAI applications in wildfire science and management. Since this study focused on a structured synthesis and descriptive meta-analysis of peer-reviewed literature rather than intervention-based evidence synthesis, no formal review protocol registration was conducted. A structured, systematic literature search was conducted across two major academic databases, Web of Science and Scopus, to identify studies on the intersection of wildfires, geospatial science, and AI methods. The search strategy was formulated around three distinct keyword categories, as outlined in Table 2. The first category focused on wildfire-related terminology, encompassing expressions such as “wildfire,” “forest fire,” “burned,” and “post-fire” to cover various types and stages of fire-related phenomena. The second category targeted Earth observation (EO) data and geospatial technologies, including terms such as “remote sensing,” “GIS,” “Satellite,” and “Earth observation,” reflecting the spatial and data-driven nature of the analysis. The third category comprised learning-methods keywords, incorporating classical ML and contemporary DL terms such as “Classification,” “Regression,” “CNN,” “Transformer,” “GAN,” and “explainable AI,” to ensure comprehensive coverage of data analysis approaches applied in this domain. The search was limited to peer-reviewed journal articles published in English over a 30-year period from January 1994 to December 2024. The inclusion criteria comprised studies that explicitly addressed wildfire-related applications through the integration of geospatial science (e.g., remote sensing, GIS, or Earth observation data) and learning-based analytical approaches, including ML and DL methods. Conference proceedings, review abstracts, editorials, and non-English publications were excluded to maintain methodological consistency and academic rigor. Studies were also excluded if they focused on unrelated hazards (e.g., floods or landslides) or employed only non-learning-based analytical approaches, such as statistical, deterministic, or rule-based methods. The entire review process followed a transparent and structured protocol, as illustrated in Figure 1. The screening process was conducted through multiple stages following PRISMA guidelines, including title, abstract, and full-text assessments. Any disagreements regarding study eligibility or thematic relevance were resolved through discussion among the authors to maintain consistency in the selection process. In the eligibility assessment phase, full-text articles and accompanying figures were examined in greater detail to evaluate their alignment with the inclusion criteria. Studies that had passed the initial screening but, upon closer inspection, did not meet methodological or thematic requirements were excluded at this stage. These included papers that did not engage with geospatial science, those addressing unrelated hazards such as floods or landslides, and studies that employed only non-learning-based analytical techniques. Specifically, this latter group comprised studies that applied purely statistical models, rule-based systems, or deterministic mathematical algorithms rather than data-driven or learning-based approaches. Following this multi-tiered filtering process, 449 studies were retained for inclusion in the final meta-analysis review. This refined dataset provides a comprehensive and methodologically consistent foundation for further quantitative and thematic analysis within the scope of this review. The keyword groups presented in Table 2 were combined using Boolean operators (AND/OR) to construct the final search queries. Specifically, terms within each group were connected using OR, while the three groups (wildfire, EO data, and learning methods) were combined using AND. The search was applied to the title, abstract, and keyword fields in both Web of Science and Scopus databases. The database search was conducted in January 2025. 3. Results A total of 449 peer-reviewed journal articles met the eligibility criteria described in Section 2. These studies represent a comprehensive body of research that intersects geospatial science, supervised AI techniques, and various wildfire management applications. Based on a structured meta-analysis, the selected literature was categorized to extract several key types of information. This section presents a detailed review of the meta-analysis results. First, the general characteristics of the reviewed papers, including journal distribution and first-author country of affiliation, are discussed to highlight the global spread and publication trends in wildfire research. Next, the data types are analyzed to reveal their prevalence and significance across wildfire tasks. Then, the application of ML and DL methods is reviewed to assess their growing role in spatial and temporal wildfire analysis. Finally, performance metrics are presented. 3.1. General Characteristics of Studies The reviewed studies were distributed across 136 different journals. As shown in Figure 2, the highest number of publications appeared in journals published by the Multidisciplinary Digital Publishing Institute (MDPI), totaling 152 papers. Elsevier followed this with 113 publications and Springer with 46. Taylor & Francis and IEEE contributed 40 and 27 papers, respectively. Additional contributions included 11 papers from Wiley, 9 from Copernic Publications, and 7 from Frontiers. Notably, publishers with at least five publications are illustrated in Figure 2. A closer examination of Figure 2 shows the specific distribution of wildfire-related publications across journals within the MDPI publisher. The Remote Sensing journal accounted for most studies, with 79 publications. The Forests followed this journal with 31 papers and the Fire Journal with 13, the latter dedicated to wildfire research. Applied Sciences and the “Other” MDPI journals contributed six papers each, while the International Society for Photogrammetry and Remote Sensing (ISPRS) International Journal of Geo-Information (ISPRS-IJGI) published five papers. Notably, the “Other” category includes journals with only a single wildfire-related publication: Sensors and Sustainability, which published four papers, and Symmetry and Drones, which published two papers. Among the Elsevier-published journals, Remote Sensing of Environment (RSE) had the highest number of wildfire-related publications, with 14 papers. This was followed by Ecological Informatics (EcoIn) and the International Journal of Applied Earth Observation and Geoinformation (JAG), which published 13 and 10 papers, respectively. Remote Sensing Applications: Society and Environment (RSA) published eight papers, while Ecological Indicators (EcoI) had seven publications. Journals such as Forest Ecology and Management (FEM), Environmental Modelling & Software (EMS), Engineering Applications of Artificial Intelligence (EAAI), and Science of the Total Environment (STE) each contributed six papers. The ISPRS and the Journal of Environmental Management (JEM) published five papers. Moreover, Photogrammetric Engineering & Remote Sensing (PERS) had four publications. Advances in Space Research (ASR) and Agricultural and Forest Meteorology (AFM) each had three papers, and the same was true for Ecological Modelling (EcoM), Science of Remote Sensing (SRS), and Heliyon. The “Other” category, including journals with only one wildfire-related publication, collectively accounted for 17 papers. Within the Springer group, the Fire Ecology (FireEc) and Natural Hazards (NH) journals each contributed the highest number of wildfire-related publications, with five papers each. This was followed by Earth Science Informatics (ESI) and the Journal of Forestry Research (JFR), which published four papers each. Environmental Science and Pollution Research (ESPR) and the Journal of the Indian Society of Remote Sensing (JISRS) each contributed three publications, while Geosciences and Fire Technology (FireTec) each contributed two papers. The “Other” category, comprising Springer journals with only one wildfire-related publication, accounted for 18 papers. For Taylor & Francis publications, the International Journal of Remote Sensing (IJRS) had the highest number of wildfire-related studies, contributing 16 papers. This was followed by Geocarto International (GeoInt) and Geo-Natural Hazards and Risk (GNHR), with five publications each. The Canadian Journal of Remote Sensing (CJRS) published four papers, while Geo-Spatial Information Science (Geo-SIS) and Geographical Research Letters (GRS) published two and three papers, respectively. The “Other” category, including journals with only one wildfire-related publication, collectively accounted for three papers. For IEEE publications, the Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) had the highest number of wildfire-related studies, with 13 papers. This was followed by IEEE Geoscience and Remote Sensing Letters (GRSL) and IEEE Transactions on Geoscience and Remote Sensing (TGRS), each of which contributed six papers. IEEE Access published two papers on wildfire management. As shown in Figure 2, 11 papers were published under Wiley. Among these, Earth’s Future and Geophysical Research Letters (GRL) each published two papers. The remaining journals, including Remote Sensing in Ecology and Conservation (RSEC), Discrete Dynamics in Nature and Society (DDNS), Journal of Geophysical Research: Atmospheres (JGR-A), Ecology and Agriculture (EcoA), Global Ecology and Biogeography Letters (GEBL), Journal of Sensors (JoS), and Advances in Civil Engineering (ACE), each published one paper. As illustrated in Figure 2, Copernic Publications and Frontiers together contributed a total of 16 wildfire-related journal papers. Copernic Publications accounted for nine papers, with Natural Hazards and Earth System Sciences (NHESS) leading at six publications, followed by Geoscientific Model Development (GMD) with two papers and Atmospheric Chemistry and Physics (ACP) with one paper. Similarly, the Frontiers journals published seven papers in total, with Frontiers in Environmental Science (F-EnS) contributing two papers and the remaining five journals, including Frontiers in Information and Communication Technologies (F-ICT), Forests and Global Change (F-FGC), Remote Sensing (F-RS), Earth Science (F-ES), and Plant Science (F-PS), each contributing one. In addition to the journals and publishers that provide an overview of wildfire-related themes, Figure 3 presents the evolution of wildfire-related research tasks from 1994 to 2024, showcasing how scientific interest and research priorities have shifted over time. The figure plots the number of studies published annually across 15 distinct wildfire application categories, ranging from BAM to wildfire vulnerability assessments. BAM appeared as the most studied application among the research tasks, with 94 publications. While only sporadic studies appeared before 2010, interest grew steadily in the following decade, culminating in a sharp increase after 2020. In 2023 and 2024, BAM accounted for 18 and 19 studies, respectively, underscoring the critical importance of accurately mapping fire-affected areas for response and recovery planning. Wildfire detection also gained substantial traction, with 85 studies in total. It followed a similar trend to BAM, with minimal activity before 2010, then steady growth, peaking in 2023 with 25 publications. Similarly, wildfire occurrence studies exploring spatial and temporal wildfire patterns also became increasingly prominent, growing from fewer than five papers before 2015 to 20 in 2024 and 19 in 2023. Moreover, Wildfire Susceptibility reached 13 studies in 2024, making it the fourth-most-studied task, with a total of 57 papers. Other wildfire applications, such as wildfire spread (34 studies), wildfire severity (29), and wildfire risk mapping (WRM) (49 studies), showed consistent but moderate growth over the years. Wildfire probability, danger, and smoke detection, with 11, 10, and 18 publications, respectively, were less frequently studied but still important, and have seen gradual increases in the number of publications over the past decade. Notably, smoke detection experienced a recent spike, reaching six studies in 2024. In contrast, some tasks, such as recovery efforts, wildfire classification, vulnerability assessment, and review studies, remained relatively underrepresented, with fewer than six total studies each. The identified wildfire tasks are grouped into three overarching stages of wildfire management: before fire, during fire, and after fire. While some categories (e.g., wildfire probability, danger, and susceptibility) are conceptually related, they represent distinct modeling objectives and are therefore analyzed separately. It is also important to note that the boundaries between these stages are not always strictly defined. For example, wildfire spread models can be used both before fire events to simulate potential scenarios and during active fires to support real-time decision-making. Notably, the overall number of wildfire-related studies has surged in recent years. Between 2020 and 2024 alone, 383 studies were published across all tasks. The number of publications rose sharply from 38 in 2020 to 43 in 2021, 71 in 2022, 110 in 2023, and 121 in 2024, signaling a growing commitment to wildfire monitoring, prediction, and mitigation using geospatial and supervised learning approaches. To understand the distribution of the reviewed papers worldwide, Figure 4 presents the geographic affiliation of the first authors by continent and country. The results highlight clear global engagement in wildfire-related research, with varying contributions across regions. Among them, Asia stands out as the dominant contributor, accounting for 237 studies, or 53% of the total reviewed papers. Within Asia, China is by far the leading country with 110 studies, followed by Iran (28), India and South Korea (24 each), Turkey (12), Vietnam (9), and Indonesia (9). Europe follows with 96 studies (21% of the reviewed papers) that show robust, distributed engagement. Greece leads with 18 publications, followed by Spain (15), Italy (14), and Germany (11). Portugal and Sweden each contributed eight studies, with Norway (4), France (3), Austria (2), and others making up the rest. Moreover, North America contributed 70 studies (16% of reviewed papers), led by the United States (US) with 49 papers and Canada with 19. Mexico accounted for two studies. Given the frequent occurrence of large wildfires in both the US and Canada, their strong research output aligns with national wildfire management and mitigation priorities. South America produced 25 studies (5% of reviewed papers), predominantly from Brazil, contributing 21. Chile added two studies, while the remaining came from other countries. This regional contribution underscores Brazil’s increasing attention to wildfire threats, particularly in ecosystems such as the Amazon rainforest. Moreover, Africa contributed the fewest, with just 8 studies (2% of the reviewed papers). Morocco led with five publications, while Mozambique, Algeria, and Ethiopia each produced one. Australia also had a notable presence, contributing 13 studies, which account for approximately 3% of the total. 3.2. Study Area Distribution To understand the geographic focus of the reviewed studies, Figure 5 presents the distribution of countries used as study areas across different wildfire-related tasks. The figure highlights which countries have served as primary study areas for tasks such as wildfire detection, spread prediction, susceptibility assessment, severity mapping, recovery efforts, etc. This analysis shows geographic preferences and research trends and reflects regional wildfire challenges, data availability, and institutional research capacity. The US is the most frequently studied region, contributing to 89 studies. It leads to tasks such as wildfire detection (20 studies), wildfire spread (16 studies), BAM (19 studies), and wildfire occurrence (9 studies), indicating a broad and balanced focus on both pre-fire and post-fire analysis. China follows with 77 studies, primarily concentrated on wildfire occurrence (22 studies), WRM (17 studies), and wildfire detection (10 studies). Australia (35 studies), Brazil (33 studies), and Greece (30 studies) are also prominent study areas. Australia’s research is largely focused on wildfire detection and BAM, consistent with its frequent and devastating fire seasons. Brazil, known for its Amazon wildfires, has many studies focused on BAM (11) and wildfire detection (6). Greece notably emphasizes BAM (15 studies), indicating an interest in post-fire damage evaluation. Countries like Iran and India show a more specialized research pattern. Iran is heavily represented in wildfire susceptibility (14 studies) and WRM (7 studies), while India’s research covers wildfire susceptibility (7 studies) and detection (2 studies). Canada and Spain each have 28 studies, with Canada more involved in fuel mapping and wildfire spread, and Spain in burned area and severity mapping. Other countries such as Portugal, Turkey, Vietnam, South Korea, and France have moderate representation, each contributing 10–21 studies. These tend to focus on a narrower set of tasks, often dictated by national priorities or specific wildfire challenges. Several developing countries from Africa, Southeast Asia, and South America (e.g., Mozambique, Cambodia, Chile, and Indonesia) also appear, but with lower frequencies, often limited to one or two studies. In addition to the spatial distribution of the study areas, the scale of analysis across wildfire-related tasks showed a dominant use of local-scale studies. Figure 6 indicates the number of studies across different wildfire-related tasks at various spatial scales. BAM had the highest number of local-scale studies, with 54, followed by wildfire occurrence with 29, and wildfire detection with 23 studies. Wildfire susceptibility, WRM, and wildfire spread were also commonly investigated at the local level, with 27, 23, and 17 studies, respectively. At the provincial and national scales, wildfire susceptibility was studied in 24 studies at the provincial level and three at the national level. Wildfire occurrence was examined in 16 provincial and 13 national studies. Similarly, wildfire detection showed 10 studies at the national level and seven at the provincial scale. WRM, wildfire spread, and wildfire severity were also addressed at both provincial and national scales, though in smaller numbers. On the other hand, fewer studies were conducted at continental and global scales. BAM included five studies at the continental level and 11 at the global scale. Wildfire detection had 17 continental and two global studies. Other tasks, such as smoke detection, wildfire probability, and wildfire severity, had limited representation at these broader scales. Tasks like wildfire classification, recovery efforts, and vulnerability were addressed in a few studies, with most limited to one or two instances across all scales. 3.3. Data Type 3.3.1. Environmental Variables Wildfire is a process in which various parameters interact. This section investigates six main groups of variables: meteorological variables, spectral indices, biophysical variables, static variables, soil variables, and others, as shown in Figure 7. It is important to note that this classification did not include raw data from passive and active sensors, such as optical bands and topographic variables. These data types will be discussed in the following section. The analysis reveals that meteorological variables were the most used geospatial data type across wildfire-related studies, totaling 709 occurrences. Among these, temperature was the most frequently included variable in 201 studies. Precipitation closely followed, with 172 studies citing it. Moreover, wind speed was used in 136 studies, reflecting its impact on wildfire spread direction and velocity. Additionally, humidity appeared in 95 studies, while wind direction, solar radiation, and atmospheric pressure were reported in 37, 32, and 19 studies, respectively. Less commonly used variables such as vapor pressure, sunshine hours, evaporation, heat flux, lightning, cloud cover, snow cover, and transpiration were also included, though their frequencies were relatively low. The second largest group comprised spectral indices, with 339 occurrences, reflecting the growing reliance on RS-derived vegetation and burn metrics in wildfire research. The Normalized Difference Vegetation Index (NDVI) was the most prominent index, featured in 158 studies. Other widely used indices included the Normalized Burn Ratio (NBR) in 36 studies, the Enhanced Vegetation Index (EVI) in 23, and the Normalized Difference Water Index (NDWI) in 22 studies. A variety of other indices were also reported, such as the Normalized Difference Moisture Index (NDMI), Soil Adjusted Vegetation Index (SAVI), Brightness, Burned Area Index (BAI), Visible Atmospherically Resistant Index (VARI), Green Normalized Difference Vegetation Index (GNDVI), Global Environmental Monitoring Index (GEMI), Mid-Infrared Burn Index (MIRBI), and Global Vegetation Moisture Index (GVMI), as well as more complex transformations like the Modified Soil Adjusted Vegetation Index (MSAVI), Tasseled Cap Wetness (TCW), Tasseled Cap Greenness (TCG), and change-based indices such as dNDVI (delta NDVI), dNBR (delta NBR), and dMIRBI (delta MIRBI). This wide range of indices reflects the diversity of spectral signals relevant to various wildfire phenomena, from pre-fire vegetation conditions to post-fire severity assessments. Biophysical and static variables are the most used groups, with 131 occurring similarly. The most used biophysical indicators were evapotranspiration in 21 studies, vegetation type in 17, canopy density in 13, and leaf area index in 12. Other important features included canopy cover, canopy height, drought indices, and forest type, each contributing to fuel structure, biomass, and ecosystem conditions. Several studies also incorporated vegetation cover, fraction, canopy moisture, biomass, forest age, and tree species to understand vegetation’s role in wildfire susceptibility and behavior. Among the static group that provides contextual spatial information, the most common were land cover, present in 69 studies, followed by distance to waterbodies in 41, and geographic coordinates in 6. Other static parameters included distance to forest, albedo, water deficit, lithology, and river density, suggesting that landscape configuration and background geologic or hydrologic features also play a part in wildfire modeling efforts. Although important for understanding moisture retention and vegetation support, soil variables were reported 50 times. Among these, soil moisture was the most frequent, with 30 studies, followed by soil type, texture, fraction, bulk density, and depth. Finally, a few studies (categorized as Other) incorporated specialized variables, such as the Relativized Burn Ratio (RBR) and the Satellite zenith angle (SAZ), which appeared in 3 and 2 studies, respectively. 3.3.2. Satellite Data Figure 8 shows the number of studies that used different types of RS data in wildfire-related research. Among these, active RS was used in 39 studies. Synthetic Aperture Radar (SAR) was the most widely used active data type, appearing in 31 studies. Sentinel-1 (S1) was the most frequently used SAR sensor, with 22 studies, while Shuttle Radar Topography Mission (SRTM) and Advanced Land Observing Satellite—Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) were used in six and three studies, respectively. In addition to SAR, Light Detection and Ranging (LiDAR) data were used in eight studies, highlighting a smaller but notable application of laser-based elevation or vegetation structure data in wildfire studies. Passive RS sources were significantly more common and utilized 509 times. Within this category, multispectral (MS) data dominated, accounting for 504 times. The Moderate Resolution Imaging Spectroradiometer (MODIS) was the most widely used passive sensor, cited in 140 studies. Landsat-8 (L8) appeared in 98 studies, followed by Sentinel-2 (S2) in 79 and UAV-based MS imagery in 46. Several other MS satellite sensors were also represented, including Visible Infrared Imaging Radiometer Suite (VIIRS) in 33 studies, Landsat-7 (L7) in 25, Landsat-5 (L5) in 20, and Satellite Pour l’Observation de la Terre (SPOT) in 10. Himawari-8 and Planet imagery were each used in eight and seven studies, respectively. Other sources, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Very High-Resolution Radiometer (AVHRR), National Oceanic and Atmospheric Administration (NOAA), National Agriculture Imagery Program (NAIP), Landsat-9 (L9), Along-Track Scanning Radiometer (ATSR), Geostationary Operational Environmental Satellite (GOES), Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), WorldView-3, IKONOS, and Project for On-Board Autonomy—Vegetation (PROBA-V), were used in fewer than six studies each. Hyperspectral (HS) data were the least commonly used passive source, appearing in only five studies. Among these, PRecursore IperSpettrale della Missione Applicativa Satellite (PRISMA-Sat) was used in three studies, and Hyperion in two. In addition to raw RS data, 50 studies used ready-to-use products that were not sensor-specific but derived datasets prepared for direct analysis. 3.3.3. Topographic Variables Topographic variables are another important data type frequently incorporated into wildfire-related studies. As shown in Figure 9, elevation was the most used topographic feature, appearing in 190 studies, followed closely by slope in 186 studies and aspect in 160 studies. These three variables form the foundation of terrain analysis in wildfire research due to their significant influence on wildfire behavior and spread patterns. In addition to these core variables, more specialized terrain attributes were utilized less frequently. The Topographic Wetness Index (TWI) was reported in 38 studies, whereas plan curvature was reported in 28 studies. Other variables, such as the Topographic Position Index (TPI), surface roughness, profile curvature, landform, and valley depth, were included in fewer than 10 studies each. This pattern highlights the predominant use of basic elevation-derived parameters in wildfire modeling, with the limited but notable inclusion of more advanced topographic metrics. 3.3.4. Wildfire-Related Variables Wildfire-related variables are another important data type used in wildfire research and are vital in understanding wildfire behavior, fuel characteristics, and the potential for wildfire spread. These variables include indices, fuel types, moisture content, and fire weather parameters that enable the prediction of wildfire danger, intensity, and spread patterns. Figure 10 indicates the frequency of these variables in wildfire studies. The analysis shows that fuel type was the most frequently used wildfire-related variable, appearing in 16 studies, highlighting the importance of understanding the available fuel for wildfire spread. The Fire Weather Index (FWI), a composite indicator of fire danger, appeared in 10 studies. Other key fire indices, such as the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC), were used in nine and seven studies, respectively, all of which contributed to wildfire risk and severity predictions. The Initial Spread Index (ISI) was included in six studies. Fuel moisture content, which indicates the readiness of fuels to ignite, was cited in 5 studies, while other, less common indices such as the Forest Fire Danger Index (FFDI), Fire Radiative Power (FRP), and Energy Release Component (ERC) were cited in three studies each. Other less frequently used variables included Live Fuel Moisture Content (LFMC), Keetch-Byram Drought Index (KBDI), Built-Up Index (BUI), Fuel Flammability (FF), Burning Index (BI), and Dead Fuel Moisture Content (DFMC), each appearing in two studies. Finally, variables such as the Geospatial Composite Burn Index (GeoCBI), Heat Load Index (HLI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were used in only one study. 3.3.5. Anthropogenic Variables Anthropogenic variables represent human-related factors that influence wildfire occurrence and risk. Figure 11 indicates that the most frequently used anthropogenic variable was distance from residential areas, included in 89 studies, followed by distance from roads in 73 studies and land use in 48 studies. Population density was used in 43 studies, reflecting its importance in modeling ignition probability and human impact. Other commonly reported variables included distance from farmland in 11 studies, distance from electrical utilities in 10 studies, distance from railway, road density, and Gross Domestic Product (GDP), each in eight studies. Less frequently used variables were holidays in five studies, distance from recreation areas and urban cover in four studies each, livestock density in three studies, and both urban area density and night light intensity, each in two studies. 3.3.6. Ground Truth Source Ground truth is an important element in supervised learning and directly affects the model’s learning process. For wildfire applications, ground-truth data collection methods vary significantly across tasks, as shown in Figure 12. Ready-to-use datasets were the most frequently used and applied in 167 studies. These datasets typically consist of satellite fire products, such as MODIS and VIIRS active fire detections, providing researchers with convenient, large-scale data coverage. The second most common source was organizational data, used in 92 studies and often provided by environmental monitoring agencies such as Natural Resources Canada (NRCan). Expert interpretation was employed in 88 studies involving visual assessments or manual labeling performed by specialists. Benchmark datasets, often used for model evaluation and comparison, were utilized in 65 studies. Field surveys, known for their high accuracy and detail, were used in 45 studies. Simulation-based data (primarily used in wildfire spread prediction tasks) and thermal UAV sensors were used less frequently in two studies, whereas other unspecified methods were used in four studies. Different wildfire tasks showed varying preferences for data-collection methods. BAM relied heavily on expert interpretation, with 36 studies using it, 28 relying on ready-to-use data, and 13 using benchmark datasets. Wildfire occurrence tasks most used ready-to-use data, found in 41 studies, followed by organizational data in 13 studies. Wildfire detection frequently relied on benchmark datasets and expert interpretation used in 25 and 28 studies, respectively. Wildfire susceptibility and WRM were supported by a balanced approach that combined field surveys and organizational data, highlighting the need for accurate environmental information. In wildfire severity assessment, 13 studies used field surveys, while seven relied on expert interpretation, reflecting the need for detailed post-fire condition data. Wildfire spread prediction studies showed a more even distribution of data sources and were among the few tasks that used simulation-based data and UAV thermal imagery. Benchmark datasets and expert interpretation primarily supported smoke detection. More specialized tasks, such as fuel mapping, wildfire probability, danger rating, recovery, vulnerability, and wildfire classification, were informed by limited studies using field, expert, or organizational sources. 3.4. AI Applications Supervised learning algorithms have been widely adopted in wildfire research, with their applications generally falling into three main categories: classification, regression, and a combination of both. Figure 13 shows the number of studies for each group. Most wildfire-related AI studies focused on classification tasks, with 382 studies falling into this category. These tasks typically involve predicting fire occurrence, mapping burned areas, or identifying fire-prone regions using categorical labels. In contrast, regression-based functions, which aim to predict continuous variables such as wildfire spread rate, fuel moisture, etc., were addressed in 39 studies. A smaller subset of 25 studies employed a hybrid approach, combining classification and regression models to address complex wildfire problems that require both discrete and continuous outputs. Examining the types of learning frameworks and algorithmic approaches used across classification and regression tasks helps better understand how AI has been applied in wildfire research. Therefore, Table 3 presents the number of studies under each combination. Most studies relied on supervised learning, reflecting the common availability of labeled datasets in wildfire applications. Supervised learning dominated classification tasks, with ML approaches used in 236 studies and DL in 156. A smaller number of studies employed unsupervised learning, including eight with ML and five with DL, typically used in tasks without predefined labels. Semi-supervised learning, which uses labeled and unlabeled data, was less common, with just two DL-based classification studies. In regression-focused studies, supervised learning again led, with 36 ML-based and 27 DL-based studies. Only one semi-supervised DL study was identified for regression tasks. This breakdown highlights the preference for supervised learning across wildfire studies and a growing adoption of DL techniques, particularly in classification tasks. 3.4.1. Traditional ML in Wildfire Traditional ML algorithms have been widely adopted in wildfire research, supporting various applications from prediction to post-fire assessment. Across all wildfire-related tasks, a total of 514 algorithmic uses were identified, illustrating the broad integration of ML in this field ( Figure 14). RF was the most frequently applied algorithm, used in 125 studies. It was most used in wildfire susceptibility mapping, with 24 studies, followed by 25 in wildfire occurrence prediction and 21 in BAM. Artificial neural networks (ANNs) were used in 83 studies overall, with 17 applied to susceptibility tasks, 12 to occurrence prediction, 12 to BAM, and additional applications across wildfire spread, WRM, recovery efforts, and vulnerability assessments. Support vector machine (SVM) was adopted in 84 studies, including 21 in susceptibility mapping, 19 in BAM, and 14 in occurrence prediction. Boosting algorithms such as Adaptive Boosting (AdaBoost) and Gradient Boosting were used in 88 studies, including 33 for susceptibility, 17 for occurrence, 13 for WRM, and 9 for BAM and wildfire detection. Logistic regression (LR) was employed in 47 studies, most often for occurrence prediction, with 14 studies, followed by nine in susceptibility, eight in BAM, and eight in detection. Decision tree (DT) appeared in 32 studies, with frequent application in occurrence and susceptibility tasks. K-Nearest Neighbors (KNNs) were used in 14 studies, most of which were in occurrence and detection. Naive Bayes (NB) was applied in 16 studies in susceptibility, occurrence, and detection tasks. Generalized Linear Models (GLMs) and Multivariate Adaptive Regression Spline (MARS) were used in six studies, mainly in susceptibility and WRM. Maximum Entropy (MaxEnt) and maximum likelihood were used in five and seven studies, respectively, generally for BAM and occurrence prediction. Less commonly used methods included Bagging Trees, Fisher Discriminant Analysis (FDA), and Spectral Angle Mapper (SAM), each applied in two studies, mostly in susceptibility or detection. Some wildfire tasks had notably fewer ML applications. Wildfire spread involved seven studies using ANN and four using RF. Fuel mapping relied on two ANN and two RF studies. Wildfire danger included three ANN and two RF studies. Moreover, recovery efforts, smoke detection, and wildfire vulnerability each involved 2 ANN-based studies. 3.4.3. Explainable AI (XAI) Researchers have adopted XAI techniques and feature-importance methods to enhance the interpretability of DL and traditional ML models in wildfire-related applications. These approaches play a critical role in identifying which input variables most influence the model’s decisions, thereby supporting transparency, trust, and informed decision-making. As shown in Figure 18, a wide range of methods have been used to assess feature relevance and model behavior. The Gini index was the most frequently used method in 19 studies. SHAP (SHapley Additive exPlanations) was applied in 14 studies, while coefficient analysis was used in 13 studies. Both the variant inflation factor and permutation importance methods were employed in 12 studies, followed by tolerance analysis in 11 studies. 10 studies used XAI or feature importance techniques but did not specify the method. Attention weights and correlation analysis were reported in eigh
