Real-time Map Imagery Manipulation with Python, Slippy Maps, Flask, and Pillow: Building Interactive
summary
A pipeline converts large imagery into slippy tiles with GDAL, stores them in S3 via boto3, and serves them through a Flask endpoint that modifies tiles in-stream. The service uses Pillow to apply a brightness filter, paste PNG overlays on the fly, and return modified bytes without regenerating tiles; a per-tile histogram applied here looks patchy. A demo wires tiles into qjs and a Cesium/ccmjs 3D globe served from S3 and Lambda with a React front end.
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chapters (20)
- 0:00Introduction
- 0:45Who am I
- 2:30Why
- 3:30OpenAerialMap
- 4:10SlippyTileMap
- 5:00Gdal
- 7:20Upload Tiles
- 9:15Create API
- 13:10Retrieving Parameters
- 14:05Use Cases
- 15:20Data Delivery
- 16:00Example
- 18:25Dynamically generating Slippy Maps
- 19:05Large image data sets
- 21:00Geospecific data overlays
- 22:10Endless possibilities
- 23:00Vector data tiles
- 24:10Frontend framework
- 25:20Different data sources
- 29:10Thank you
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