Satellite images, geological tomography, climate and data records, miscellaneous useful data points about our globe
Maps and satellite photos
Here is a review of satellite image sources. I have only checked out a handful of these. If you just want eye candy, NASA Visible Earth is a good one. I’m fond of LANDSAT maps. Various can be found through Earth Explorer. All these resources blur into one after a while, with similarly confusing interfaces, and unexpected UI glitches and apparently random surprise pricing structures revealed belatedly.
Earth Observation data are becoming too large to be downloaded locally for analysis. Also, the way they are organised (as tiles, or granules: files containing the imagery for a small part of the Earth and a single observation date) makes it unnecessary complicated to analyse them. The solution to this is to store these data in the cloud, on compute back-ends, process them there, and browse the results or download resulting figures or numbers. But how do we do that?
earthengine.google.com/ provides lots of imagery with an eye to discoverability and UX.
The public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.
See also Australia-specific stuff.
from intake import open_catalog cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/master.yaml") list(cat)
Open Data Cube is a whole python library for working with satellite images and other large scale raster data.
- 1460 example images (4 per day, 365 days in the year)
- 16 channels in each image corresponding to various weather-related quantities
- each channel is 768 x 1152 corresponding to one measurement per 25 square km on earth
“This webpage provides an interactive and searchable catalogue of public benchmark datasets for earth observation with the aim to support researchers in the fields of geoscience, remote sensing, and ML.“