How Remote Sensing Improves Fire Monitoring

20 minutes. That’s the difference between a 3 acre brush fire and a catastrophe. For decades, we relied on human lookouts. People sitting in towers, scanning the horizon, hoping to spot smoke. It worked. But it was slow. And in wildfire terms, slow is deadly. Enter Pano AI. They aren't trying to stop fires from starting. They are just changing the math on how fast we respond. Here is how their system works: - Mounts ultra HD, 360-degree cameras on high-altitude cell towers. - Scans the surrounding landscape every 60 seconds, 24/7. - Uses AI to instantly detect the first visual sign of smoke. - Triangulates exact coordinates using multiple cameras. - Pushes alerts directly to local fire agencies in real-time. Immediate, actionable data. In Colorado, the system caught a remote lightning strike. Firefighters responded immediately. The fire was contained to just 3 acres. Without that camera? It burns unchecked for another 20 minutes before anyone notices. The best AI companies aren't building party tricks. They are building invisible infrastructure that saves lives, resources, and our planet. 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc

Pano AI , a #SanFrancisco-based startup specializing in AI-powered wildfire detection, has raised $44 million in Series B funding to expand its early detection platform. As climate change accelerates, wildfires are becoming more frequent, destructive, and harder to manage. In the 2023–24 season alone, fires scorched nearly 4 million square kilometers, an area larger than India. 😔 Faster Detection, Smarter Response Pano AI is addressing this growing threat by providing emergency responders with cutting-edge AI tools for early detection and rapid response. ► The company’s platform integrates ultra-high-definition, 360-degree cameras with proprietary AI models to monitor nearly 30 million acres across the U.S., Canada, and Australia. ► These cameras, placed on high vantage points, continuously scan for signs of smoke and fire. ► When the AI detects a potential incident, human analysts verify it before dispatching alerts with precise GPS coordinates to local emergency crews—enabling faster, more effective responses. Pano AI’s system proved its value during Colorado’s Bear Creek Fire, detecting smoke within minutes and helping contain the blaze to three acres. Today, over 250 agencies and 15 major utilities rely on the platform. The company says its tools have helped keep 95% of detected fires from growing beyond 10 acres. If done right, this kind of early detection could save lives, protect homes, and prevent billions in damage — all by buying first responders a little more time. #AI #Technology

🌟 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗢𝗻𝗲 𝗼𝗳 𝗠𝘆 𝗠𝗼𝘀𝘁 𝗦𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 🌟 I’m delighted to share my latest — and one of my most meaningful — research contributions as 𝐬𝐨𝐥𝐞 𝐚𝐮𝐭𝐡𝐨𝐫: 🛰️ 𝗧𝗵𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗕𝘂𝗿𝗻 𝗜𝗻𝗱𝗲𝘅 (𝗔𝗧𝗕𝗜) 𝗳𝗼𝗿 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗕𝘂𝗿𝗻𝗲𝗱 𝗔𝗿𝗲𝗮 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 📘𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗶𝗻: 𝘐𝘯𝘵𝘦𝘳𝘯𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 𝘰𝘧 𝘈𝘱𝘱𝘭𝘪𝘦𝘥 𝘌𝘢𝘳𝘵𝘩 𝘖𝘣𝘴𝘦𝘳𝘷𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘎𝘦𝘰𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 (𝘌𝘭𝘴𝘦𝘷𝘪𝘦𝘳) 🧭 𝗜𝗺𝗽𝗮𝗰𝘁 𝗙𝗮𝗰𝘁𝗼𝗿: 8.6 🔓 𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 This study introduces the Automated Temporal Burn Index (ATBI) — a novel, physics-based wildfire index designed to improve the accuracy and scalability of burned area mapping using Landsat and Google Earth Engine (GEE). 🔍 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 1️⃣ The new ATBI significantly reduces wildfire mapping errors compared to traditional indices. 2️⃣ dATBI achieves higher precision (~0.92) and F1 score (~0.94) than dNBR across diverse ecosystems. 3️⃣ The multi-temporal dATBItm robustly suppresses cloud, smoke, and snow artifacts. 4️⃣ SREM-corrected reflectance improves index accuracy over standard LaSRC data. 5️⃣ Demonstrated scalability across 50 wildfires worldwide, covering boreal, tropical, Mediterranean, and savanna ecosystems. 🎓 This work paves the way for more reliable, automated, and large-scale wildfire monitoring that supports climate resilience strategies. 👉 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗙𝘂𝗹𝗹 𝗔𝗿𝘁𝗶𝗰𝗹𝗲 (𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀): 🔗 https://lnkd.in/g6Vt_iRa #Alhamdulillah #الحمدالله #PraiseBeToAllah #KFUPM #Wildfire #ATBI #NBR #RemoteSensing #EarthObservation #GoogleEarthEngine #BurnedAreaMapping #ClimateChange #Landsat #GeospatialScience #ResearchInnovation #dNBR #dATBI #GEE #FireEcology #Elsevier #ScientificResearch #California #ParkFire #NWT #Alberta #Pakistan #Australia #Spain #Greece #Chile #RanchFire

California Forest Fire Analysis Using SAR and Multispectral Data The Eaton and Palisades fires, two of the most destructive wildfires in Southern California's history, have devastated approximately 60 square miles, claiming at least 25 lives and displacing over 88,000 people. To analyze the fire's impact, we used Sentinel-1 SAR data (Log Difference technique) and Sentinel-2 multispectral data (Burn Area Indices via a change detection model in ArcGIS Pro). The analysis utilized pre-fire and post-fire imagery, leveraging band combinations like SWIR, IR, and NIR to highlight affected areas. The Normalized Burn Ratio Index (NBRI) was calculated to map and quantify the burned regions. Post-processing included raster reclassification and polygon conversion to extract precise burned area maps. This workflow not only visualizes fire damage but provides a replicable method for forest fire monitoring in other regions, supporting rapid response and recovery efforts. #RemoteSensing #GIS #ForestFireMonitoring #SAR #MultispectralAnalysis #ArcGIS

🌐🚁 Airbus’ Integrated Air–Ground Wildfire Response Ecosystem ✈️ Airbus is advancing a new model for wildfire response built around a fully connected, multi‑asset ecosystem. 🛰️ Instead of relying on isolated platforms, Airbus integrates airborne assets, unmanned systems, satellites, and ground command units into one real‑time operational network. 📡 At the center is an Airbus‑developed secure digital backbone that fuses data from all assets — aerial sensors, infrared cameras, drones, satellite imagery, and ground teams — into a single, continuously updated tactical picture. This enables faster detection, smarter coordination, and safer operations. 🚁 Airborne assets Airbus aircraft and helicopters contribute: - Precision water‑drop capability - ISR and mapping - Medical evacuation - Aerial command and control - Connectivity relay for responders Flight paths and drop points are optimized through Airbus’ real‑time analytics. 🛰️ Space‑based sensing Airbus satellites provide: - Early fire detection - Risk and damage assessment - Wide‑area situational awareness - Connectivity support in remote areas This gives commanders a macro‑level understanding of fire behavior. 🚁 Unmanned systems Airbus UAS platforms add: - Persistent monitoring - Optical and infrared imaging - Close‑range assessment - Airborne communication nodes They bridge the gap between satellites and crewed aircraft. 🛠️ Ground command systems Airbus ground solutions deliver: - Real‑time C3 (Command, Control & Coordination) - Asset tracking - Fire‑spread prediction - Secure voice, data, and video Every responder operates from the same synchronized picture. 🔗 Digital backbone The ecosystem is unified through Airbus’ mission‑critical technologies: - Cloud‑based data fusion - AI‑driven analytics - Private 4G/5G mission networks - Secure remote access for decision‑makers 🔵 This transforms raw data into actionable guidance for air and ground teams. 🎯 The Result Airbus is shifting wildfire response from platform‑centric to ecosystem‑centric — delivering faster detection, smarter coordination, and safer operations across the entire mission chain.

My 3rd PhD paper has been published! 📄 It is about mapping burned areas using Sentinel-2 data and Rao's Q-Index. 🔥 📍 Study Areas - Sardinia (Italy). - Thassos (Greece). - Pantelleria (Italy). 🛰️ Sensors Used - Sentinel-2 — spectral data for analysis and mapping. - Copernicus Emergency Management System (#CEMS) — high-resolution data to validate the results. 🗺️ Spectral Indices - Normalized Burn Ratio (NBR). - Mid-infrared Burn Index (MIRBI). - Normalized Difference Vegetation Index (NDVI). - Burned Area Index for Sentinel-2 (BAIS2). ✍🏾 Methodology - Change detection approach based on Rao's Q-Index. - Analysis of Sentinel-2 images pre- and post-fire. - Calculation of spectral indices and application of Rao's Q Index in classical and multidimensional modes. - Validation of results with CEMS data. 📊 Accuracy Results - In classical mode, the highest accuracy was 72% with the MIRBI index. - In multidimensional mode, the highest accuracy was 96% combining MIRBI and NBR. - The combination of MIRBI and NBR consistently achieved the highest accuracy in all study areas. 🚀 Relevance of the Methodology - The combination of spectral indices and the use of Rao's Q Index improves the accuracy in identifying areas affected by fires. - The study highlights the importance of selecting the appropriate indices based on regional characteristics and specific detection needs. 👩🏾💻 Software and Repository - Google Colab with Google Earth Engine (GEE) integration - Python 3.10 - Open repository on GitHub: https://lnkd.in/dmJ4r9Wt 🟢 Conclusion - The combination of MIRBI and NBR indices provides a robust approach to increase the accuracy of fire detection. - Consistent alignment with CEMS data, validating the reliability of the methodology. ____________________ 🔗 Read the article: Tiengo et al., 2025 | https://lnkd.in/dFtsMfDN ____________________ 🍀 Share this post with your network! 📌 Save this post for later!

Swarm intelligence and #drones to aid in wildfire management 🔥🚁🚁🔥 Wildfires are a formidable challenge: As climate conditions grow increasingly volatile, the need for innovative solutions is more urgent than ever. We at VTT have had the opportunity to develop new approaches to machine intelligence for wildfire management in the #FireMan project. Our specific focus? Leveraging swarm intelligence to provide real-time, actionable insights to firefighters on the ground. Here’s how it works: 🔹 Autonomous drone swarms: Groups of 3–10 drones collaborate as an intelligent collective to locate smoke, detect fire sources, and monitor fire progression autonomously — no human intervention needed. 🔹 Thermal imaging technology: By using advanced thermal cameras, the drones’ imaging from above can cut through dense smoke to pinpoint the advancing fire front. This ensures that the wildfire incident commander has precise, real-time information to make critical decisions. 🔹 Dynamic swarm coordination: Once a fire is detected by a member of the swarm, it calls for additional drones to the site for comprehensive monitoring from multiple angles. The potential of drone swarms in wildfire management is, e.g., in: ⏳ reducing response times, 📈 improving situational awareness, and 🌾 ultimately, saving lives and ecosystems. This post is the 9th in a series of videos from the FireMan project. The FireMan project is about Unmanned aerial systems based solutions for real-time management of wildfires. The research is funded by the Research Council of Finland | Suomen Akatemia with EU recovery instrument funding, and it involves Finnish Geospatial Research Institute (FGI), University of Jyväskylä, University of Oulu, and VTT as the core partners. Please do like the post and follow me if you wish to see more of this kind of content! #dronesforgood #droneswarms #autonomousdrones #thermalimaging #wildfires

Harnessing Space to Combat Wildfires: FireSat In a recent CNBC podcast, our partners from Muon Space and the Earth Fire Alliance discussed how we’re collaborating to leverage geospatial data to fight wildfires – specifically, our progress on FireSat, a satellite constellation designed to detect and track 5x5 meter wildfires anywhere in the world, updated every 20 minutes. Fire authorities tell us that fires are exponentially easier to suppress when they are small, which will make FireSat a game changer for their ability to respond to fires. We also expect this data to enable scientists to improve wildfire modeling and prediction. Key takeaways: 𝗡𝗶𝗰𝗵𝗲 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲: Thanks to cost reductions, it's now possible to move from general-purpose satellites to focused purpose-built satellites that are better tailored to specific insights. 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: I’ve seen time and time again how our Google Research teams with remote sensing expertise are able to achieve breakthroughs by leveraging massive amounts of satellite data in combination with other data sources – see e.g. our work on Contrails, Solar API, Flood Forecasting, and more. (We are also leveraging current geostationary satellites for the wildfire boundaries that we show today in Google Maps.) 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹: Our focus will be on applying AI to FireSat to enable firefighters, first responders, and scientists to access the satellite data in near real-time, enabling it to translate directly into faster, more effective response. 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗪𝗶𝗹𝗱𝗳𝗶𝗿𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: With the support of Google Research in sensor design, and Google.org's $13 million grant to the Earth Fire Alliance, the first FireSat satellite is scheduled to launch this quarter, as the first step in a new era of global wildfire detection and response. We’re excited to apply our set of AI tools to tackle this growing challenge. Here are the links to the full podcast: Apple: https://lnkd.in/g2PTXRvV Spotify: https://lnkd.in/grbRJDue and you can learn more about FireSat here: https://lnkd.in/gJKUHaiR

These two fight wildfires for $1 a year 🌳 (Their device detects fires 15x faster) Dr. Muhammad Ali Örnek and Suat Batuhan Esirger were devastated by the wildfires destroying Turkey's forests. Traditional detection methods were failing: Satellite imagery too slow. Cameras too limited. So they created ForestGuard - the "Internet of Trees." How it works: A) Sensors attached to trees monitor air quality and temperature B) They detect smoke particles as small as 1 microgram C) Data transmits via satellite to monitoring systems D) Fires detected within 15 minutes (vs 4+ hours for cameras) E) Alerts sent directly to local authorities Why this matters: ↳ Wildfires release 8 billion tons of CO2 annually (20% of global emissions) ↳ 2023 was Europe's worst fire season, with 12.7M acres burned ↳ Every minute saved can prevent 30+ acres from burning The impact so far: ↳ Just $1 protects 500 trees for an entire year ↳ Satellite connectivity works where cellular networks fail ↳ System predicts fire spread direction to keep firefighters safe From architecture graduates with a passion for forests... ...to creators of the "WoodWideWeb" helping in the fight against fires Are you a fan of this wildfire fighting innovation? 📥 Follow me for daily insights on ClimateTech

Two NASA-developed technologies are key components of a new high-resolution sensor for observing wildfires: High Operating Temperature Barrier Infrared Detector (HOT-BIRD), developed with support from NASA’s Earth Science Technology Office (ESTO), and a cutting-edge Digital Readout Integrated Circuit (DROIC), developed with funding from NASA’s Small Business Innovation Research (SBIR) program. Developed with support from NASA’s Earth Science Technology Office (ESTO), the “Compact Fire Infrared Radiance Spectral Tracker” (c-FIRST) is a small, mid-wave infrared sensor that collects thermal radiation data across five spectral bands. Most traditional space-based sensors dedicated to observing fires have long revisit times, observing a scene just once over days or even weeks. The compact c-FIRST sensor could be employed in a SmallSat constellation that could observe a scene multiple times a day, providing first responders data with high spatial resolution in under an hour. In addition, c-FIRST’s dynamic spectral range covers the entire temperature profile of terrestrial wild fires, making it easier for first-responders to detect everything from smoldering, low-intensity fires to flaming, high intensity fires. The need for space-based assets dedicated to wildfire management is severe. During the Palisade and Eaton Fires earlier this year, strong winds kept critical observation aircraft from taking to the skies, making it difficult for firefighters to monitor and track massive burns. Space-based sensors with high revisit rates and high spatial resolution would give firefighters and first responders a constant source of eye-in-the-sky data. c-FIRST leverages decades of sensor development at JPL to achieve its compact size and high performance. In particular, the quarter-sized High Operating Temperature Barrier Infrared Detector (HOT-BIRD), a compact infrared detector also developed at JPL with ESTO support, keeps c-FIRST small, eliminating the need for bulky cryocooler subsystems that add mass to traditional infrared sensors. With HOT-BIRD alone, c-FIRST could gather high-resolution images and quantitative retrievals of targets between 300°K (about 80°F) to 1000°K (about 1300°F). But when paired with a state-of-the-art Digital Readout Integrated Circuit (DROIC), c-FIRST can observe targets greater than 1600°K (about 2400°F). Developed by Copious Imaging LLC. and JPL with funding from NASA’s Small Business Innovation Research (SBIR) program, this DROIC features an in-pixel digital counter to reduce saturation, allowing c-FIRST to capture reliable infrared data across a broader spectral range. Full Article: https://lnkd.in/gJ2MjPW2 #JPL #NASA #cFIRST NASA’s c-FIRST instrument could provide high resolution data from a compact space-based platform in under an hour. (NASA/JPL)