🌐 localtileserver#

Need to visualize a rather large (gigabytes+) raster? This is for you.

Try it out below!

from localtileserver import TileClient, get_leaflet_tile_layer, examples
from ipyleaflet import Map

# Create a TileClient from a raster file
# client = TileClient('path/to/geo.tif')
client = examples.get_san_francisco()  # use example data

# Create ipyleaflet TileLayer from that server
t = get_leaflet_tile_layer(client)
# Create ipyleaflet map, add tile layer, and display
m = Map(center=client.center(), zoom=client.default_zoom)

A Python package for serving tiles from large raster files in the Slippy Maps standard (i.e., /zoom/x/y.png) for visualization in Jupyter with ipyleaflet or folium.

Launch a demo on MyBinder MyBinder

Under the hood, this is also a Flask blueprint/application for use as a standalone web app or in your own web deployments needing dynamic tile serving.

🌟 Highlights#

  • Launch a tile server for large geospatial images

  • View local or remote* raster files with ipyleaflet or folium in Jupyter

  • View rasters with CesiumJS with the built-in Flask web application

  • Extract regions of interest (ROIs) interactively

  • Use the example datasets to generate Digital Elevation Models

remote raster files should be pre-tiled Cloud Optimized GeoTiffs

ℹ️ Overview#

The TileClient class can be used to to launch a tile server in a background thread which will serve raster imagery to a viewer (see ipyleaflet and folium examples in 🚀 User Guide).

This tile server can efficiently deliver varying resolutions of your raster imagery to your viewer; it helps to have pre-tiled, Cloud Optimized GeoTIFFs (COG), but no wories if not as the backing library, large_image, will tile and cache for you when opening the raster.

There is an included, standalone web viewer leveraging CesiumJS and GeoJS. You can use the web viewer to select and extract regions of interest from rasters.

🪢 Community Usage#