Speedy Rocket

https://www.youtube.com/watch?v=FGQate7Lk0g

https://linkmix.co/6549780

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The global Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to provide situational awareness of landslide hazards for a wide range of users. The LHASA Model takes into account two sets of data. First is the Global Precipitation Measurement Mission which shows recent precipitation satellite data. Next is the Landslide Susceptibility Map which was developed to assess the underlying landslide susceptibility of terrain.

Precipitation & Applications Viewer | NASA Global Precipitation Measurement Mission

Github page: ojo-bot/python at master · vightel/ojo-bot · GitHub

While the LHASA model is currently fully functional for landslide risk assessment, this does come with some limitations. As this model is specifically deployed at a global scale, spatial resolution becomes a big problem when it comes to more specific areas. As landslides can affect only smaller communities rather than entire regions of a country, a model with higher spatial resolution is needed for the purpose of proper risk assessment.

These are the current variables that the LHASA model takes into account:

Most of these variables are data types have low spatial resolution as these are mostly satellite images. There is a need for data that is more specific to be able to increase the spatial resolution and accuracy of the model to provide proper risk assessment operations for local government units.

This is where the crowd-sourced data from LangitLupa comes in.

Since LangitLupa data would be much more specific to a certain area, there is much value for the crowdsourced dataset to provide to the existing LHASA model. Training and tweaking the LHASA model using this dataset can increase both the spatial resolution of the model as well as acclimatize it specifically to the Philippine environment to provide more accurate results. The additional data source that LangitLupa provides would have extremely high spatial resolution especially around residential areas as Google Street View photos are only a few meters in between each other. Not only does this provide a lot more data for the model to utilize, this can also provide information that no satellite photo can give but is truly a need, a visual landslide hazard assessment.

From all the challenges that we could've chosen, we chose to focus on identifying risks with science + communities. The reason behind this was to create a bridge between our passion of science and the communities we wanted to help.

We narrowed down the challenge to focus on landslides due to the multiple typhoons that occur in the Philippines. With the use of technology to Game, Gather and Guide, "LangitLupa" was born; a system that connects the earth (Lupa) and the skies (Langit) together. Throughout this journey, we have learnt that there are many obstacles that could occur such as gathering reliable data or relaying information to the affected communities, finding the best places to evacuate to and more.

In the end, we realized that the main solution to these challenges is not us but them because compared to the observers from the sky, the communities would know better of the land they call their home.

"Alone, we can do so little; together, we can do so much" -Helen Keller

We'd like to give our biggest thanks to the organizing team, Animo Labs and American Corner Manila - DLSU Libraries (most especially Ms Roana), our mentors (Sir JC Torreda), and friends (Tiff, Vince, Jon, Thom, Aaron) for their inputs in building our project!

Precipitation & Applications Viewer | NASA Global Precipitation Measurement Mission

Other Data Sources as Seen in Project Description

Microsoft Word - DSWD-DROMIC-Report-19-on-the-Landslide-Incident-in-Naga-City-Cebu-as-of-05-November-2018-7PM (reliefweb.int)

GRI_policy_report_Disaster-impacts-and-financing_Local-insights-from-the-Philippines.pdf (lse.ac.uk)

Natural Disasters, Public Spending, and Creative Destruction: A Case Study of the Philippines (adb.org)

The Philippines: Transferring the Cost of Severe Natural Disasters to Capital Markets (worldbank.org)

SVS: Global Landslide Hazard Assessment Model (LHASA) with Global Landslide Catalog (GLC) data (nasa.gov)

Help NASA Build the Largest Open Landslide Catalog with Landslide Reporter | CitizenScience.gov

P I N O Y - C U L T U R E { The Official Tumblr of Pinoy-Culture.Com }

CHAPTER 10 - LANDSLIDE HAZARD ASSESSMENT (oas.org)

#Landslide, #Risk_Assessment, #GameGatherGuide, #LangitLupa, #Crowd_Source, #LandslidingInYourDMs

This project has been submitted for consideration during the Judging process.