This layer shows potential fire locations identified on satellite imagery by the NOAA Hazard Mapping System (HMS) that are deemed to be associated with biomass burning, including wildfires, prescribed and agricultural fires. This is a blended product composed of fire detection data from GOES/ABI, the JPSS/VIIRS and EOS/MODIS sensors. A quality control procedure is performed using both machine- and analyst-based data screening, thereby discarding detections associated with industrial activity (ex., steel mills, gas flares, power plants) as well as potential false alarms caused by solar panels and other highly reflective surfaces, while also correcting for potential omission errors in the automated satellite fire products. A new daily map is typically initiated around 7-8am Eastern Time, and updated multiple times until the next morning as data becomes available. The information on fire position should be used as general guidance and for strategic planning. Tactical decisions, such as the activation of a response to fight these fires and evacuation efforts, should not be made without other information to corroborate the fire's existence and location. Users should note:The initial HMS product for the current day is created and updated by a satellite analyst roughly between 8am and 10am Eastern Time. After 10am, the analysis is fine-tuned as time permits as additional satellite data becomes available. Areas of smoke are analyzed and added to the analysis during daylight hours as visible satellite imagery becomes available. The product is finalized and "completed" for the archive the following morning - generally by around 8:00am.The fire sizes depicted in the product are primarily determined by the field of view of the satellite instrument, or the resolution of the analysis tool. They should not be used to estimate specific fire perimeters.The ability to detect fires and smoke can be compromised by many factors, including cloud cover, tree canopy, terrain, the size of the fire or smoke plume, the time of the day, etc. The satellite sensors used to detect fires are sensitive to heat sources and reflected sunlight. Analysts do their best to distinguish between fires and other heat sources or highly reflective surfaces, such as factories, mines, gas flares, solar panels, clouds, etc. but some false detects do get included in the analysis.Email your questions to the HMS fire team at: ssdfireteam@noaa.gov

The Satellite Services Division of NESDIS/NOAA created an interactive Web-based GIS used to display satellite data of fire detects in near-real time. It converts the data to a format compatible with ArcIMS and creates a steady flow of the data to the Web Server. The Web Server updates the data being displayed on the internet by deleting the old data and displaying new data. The analyzed fires and smoke layer are update primarily between the hours of 1 pm and 11 pm Eastern time, with occasional updates during the rest of the day. The automated layers are updated twenty-four hours a day as new satellite imagery is received. The product covers the continental United States, Alaska and Hawaii.

This is a data set of United States population and wildland fire smoke spatial and temporal coincidence beginning in 2010 and continuing through 2019. It combines data from the National Oceanic and Atmospheric Administration (NOAA) Office of Satellite and Product Operations Hazard Mapping System’s Smoke Product (HMS-Smoke) with U.S. Census Block Group Population Centers to estimate a potential exposure to light, medium, and heavy categories of wildfire smoke.The data represents a modest advancement of NOAA's HMS-Smoke product, with the aims of spurring additional work on the impacts of wildfire smoke on the health of US Populations. Namely, these should include tracking potential wildfire smoke exposures to identify areas and times most heavily impacted by smoke, adding potential smoke exposures to population characteristics describing the social determinants of health in order to better distribute resources and contextualize public health messages and interventions, and combining information specific to wildfire smoke with other air pollution data to better isolate and understand the contribution of wildfires to poor health.

This layer shows potential fire locations identified on satellite imagery by the NOAA Hazard Mapping System (HMS) that are deemed to be associated with biomass burning, including wildfires, prescribed and agricultural fires. This is a blended product composed of fire detection data from GOES/ABI, the JPSS/VIIRS and EOS/MODIS sensors. A quality control procedure is performed using both machine- and analyst-based data screening, thereby discarding detections associated with industrial activity (ex., steel mills, gas flares, power plants) as well as potential false alarms caused by solar panels and other highly reflective surfaces, while also correcting for potential omission errors in the automated satellite fire products. A new daily map is typically initiated around 7-8am Eastern Time, and updated multiple times until the next morning as data becomes available. The information on fire position should be used as general guidance and for strategic planning. Tactical decisions, such as the activation of a response to fight these fires and evacuation efforts, should not be made without other information to corroborate the fire's existence and location. Users should note:The initial HMS product for the current day is created and updated by a satellite analyst roughly between 8am and 10am Eastern Time. After 10am, the analysis is fine-tuned as time permits as additional satellite data becomes available. Areas of smoke are analyzed and added to the analysis during daylight hours as visible satellite imagery becomes available. The product is finalized and "completed" for the archive the following morning - generally by around 8:00am.The fire sizes depicted in the product are primarily determined by the field of view of the satellite instrument, or the resolution of the analysis tool. They should not be used to estimate specific fire perimeters.The ability to detect fires and smoke can be compromised by many factors, including cloud cover, tree canopy, terrain, the size of the fire or smoke plume, the time of the day, etc. The satellite sensors used to detect fires are sensitive to heat sources and reflected sunlight. Analysts do their best to distinguish between fires and other heat sources or highly reflective surfaces, such as factories, mines, gas flares, solar panels, clouds, etc. but some false detects do get included in the analysis.Email your questions to the HMS fire team at: ssdfireteam@noaa.gov

This layer shows the areas with observed smoke associated with fires using the latest satellite imagery from the NOAA Hazard Mapping System (HMS). The smoke analysis is based on visual classification of plumes using GOES-16 and GOES-18 ABI true-color imagery available during the sunlit part of the day. A new daily map is initiated around 7-8 Eastern Time although since the analysis generally requires multiple satellite images to help distinguish smoke from clouds and other atmospheric aerosols, the first smoke analysis for the current day is usually produced around the local noon time – until then, only fire detection points may be available. Additional smoke analysis and updates will occur throughout the day until sunset or as observation conditions permit.Smoke attributes carry the start and end times (in Universal Time Coordinated - UTC) of the satellite image sequence used to outline the smoke polygon, the corresponding satellite from which the image was derived, and the plume density. The density information is qualitatively labeled as light, medium, and heavy based on the apparent thickness (opacity) of the smoke in the satellite imagery. Those three distinct groups are meant to approximate smoke concentrations ranging between 0-10, 10-21, and 21-32 micrograms per cubic meter, respectively, although no guarantee is made about the actual smoke density measured in the atmospheric column surrounding those areas. Email your questions to the HMS fire team at: ssdfireteam@noaa.gov

Impacts of fire smoke plumes on regional air quality, 2006–2013 data. Shape files of smoke plumes that define the geographic extent of smoke are from the NOAA Hazard Mapping System (HMS), and O3, total PM2.5, and PM2.5 constituent measurements for 2006–2013 are from the U.S. Environmental Protection Agency’s (EPA) Air Quality System database. This dataset is associated with the following publication: Larsen, A., B. Reich, M. Ruminski, and A. Rappold. Impacts of wildfire smoke plumes on regional air quality, 2006-2013. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 28(4): 319-327, (2018).

Global Environmental Hazard Mapping Systems Market is segmented by Application (Land-Use Planning_Emergency Response_Environmental Permitting_Community Engagement_Industrial Siting), Type (GIS Hazard Layers_Chemical Plume Modelling_Flood And Wildfire Risk Maps_Noise And Emission Overlays_Site Contamination Heatmaps), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Seasonal-mean concentrations of particulate matter with diameters smaller than 2.5 μm (PM2.5) have been decreasing across the United States (US) for several decades, with large reductions in spring and summer in the eastern US. In contrast, summertime-mean PM2.5 in the western US has not significantly decreased. Wildfires, a large source of summertime PM2.5 in the western US, have been increasing in frequency and burned area in recent decades. Increases in extreme PM2.5 events attributable to wildland fires have been observed in wildfire-prone regions, but it is unclear how these increases impact trends in seasonal-mean PM2.5. Using two distinct methods, (1) interpolated surface observations combined with satellite-based smoke plume estimates and (2) the GEOS-Chem chemical transport model (CTM), we identify recent trends (2006–2016) in summer smoke, nonsmoke, and total PM2.5 across the US. We observe significant decreases in nonsmoke influenced PM2.5 in the west..., , , # Spatially interpolated non-smoke and smoke PM2.5 concentrations for the US from 2006-2023

https://doi.org/10.5061/dryad.k0p2ngfhv

Description of the data and file structure

We estimated daily smoke and non-smoke PM2.5 across the contiguous US (CONUS) for 2006-2023 using Environmental Protection Agency (EPA) ground monitors and NOAA Hazard Mapping System (HMS) smoke polygons.

The daily PM2.5 data from EPA ground monitors are interpolated to ~15 km resolution to create a total PM2.5 estimate. The HMS smoke polygons are then used to identify locations where there is likely smoke somewhere in the atmospheric column. The seasonal mean or median background is calculated using pixels within the season where an HMS (dense, medium, or light) smoke polygon is not located. The seasonal background can then be subtracted from the total PM2.5 to estimate smoke PM2.5.

In recent years, an active fire season has resulted in some regions having HMS...,

Wildland fire event polygons for 2004-2017 reconciled in SmartFire 2 for the EPA Air Quality Times Series (EQUATES) modeling project (https://doi.org/10.1016/j.dib.2023.109022). These event polygons represent a combination of properties from a collection of remotely sensed and ground-based fire activity datasets. The primary underlying fire activity datasets for the fire event polygons are the Hazard Mapping System (HMS) remote sense fire product (https://www.ospo.noaa.gov/Products/land/hms.html), SIT-ICS/209 Incident Reports (https://www.wildfire.gov/application/sit209), GeoMAC Fire Event polygons (https://data-nifc.opendata.arcgis.com/datasets/nifc::historic-perimeters-combined-2000-2018-geomac/about), and the Monitoring Trends in Burn Severity (MTBS) burn scar event perimeters (https://www.mtbs.gov/direct-download). This dataset includes events identified as over wildland and does not contain biomass burning events over agricultural areas, such as crop residue field burns. Additionally, certain grass fires, such as the annual prescribed fires in the Flint Hills region, have been removed for inclusion in a separate processing stream. Some minor updates have been made to the dataset since the publishing of the EQUATES emission inventories including removal of known errors related to issues in the underlying activity. This dataset is associated with the following publication: Beidler, J., G. Pouliot, and K. Foley. 2004-2017 Geospatial Dataset of Wild and Prescribed Fire Activity Over the Conterminous United States. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 56: 110856, (2024).

Linked respiratory prescriptions for children, along with information on birth and MSA of birth, and smoke days assigned through pregnancy. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Access to underlying health data may be purchased through MarketScan. Smoke data (Hazard Mapping System) is available through NOAA. Format: Data is in csv and R dataset formats. This dataset is associated with the following publication: Jardel, H., K. Rappazzo, T. Luben, C. Keeler, B. Staley, C. Ward-Caviness, C. O'Lenick, M. Rebuli, Y. Xi, M. Hernandez, A. Chelminski, i. jaspers, A. Rappold, and R. Dhingra. Gestational and postnatal exposure to wildfire smoke and prolonged use of respiratory medications in early life. Environmental Research: Health. IOP Publishing, BRISTOL, UK, 2: 045004, (2024).

Two specific fires from 2011 are tracked for local to regional scale contribution to ozone (O3) and fine particulate matter (PM2.5) using a freely available regulatory modeling system that includes the BlueSky wildland fire emissions tool, Spare Matrix Operator Kernel Emissions (SMOKE) model, Weather and Research Forecasting (WRF) meteorological model, and Community Multiscale Air Quality (CMAQ) photochemical grid model. The modeling system was applied to track the contribution from a wildfire (Wallow) and prescribed fire (Flint Hills) using both source sensitivity and source apportionment approaches. The model estimated fire contribution to primary and secondary pollutants are comparable using source sensitivity (brute-force zero out) and source apportionment (Integrated Source Apportionment Method) approaches. Model estimated O3 enhancement relative to CO is similar to values reported in literature indicating the modeling system captures the range of O3 inhibition possible near fires and O3 production both near the fire and downwind. O3 and peroxyacetyl nitrate (PAN) are formed in the fire plume and transported downwind along with highly reactive VOC species such as formaldehyde and acetaldehyde that are both emitted by the fire and rapidly produced in the fire plume by VOC oxidation reactions. PAN and aldehydes contribute to continued downwind O3 production. The transport and thermal decomposition of PAN to nitrogen oxides (NOX) enables O3 production in areas limited by NOX availability and the photolysis of aldehydes to produce free radicals (HOX) causes increased O3 production in NOX rich areas. The modeling system tends to overestimate hourly surface O3 at routine rural monitors in close proximity to the fires when the model predicts elevated fire impacts on O3 and Hazard Mapping System (HMS) data indicates possible fire impact. A sensitivity simulation in which solar radiation and photolysis rates were more aggressively attenuated by aerosol in the plume reduced model O3 but does not eliminate this bias. A comparison of model predicted daily average speciated PM2.5 at surface rural routine network sites when the model predicts fire impacts from either of these fires shows a tendency toward overestimation of PM2.5 organic aerosol in close proximity to these fires. The standard version of the CMAQ treats primarily emitted organic aerosol as non-volatile. An alternative approach for treating organic aerosol as semi-volatile resulted in lower PM2.5 organic aerosol from these fires but does not eliminate the bias. Future work should focus on modeling specific fire events that are well characterized in terms of size, emissions, and have extensive measurements taken near the fire and downwind to better constrain model representation of important physical and chemical processes (e.g. aerosol photolysis attenuation and organic aerosol treatment) related to wild and prescribed fires. This dataset is associated with the following publication: Baker, K., M. Woody, G. Tonnesen, B. Hutzell, H. Pye, M. Beaver, G. Pouliot, and T. Pierce. Contribution of regional-scale fire events to ozone and PM2.5 air quality estimated by photochemical modeling approaches. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 140: 539–554, (2016).

Smoke from agricultural fires is a potentially important source of fine particulate matter (PM2.5) in the US. Sugarcane is burned in Florida to facilitate the harvesting process, with the majority of these fires occurring in the Everglades Agricultural Area (EAA), where there is only one regulatory air quality monitor. During the 2022–2023 sugarcane burning season (October–May), we used public low-cost PurpleAir sensors, regulatory monitors, and 29 PurpleAir sensors deployed for this study to quantify PM2.5 from agricultural fires. We found satellite imagery is of limited use for detecting smoke from agricultural fires in Florida due to the cloud cover, overnight smoke, and the fires being small and short-lived. For these reasons, surface measurements are critical for capturing increases in PM2.5 from smoke, and we used multiple smoke-identification criteria. During the study period, median 24-hour PM2.5 concentrations increased by 2.3–6.9 µg m-3 on smoke-impacted days compared to unimp..., To track the near-source transport of smoke from fires in Florida, we used the NOAA HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. This model is commonly used to compute the trajectory of air parcels. We calculated 12-hour forward trajectories initiated from all HMS fire hotspots in southern Florida (-81.5o x -80o; 26o x 27.5o) during the campaign (October 2022 - September 2023). The HMS fire hotspot product combines detections from the Geostationary Operational Environmental Satellite - R Series (GOES-R)/ Advanced Baseline Imager (ABI) Fire Detection product and the Joint Polar Satellite System (JPSS)/ Visible Infrared Imaging Radiometer Suite (VIIRS) products. We used meteorological data from the NOAA High-Resolution Rapid Refresh (HRRR) model (Dowell et al., 2022), which provides conditions every 4 hours. The HYSPLIT model produces trajectory locations for every hour; however, we interpolated between the reported locations to provide 10-minute observat..., # Model output tracking smoke from agricultural fires in south Florida from October 2022 - May 2023

Dataset DOI: 10.5061/dryad.70rxwdc9k

Description of the data and file structure

This dataset includes monthly gridded output from the National Oceanic and Atmospheric Administration's Hybrid Single-Particle Lagrangian Integrated Trajectory (NOAA HYSPLIT) model. The model was initiated from every NOAA Hazard Mapping System fire hotspot that was detected by the Geostationary Operational Environmental Satellite - R Series (GOES-R)/ Advanced Baseline Imager (ABI) Fire Detection product and the Joint Polar Satellite System (JPSS)/ Visible Infrared Imaging Radiometer Suite (VIIRS) products. We used meteorological data from the NOAA High-Resolution Rapid Refresh (HRRR) model (Dowell et al., 2022), which provides conditions every 4 hours. The HYSPLIT model produces trajectory locations for every hour; however, we interpolated between the reported locations to prov...,

This dataset contains aggregated measurements of daily wildfire-specific fine particulate matter (PM2.5) concentrations at the census tract level in California from 2006 to 2020. Similar data at the zip code level was first described in a study by Aguilera et al. (2023): "A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006-2020)," published in Environment International. We used monitoring data and statistical techniques to estimate daily wildfire PM2.5 concentrations at each census tract population-weighted centroid across California. Input data included monitored PM2.5 concentrations and a wide range of predictors for PM2.5, such as aerosol optical depth, land cover, and meteorological conditions, to estimate daily concentrations of PM2.5. We also used the National Oceanic and Atmospheric Administration Hazard Mapping System and fire perimeter data from CalFIRE to isolate daily wildfire smoke PM2.5 from total PM2.5.

Wildfire smoke is frequently present over the U.S. during the agricultural growing season and will likely increase with climate change. Studies of smoke impacts have largely focused on air quality and human health; however, understanding smoke’s impact on photosynthetically active radiation (PAR) is essential for predicting how smoke affects plant growth. We compare surface shortwave irradiance and diffuse fraction (DF) on smoke-impacted and smoke-free days from 2006-2020 using data from multifilter rotating shadowband radiometers at ten U.S. Department of Agriculture (USDA) UV-B Monitoring and Research Program stations and smoke plume locations from operational satellite products. On average, 20% of growing season days are smoke-impacted, but smoke prevalence increases over time (r = 0.60, p < 0.05). Smoke presence peaks in the mid- to late growing season (i.e., July, August), particularly over the northern Rocky Mountains, Great Plains, and Midwest. We find an increase in the distribution of PAR DF on smoke-impacted days, with larger increases at lower cloud fractions. On clear-sky days, daily average PAR DF increases by 10 percentage points when smoke is present. Spectral analysis of clear-sky days shows smoke increases DF (average: +45%) and decreases total irradiance (average: -6%) across all six wavelengths measured from 368-870 nm. Optical depth measurements from ground and satellite observations both indicate that spectral DF increases and total spectral irradiance decreases with increasing smoke plume optical depth. Our analysis provides a foundation for understanding smoke’s impact on PAR, which carries implications for agricultural crop productivity under a changing climate. Methods This dataset contains information on surface-level photosynthetically active radiation, smoke plume location, aerosol optical depth, and cloud fraction from four publicly available sources:

U.S. Department of Agriculture's UV-B Monitoring and Research Program (UVMRP) National Oceanic and Atmospheric Administration/National Enviromental Satellite, Data, and Information Service's Hazard Mapping System (HMS) Smoke Product National Aeronautics and Space Administration's Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth Product (MCD19A2) National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere L3 Daily Product (MOD08_D3, MYD08_D3)

The dataset covers 10 UVMRP stations located across the contiguous U.S.:

Davis, California Pullman, Washington Pawnee, Nunn, Colorado Poplar, Montana Fargo, North Dakota Billings, Oklahoma Grand Rapids, Minnesota Bondville, Illinois Starkville, Mississippi Geneva, New York

These sites were selected to provide broad spatial coverage of the regions analyzed in the Brey et al. (2018) smoke climatology, capture much of the smoke variability across the U.S., align with agricultural areas, and reduce the impact of metropolitan air pollution. The UVMRP staff were instrumental in providing the underlying UVMRP data and advise on working with the data. Extensive cleaning was conducted to remove data anomalies, quality control issues, and high solar zenith angles (> 75 degrees). Additional processing of underlying records created additional factors, such as average diffuse fraction, used for analysis. We also averaged values to a daily resolution. A detailed description of the site selection, data cleaning, and data processing methods used to produce this final merged dataset are available in the article by Corwin et al. entitled "Smoke-driven changes in photosynthetically active radiation during the U.S. agricultural growing season."

The NPMS Public Map Viewer allows the general public to view maps of transmission pipelines, LNG plants, and breakout tanks in one selected county. Distribution and Gathering systems are not included in NPMS. Users are permitted to print maps of the data, but the data is not downloadable.Always contact Dig Safely at 811 before digging. Visit Call Before You Dig for more information.

Avalanche Hazard Mapping via Satellite Market Outlook

According to our latest research, the global avalanche hazard mapping via satellite market size reached USD 1.02 billion in 2024. The market is experiencing robust expansion, propelled by heightened demand for advanced natural disaster monitoring solutions. As of 2025, the market is set to grow at a compound annual growth rate (CAGR) of 8.9% during the forecast period, with the market expected to reach USD 2.16 billion by 2033. This impressive growth is primarily attributed to the integration of cutting-edge satellite technologies for real-time avalanche risk assessment and the increasing frequency of extreme weather events linked to climate change.

The growth trajectory of the avalanche hazard mapping via satellite market is underpinned by the urgent need for precise, large-scale, and timely hazard assessments in mountainous regions worldwide. With climate change leading to unpredictable snowfall patterns and heightened avalanche risks, governments and private stakeholders are investing in satellite-based mapping systems to improve disaster preparedness and response. The integration of advanced technologies such as optical imaging, synthetic aperture radar (SAR), and LiDAR has significantly enhanced the accuracy and reliability of avalanche detection, enabling authorities to identify vulnerable zones and implement effective mitigation strategies. The increasing adoption of these systems by disaster management agencies, coupled with rising public awareness about the devastating impacts of avalanches, is expected to drive sustained market growth throughout the forecast period.

Another critical growth factor is the expanding application scope of satellite-based avalanche mapping beyond traditional disaster management. Environmental monitoring organizations and infrastructure planners are leveraging these technologies to assess snowpack stability, monitor environmental changes, and ensure the safety of transportation and energy infrastructure in high-risk regions. The continuous improvement in satellite resolution, data processing algorithms, and cloud-based analytics platforms has made it possible to deliver near real-time hazard assessments, which are essential for early warning systems and proactive risk management. Furthermore, the increasing availability of commercial satellite imagery and open-source geospatial data has democratized access to avalanche hazard mapping tools, fostering innovation and collaboration among research institutes, commercial enterprises, and government agencies.

The regional outlook for the avalanche hazard mapping via satellite market reveals significant growth opportunities across both developed and emerging economies. Europe and North America currently dominate the market due to their advanced satellite infrastructure, strong governmental support for disaster risk reduction, and high prevalence of avalanche-prone areas such as the Alps and the Rocky Mountains. However, the Asia Pacific region is witnessing the fastest growth, driven by increasing investments in satellite technology and heightened awareness of avalanche risks in the Himalayas and other mountainous areas. Latin America and the Middle East & Africa are also emerging as potential markets, supported by international collaborations and capacity-building initiatives aimed at enhancing regional disaster resilience.

Technology Analysis

The technology segment of the avalanche hazard mapping via satellite market is characterized by rapid advancements and diversification, with optical imaging, synthetic aperture radar (SAR), and LiDAR emerging as the core technologies driving market growth. Optical imaging remains a foundational technology, providing high-resolution visual data that enables the identification of snow cover, terrain features, and potential avalanche paths. The evolution of multispectral and hyperspectral sensors has further improved the detection of subtle changes in snowpack conditions, which are crucial for early warning systems. However, optical imaging is often limited by weather conditions and daylight availability, prompting the need for complementary technologies.

Synthetic aperture radar (SAR) has gained significant traction due to its ability to penetrate cloud cover and operate effectively under all weather conditions, including at night. SAR technology enables the detection of snow depth, snow water equivalent, and ground movement, makin

Hazard Anticipation Map Market Outlook

According to our latest research, the global Hazard Anticipation Map market size reached USD 2.18 billion in 2024, with a robust compound annual growth rate (CAGR) of 11.2% projected through the forecast period. By 2033, the market is expected to attain a value of USD 6.28 billion, driven by increasing investments in disaster preparedness, growing urbanization, and the rising frequency of natural and anthropogenic hazards. The market’s growth is underpinned by the integration of advanced geospatial technologies, artificial intelligence, and real-time data analytics, which are fundamentally transforming how governments and enterprises anticipate and mitigate risks.

One of the primary growth factors for the Hazard Anticipation Map market is the escalating demand for advanced disaster management solutions. As climate change intensifies and severe weather events become more frequent, governments and organizations worldwide are recognizing the critical importance of proactive hazard anticipation. The deployment of sophisticated mapping technologies enables real-time monitoring and early warning, significantly reducing loss of life and property. Additionally, the increasing adoption of IoT and sensor-based networks enhances the granularity and accuracy of data inputs, further strengthening the efficacy of hazard anticipation maps in predicting and managing disasters.

Another significant driver is the rapid urbanization occurring in both developed and emerging economies. Urban centers are increasingly vulnerable to a variety of hazards, including floods, earthquakes, industrial accidents, and infrastructure failures. The need for resilient urban planning and infrastructure protection is fueling investments in hazard anticipation mapping solutions. These systems empower city planners and local authorities to identify high-risk zones, facilitate emergency response planning, and optimize resource allocation. The integration of AI-driven predictive analytics is also enabling more dynamic and adaptive hazard mapping, allowing for better anticipation of evolving risks in complex urban environments.

Furthermore, the growth of the Hazard Anticipation Map market is propelled by regulatory mandates and heightened awareness regarding environmental sustainability. Governments across the globe are enacting stringent regulations that require organizations to implement comprehensive risk assessment and mitigation strategies. As a result, enterprises across sectors such as manufacturing, transportation, and utilities are leveraging hazard anticipation maps to ensure compliance, protect assets, and maintain business continuity. The increasing collaboration between public agencies, private enterprises, and research institutions is also fostering innovation and expanding the market’s reach, with a focus on developing scalable, interoperable, and user-friendly mapping solutions.

From a regional perspective, North America currently leads the market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to robust investments in disaster management infrastructure, a high level of technological adoption, and the presence of key market players. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid urbanization, frequent natural disasters, and increasing government focus on risk mitigation. Europe’s market is characterized by strong regulatory frameworks and cross-border collaborations aimed at enhancing regional resilience. Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and international aid initiatives.

Component Analysis

The Component segment of the Hazard Anticipation Map market is broadly categorized into Software, Hardware, and Services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of hazard anticipation mappi

OpenLISEM is an open-source hydrological model suited for the simulation of floods, flash floods and erosion events. The following sections provide an overview of the results from the OpenLISEM model used in the exposure mapping A 30x30m flood map (maximum flood height) for the BuPuSa region was developed for several points on the intensity-frequency-duration curve. This curve represents the extreme value analysis (EVA) for the rainfall across the BuPuSa area. Based on 50 years of historic rainfall data from TAMSAT the EVA is developed for a 1000 year period. From this different rainfall intensities area taken which are referred to at the return period. The statistical possibility of a certain rainfall intensity to happen once in X many years. Flood maps were developed for the following return periods: 1/2, 1/10, 1/50, 1/100 and 1/1000. In addition to 5 different return periods, two different scenarios were modeled. A short high intensity rainfall event that typically causes flash floods, and a longer term lower intensity rainfall event that typically causes fluvial (river) floods. These events were represented by respectively a 6h rainfall event and a 14 day rainfall event. As a result 10 different flood maps were developed.

Fuel moisture content (FMC) is an important fuel property for assessing wildfire hazard, since it influences fuel flammability and fire behavior. The relationship between FMC and fire activity differs among land covers and seems to be a property of each ecosystem. Our objectives were to analyze pre-fire FMC among different land covers and to propose a wildfire hazard classification for the Sierras Chicas in the Chaco Serrano subregion (Argentina), by analyzing pre-fire FMC distributions observed for grasslands, shrublands and forests and using percentiles to establish thresholds. For this purpose, we used a fire database derived from Landsat imagery (30 m) and derived FMC maps every 8 days from 2002 to 2016 using MODIS reflectance products and empirical equations of FMC. Our results indicated that higher FMC constrains the extent of wildfires, whereas at lower FMC there are other factors affecting their size. Extreme and high fire hazard thresholds for grasslands were established at FMC of 55% and 67% respectively, at 72% and 105% for forests and at 106% and 121% for shrublands. Our FMC thresholds were sensitive to detect extreme fire hazard conditions during years with high fire activity in comparison to average conditions. The differences in the distributions of pre-fire FMC among land covers and between ecosystems highlighted the need to locally determine land cover-specific FMC thresholds to assess wildfire hazard. Our wildfire hazard classification applied to FMC maps in an operational framework will contribute to improving early warning systems in the Sierras Chicas. However, moisture alone is not sufficient to represent true fire hazard in Chaco forests and the combination with other variables would provide better hazard assessments. These operational wildfire hazard maps will help to better allocation of fire protective resources to minimize negative impact on people, property and ecosystems. To the best of our knowledge, this is the first study analyzing pre-fire FMC over several fire seasons in a non-Mediterranean ecosystem, aiming at assessing wildfire hazard.

Lane-Level Hazard Mapping for OEMs Market Outlook

According to our latest research, the Global Lane-Level Hazard Mapping for OEMs market size was valued at $1.2 billion in 2024 and is projected to reach $6.8 billion by 2033, expanding at a robust CAGR of 21.5% during the forecast period of 2025–2033. The primary driver fueling this impressive growth is the accelerated adoption of advanced driver-assistance systems (ADAS) and autonomous driving technologies by original equipment manufacturers (OEMs) worldwide. As automotive safety regulations become increasingly stringent and consumer demand for intelligent mobility solutions rises, the need for precise, real-time lane-level hazard mapping has become critical for OEMs aiming to enhance vehicle safety, navigation accuracy, and overall driving experience.

Regional Outlook

North America currently holds the largest share of the Lane-Level Hazard Mapping for OEMs market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region’s mature automotive sector, high penetration of advanced driver-assistance systems, and the presence of leading technology innovators and OEMs. Stringent road safety regulations enforced by agencies such as the National Highway Traffic Safety Administration (NHTSA) have further accelerated the integration of lane-level hazard mapping technologies in both passenger and commercial vehicles. Additionally, North America has witnessed significant investments in smart infrastructure and connected vehicle initiatives, creating a fertile environment for the rapid deployment and scaling of these solutions.

Asia Pacific is emerging as the fastest-growing region in the Lane-Level Hazard Mapping for OEMs market, projected to register a remarkable CAGR of 25.4% between 2025 and 2033. The surge is propelled by robust automotive production, increasing urbanization, and government initiatives supporting intelligent transportation systems across countries such as China, Japan, and South Korea. The rapid adoption of electric vehicles and the proliferation of local OEMs investing heavily in autonomous and connected car technologies are key growth enablers. Strategic partnerships between global technology providers and regional automakers are further catalyzing innovation and accelerating market penetration, making Asia Pacific a focal point for future market expansion.

In emerging economies across Latin America, the Middle East, and Africa, the Lane-Level Hazard Mapping for OEMs market is experiencing steady but comparatively moderate growth. Challenges such as limited infrastructure readiness, high initial investment costs, and regulatory uncertainty are impeding rapid adoption. However, localized demand for enhanced road safety and the gradual introduction of advanced in-vehicle technologies are expected to drive incremental growth. Policy reforms, pilot smart city projects, and the entry of multinational OEMs and mapping service providers are laying the groundwork for gradual market development, with significant opportunities anticipated as digital infrastructure matures.

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