Benchmarking Rust vs Python+Dask for NDVI: 23× Faster, and Why That Matters for Carbon
Last week I wanted to benchmark our Rust pipeline against the Python “standard” — the DEA Knowledge Hub notebooks that everyone uses. I based the workflow on the burnt area mapping notebook, which uses the NDVI mean-over-time approach.
What followed was a result that confirmed what I’d suspected about Python’s Dask for CPU-bound geospatial workloads — and what I’d argued at FOSS4G 2025: the language you choose has a real environmental cost. 23× slower doesn’t just mean 23× more money. It means substantially more energy and CO₂ for the exact same output. How much more? We haven’t measured it with a power meter yet — but we can estimate, and the direction is unambiguous.
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