# I Replaced 30 Lines of Rasterio with 30 Lines of TorchGeo for Spatial Analysis — Here’s What… > Source: > Published: 2026-08-01 20:56:01+00:00 Member-only story **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 — loading 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…