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I Replaced 30 Lines of Rasterio with 30 Lines of TorchGeo for Spatial Analysis — Here’s What Changed
An honest take on whether TorchGeo is actually worth the migration, or just a fashionable detour
Introduction
Every few months, a new library shows up promising to make geospatial Python “easier.” Most of the time, it’s a thin wrapper that adds a dependency without adding value. TorchGeo felt different — or at least, that’s what the GitHub stars and conference talks suggested.
So I ran an experiment. I took a workflow I’ve written a dozen variations of over the past two years — a Sentinel-2 tile, clipping it to an AOI, computing NDVI, and preparing patches for a downstream model — and rebuilt it twice. Once in plain Rasterio, the way I’ve always done it. Once in TorchGeo, the way the PyTorch-native crowd is doing it now.
Both versions landed at roughly 30 lines. Same task, same output. But the shape of the code, the assumptions baked into each library, and what breaks when you scale from a single tile in Nairobi to a country-wide time series — those turned out to be very different stories.
This isn’t a “TorchGeo is the future, throw out Rasterio” piece. It’s a breakdown of where the trade actually pays…