Identifying risk with science + communities

Background

Rural communities need to identify and spatialize the probability of landslide events that interfere with economic development by affecting community resilience and compromising their social and economic systems. Considering risk in detail can improve land use planning, so that infrastructure is developed in suitable areas with suitable conditions.

Currently, the inputs used to determine the risk of landslides all over the world are limited by large-scale maps that do not allow actions to mitigate risk at the local level. There is an opportunity for researchers and scientists to develop dynamic mapping tools to determine the risk of landslides more accurately. Researchers are also leveraging machine learning and cloud computing to determine zoning and the level of risk for landslides. 

Objectives

Your challenge is to create prototypes and methodologies to incorporate Earth observations (as satellite data) with local open data provided by national entities and scientific institutes. Additionally, participants are encouraged to include the information that the general public can contribute by capturing data in their territories to improve the precision of the analysis. Finally, any tool or prototype that meets the overall objective of this challenge must potentially be implemented and executed at a low cost by local governments.

Potential Considerations

As you develop your tool, you may (but are not required to) consider the following:

• To address this challenge, you could take into account the geology, the historical databases of landslides in the world, the elevation and surface information of the territory, the Normalized Difference Vegetation Index (NDVI), the current land use, demographics, and other variables that you consider helpful in your methodology. Included variables could be processed under a model that allows the method to be standardized and replicable in different municipalities.
• Remember that incorporating data capture by the community will considerably enrich your analysis of both detection and prediction of risks.
• It is critical to present information in a straightforward, intuitive, and easy way for communities and local governments.
• Your tool should have a graphical interface that allows communities and local governments to view, interpret, include local information, and analyze decision-making results. 
• Develop a prototype that is scalable but also operable at a low cost.
• Build models that allow quantification of potential human losses and the cost of impacts from landslides.
• Develop machine learning training data sets to predict landslides so that other researchers can easily incorporate training data into their processes.
• Create a code repository for your project so other people can review and take advantage of your efforts.
• Potential keywords your can search include: world, soil, information

For data and resources related to this challenge, refer to the Resources tab at the top of the page. More resources may be added before the hackathon begins.

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