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Standardizes raw satellite imagery (Landsat, Sentinel-2) into Analysis Ready Data (ARD) by performing atmospheric and topographic corrections.
Utility
stars
32
forks
7
wagl (Workflow for Analysis Ready Data) is a critical piece of infrastructure maintained by Geoscience Australia, forming a pillar of the Digital Earth Australia program. While its star count (32) and velocity (0.0) suggest a niche audience, these metrics are deceptive for institutional scientific software. Its defensibility stems from 'deep domain expertise'—specifically the implementation of complex physics-based models like Bidirectional Reflectance Distribution Function (BRDF) and atmospheric radiative transfer models (e.g., MODTRAN). This is not commodity code; it is a high-fidelity scientific instrument. Its main competitors are proprietary platforms like Google Earth Engine (GEE), which provide pre-processed ARD, and commercial providers like Planet. However, for sovereign data requirements or custom processing pipelines outside of GEE's walled garden, wagl remains a de facto standard within the Open Data Cube ecosystem. The 'platform domination risk' is high because cloud providers (AWS/Google) increasingly offer managed ARD products, reducing the need for users to run their own wagl pipelines. Displacement is unlikely in the short term because scientific standards for ARD are sticky and heavily vetted by national space agencies.
TECH STACK
INTEGRATION
library_import
READINESS
The reusable building blocks distilled from this project — each a mechanism you could lift into your own.
Array -> CompressedBytes
Reorganize the bit layout of high-precision numerical raster datasets using bitshuffling before applying standard lossless compression algorithms.
GeospatialFootprint + Timestamp -> GeometryAngles
Calculate precise solar and satellite viewing angles (azimuth and elevation) for each pixel using orbital ephemeris and acquisition timestamps.