stacmap 0.0.4

Create interactive maps of STAC items.

stacmap

Create interactive maps of STAC items and collections without the heavy dependencies and boilerplate of geopandas.GeoDataFrame.explore.

Features

• 🗺️ Explore STAC item footprints
• 🌈 Color-code items by properties
• 🖼️ Preview item thumbnails
• 🪶 Lightweight dependencies (just folium and pystac)

Installation

$ pip install stacmap

Quickstart

stacmap.explore creates an interactive Folium map from STAC items or collections.

import stacmap
from pystac_client import Client

# Find Landsat Collection 2 scenes over an area of interest
catalog = Client.open("https://landsatlook.usgs.gov/stac-server")
items = catalog.search(
    bbox=[-120.9519, 37.2455, -113.4812, 45.1025],
    collections=["landsat-c2l2-srby"],
    datetime="2019-08-01/2019-08-03"
).get_all_items()


# Plot the items on an interactive map, color-coded by cloud cover
stacmap.explore(items, prop="eo:cloud_cover")

Check out the docs for details or try out an interactive notebook in Binder.

Compared to GeoPandas

Let's look at a simple example to see how stacmap simplifies plotting a STAC collection and search bounds over geopandas.

First, we'll load our STAC items:

from pystac_client import Client

catalog = pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1")

bbox = (-67.008753, -9.96445, -65.615556, -8.57408)

items = catalog.search(
    collections=["sentinel-2-l2a"],
    bbox=bbox,
    datetime="2019-06-01/2019-06-10"
).get_all_items()

Now we'll create an interactive map that shows our items and our bounding box.

stacmap	geopandas
!pip install stacmap import stacmap stacmap.explore( items, prop="eo:cloud_cover", bbox=bbox )	!pip install geopandas folium mapclassify matplotlib import geopandas as gpd import shapely import folium gdf = gpd.GeoDataFrame.from_features( items.to_dict(), crs="EPSG:4326" ) bbox_geom = shapely.geometry.mapping(shapely.geometry.box(*bbox)) bbox_layer = folium.GeoJson(bbox_geom) m = gdf.explore(column="eo:cloud_cover") bbox_layer.add_to(m) m

Users coming from geopandas can check out the transition guide for tips on switching to the stacmap API.