AWS Hackathon for Public Good 2019 Urban Institute Challenge

What it does

Given two sets of input data: 1: a FOOTPRINT file containing polygons of defined houses 2: one or more LAS files containing LIDAR information.

Annotate the FOOTPRINT file with a new column "ALTITUDE_M", which is the altitude (in meters) of each individual building.

How We built it

Lightweight front end to submit URLs of data location Heavy lifting done by Autoscaled EC2s which listen to a queue of job requests

Challenges we ran into

Provided data was in the wrong projection Suggested Python library was incompatible with LAS 1.4 format Lambda is insufficient (memory and time) for the job execution

Accomplishments that we are proud of

Worked with the customer to verify the provided data set Considered Speed of Execution and Cost in the solution

What we learned

Working with GIS data has a learning curve GIS Coordinate systems need to be aligned Working with very large datasets slows down prototyping

What's next for Hypsometer

Swap UI layer for S3 web hosting, API Gateway and Lambda Containerize the ASG to work in Fargate for on-demand compute

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