Assistant Professor
Lukas Stefan Schild
Field of work
I am engaged in research at the intersection of geoscience, data science, and artificial intelligence. My primary focus revolves around Early Warning Systems for Rock Falls and Landslides, employing innovative approaches to enhance predictive capabilities and improve understanding of unstable rock slopes.
Research Objectives:
- Develop advanced Early Warning Systems that incorporate multi-disciplinary data sources.
- Explore the potential of data science in analyzing geological and environmental data for improved hazard prediction.
- Investigate the integration of sensor networks to enhance our understanding of inaccessible rock slopes.
- Advance the application of Explainable Artificial Intelligence for transparent and comprehensible forecasting models.
Courses taught
- ELE201 Microcontrollers and Computer Networks
- DAT160 Intelligent Robots
- DAT103 Computer Architecture and Operating Systems
Research areas
- eXplainable Artificial Intelligence (XAI)
- Sensor Data Fusion
- Early Warning Systems for Rock Slope Failures
Research groups
Publications
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Autonomous UAVs for Monitoring Glacier Dynamics and Hazards: A Case Study from Jostedalsbreen, Norway
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Multistage 54,000 m3 rockfall (Stampa, Western Norway): Insights from comprehensive monitoring and failure analysis
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Forstå Ustabile Fjellsider med Kunstig og Naturlig Intelligens
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Data for: Multistage 54,000 m3 Rockfall (Stampa, Western Norway): Insights from Comprehensive Monitoring and Failure Analysis
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A Fine Line: Explaining Landslide Forecasting with Dynamic Rainfall Thresholds
Laster...