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To better understand the impact of wildfires on land and structures, advanced analysis leveraging the power of Google Earth Engine (GEE) and Sentinel-2 imagery. Key Highlights of the Project: 🔹 Pre- and Post-Fire Analysis: Calculated Normalized Burn Ratio (NBR) to quantify burn severity for the specified wildfire period. 🔹 Burn Severity Visualization: Mapped the delta NBR (dNBR) with vivid palettes to easily interpret burn severity. 🔹 Burned Area Estimation: Accurately calculated the total burned area in hectares. 🔹 Vectorization and Impact Assessment: Identified burned areas and overlayed affected regions with critical infrastructure, including buildings. Technologies Used: 📌 Sentinel-2 Harmonized Imagery 📌 GEE for cloud masking, NDWI, NBR, and dNBR calculations 📌 Python library osmnx to extract the building footprint and analysing the buildings within the affected area 📌 QGIS for effective data visualization and analysis Challenges and Limitations ⚠️ A major limitation was the absence of building footprints in some burned areas within the OSM dataset. This makes it difficult to predict the exact number of affected houses, highlighting the need for improved spatial data availability and accuracy. Applications of This Work ✅ Quantifying wildfire impact on natural and built environments. ✅ Providing actionable insights for disaster management and urban planning. ✅ Supporting efforts to improve wildfire resilience and recovery strategies. 💡 This project demonstrates the powerful synergy between cloud-based geospatial analysis (GEE), desktop GIS platforms (QGIS), and Python-based data science (Jupyter Notebook). Original Credit: https://bit.ly/3WsgcgC Thanks for watching it. Please like, share, and subscribe to this channel. YouTube: / 7startech95 Facebook: / 7startech95 Instagram: / 7startech95 Twitter: / 7startech95 Blog: https://www.7startech95.blogspot.com LinkedIn: / 7startech95 Contact us at: [email protected] #gee #calfire #BurnSeverity #7startech95