Salnom Tech
Featured Projects
Explore how we combine AI, engineering, remote sensing, and advanced modelling to solve complex real-world challenges.
From disaster risk assessment to structural health monitoring, our projects turn complex data into actionable insights for safer and more resilient infrastructure.
Hurricane-Induced Risk Assessment in Florida
AI-powered analysis of aerial imagery to detect and quantify hurricane damage at the property level. Our model identifies damaged roof areas, classifies severity, and maps impacts across affected regions. The results provide rapid intelligence for insurance, emergency response, and recovery planning.
Wildfire-Induced Risk Assessment in California
Machine learning for rapid, structure-by-structure assessment of wildfire damage from aerial imagery. The system classifies buildings as undamaged, partially damaged, or fully destroyed after a wildfire event. Interactive damage maps provide clear insights for claims assessment, emergency response, and rebuilding.
Permafrost Damage Estimation in the Arctic Region
Predictive modelling to identify where permafrost degradation threatens roads, airports, and communities. Satellite, terrain, and environmental data are combined to map infrastructure vulnerability across Arctic regions. The resulting risk maps help prioritize monitoring, mitigation, and long-term infrastructure resilience.
House Fire Structural Damage Modelling and Mitigation
An intelligent assessment system for evaluating fire-damaged concrete structural members. The model estimates fire exposure and remaining load capacity while identifying the severity of structural damage. It also evaluates retrofit options such as CFRP strengthening and estimates the recovered structural capacity.
Concrete Crack Identification, Classification and Measurement
Deep learning for automated detection and quantitative assessment of cracks in concrete structures. A U-Net-based model identifies, traces, and classifies cracks directly from structural inspection images. Crack length and width measurements provide objective data for condition assessment and maintenance planning.