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REU Week v

This week, we focused on tuning building projections in height, generating KML/KMZ files from Google Earth for GPS locations of various structures, and innovating through the fusion of building projection and super-pixilation techniques. Initial results included binary mapping and application of belief propagation for data fusion. We also established a grid of points overlaid on maps, segmented street labels, and defined confidence scores for labelled buildings based on color consistency. Several completed tasks include height tuning and addressing accuracy using Google Maps API.

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REU Week v

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  1. REU Week v Malcolm Collin-Sibley Mentor: ShervinArdeshir

  2. Goals from last week • Tuning building projections in terms of height. • Generating KML/KMZ files from google earth containing GPS locations of different buildings/roads • Fusion between building projection and super-pixilation • First with binary mapping • Initial fusion results (Belief Propagation) • Run that fusion on the data set

  3. Completed work • Height Tuning Pre Tuning Post Tuning

  4. Completed work • Height Tuning Pre Tuning Post Tuning

  5. Completed work • Fusion – Propagation

  6. Completed Work • Fusion – Propagation, Post Height tuning

  7. Completed Work • Fusion – Propagation, Post Height tuning

  8. Completed work • Addresses url = http://maps.googleapis.com/maps/api/geocode/xml?latlng=38.9052,-77.0298&sensor=true

  9. Completed work • Addresses

  10. Completed work • Addresses

  11. Completed work • Addresses

  12. Completed work • Addresses

  13. Completed work • Addresses

  14. Completed work • Addresses

  15. Current work • Street Projection

  16. Current work • Street Projection

  17. The next step • Creating a grid of points overlaid to the map • Extracting labels of the streets for each point in the grid • Identifying the segments belonging to the streets by projection points of the grid from top view • Defining a confidence score for each of the labelled buildings based on color consistency

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