Urban Institue

  1. Affordable housing in cities, we work with federal city and state governments, want to stop pushing out low income families out of homes
  2. To remedy and predict building hights data fushion ML washington DC input data sets polygon footprints lidar data, drone and get lattitude and longittudes, and satellite imagery datafusion we have the polygon and lattitue and longitudue smart data to merge both datas and use ML to expand past DC
  3. Building heights why? Building heights are important tell you about character of the neighborhood and zoning policies
  4. Keeping families together, years of research measuring building heights correlates to quality of life.

Our Approach

Our approach is completely Serverless, to reduce load times you can incorporate a Database for importing data. A formula for Quality of Living vs Building Height to Correlate for analytics Platform AWS QuickSight or Tableau Take satellite imagery out of Google Drive and put into S3 for historical Use AWS app sync to get data back faster with one endpoint and get json format the way you want S3 Folders for Input and Output .geojson to .csv and backend Visualizing and meshing the sources including mapbox Building a Scalable solution by File size Serverless Take File clean up file for data cleansing New Headers and append additional data via Lidar and Satellite

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