According to a December 2017 report by the research arm of reinsurance giant Swiss Re, the estimated global economic losses from natural and man-made disasters totaled US$306 billion in 2017, representing a 63% increase from US$188 billion in 2016.
Natural hazard assessments are expensive, time-intensive tasks. Regional-scale studies are rare, and the results are typically delivered as reports written in domain-specific jargon along with static maps.
Natural hazard events such as landslides need to be thoroughly understood so that risk can be accurately assessed and protection or mitigation measures put in place. For this reason, we created GAIA Landslide, Minerva’s Natural Hazards AI application.
Simply put, GAIA Landslide helps users identify landslides before they happen. GAIA Landslide is an AI-assisted application that reasons with complex geospatial datasets, enabling users to visualize susceptibility to natural hazards over large areas. Our cognitive AI system mimics human-expert reasoning, and provides explainable assessments, making it easy for non-experts to understand the results.
Minerva has successfully produced landslide susceptibility maps for British Columbia, Canada and Veneto, Italy. The Veneto project recently won the “INSPIRE Helsinki 2019 Data Challenge” for the most innovative practical use of spatial data in the domains of sea, weather and cities. Applications for wildfires and floods are currently in development.
Case Study: Sea to Sky Highway, British Columbia, Canada
The Sea to Sky corridor north of Vancouver, British Columbia, Canada is a mountainous area that has been glacially sculpted, forming deep valleys flanked by rocky mountains. Slopes along the valley walls are covered by thick forests, prone to soil slides, while the steep, unvegetated slopes at high elevation are typically associated with devastating rock avalanches and localized rockfall. The highly variable landscape means that susceptibility to different landslide types must be evaluated based on the specific geological and geomorphological attributes of each slope.
Mimicking the traditional process that professional geologists use to evaluate slopes, landslide susceptibility maps displayed on the user interface represent a new means of interrogating natural hazard data. Our method is an effective screening tool to evaluate the relative susceptibility of slopes to various types of landslides.
Case Study: Veneto, Italy
This project and the associated applications were created by Minerva Intelligence as part of the 2019 INSPIRE Helsinki Data Challenge. INSPIRE Helsinki 2019 hosted four data challenges that searched for innovative practical uses of spatial data in the domains of sea, weather, and cities. The INSPIRE Directive is a source of standardized spatial data well-suited for use in cognitive artificial intelligence (AI) systems. Our challenge was to showcase how INSPIRE provides a framework to conduct complex data analysis and share the results in an interoperable and reusable way.
The application developed for this data challenge compares spatial data to conceptual models of different landslide types: debris flows, slides in soil and slides in rock. By aligning the semantics of our conceptual models and cognitive reasoning system to the terminology standardized in INSPIRE code lists, we demonstrate that INSPIRE data is not only interoperable for data exchange, but that it is particularly appropriate for use by powerful artificial intelligence applications.
Nathaniel Tougas, P. Eng.
“By using Minerva’s cognitive AI to identify relationships between various geomorphological parameters, we can essentially get an unbiased second opinion to augment our own geohazard assessment. The GAIA Landslide Map provides geological engineers with AI tools and insights that have never been available in our field, and this is truly exciting to me.”
“Minerva’s synergy of AI with the fruits of Earth science research is not limited to mineral exploration… its AI is powerfully applying geotechnical knowledge to natural hazards avoidance and risk management faster than is possible with humans alone. The results are transparent to stake-holders.”
“Minerva Intelligence provides knowledge for engineering, services and applications based on advanced AI. Through a semantic approach, they explain complex physical models to scientists, practitioners and non-experts.”
“The SLRD Emergency Program relies on quality hazard information to make informed decisions for public safety and Minerva Intelligence contributes to the field of innovative professionals.”
Brent Ward, P. Geo.
“Minerva is bringing cutting-edge technology to natural hazard management.”
John J. Clague
“Minerva Intelligence is bringing the power of artificial intelligence to geology. Their AI approach can help bridge the gap between scientists and decision-makers in fostering disaster risk reduction and emergency management.”
”Minerva Intelligence leverages standards and interoperable data to foster disaster risk reduction… an absolute game-changer in emergency management!”