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The GeoTessera project produces 128-channel geospatial embeddings from Sentinel satellite imagery. The dataset is tiled at 0.1-degree resolution across the globe, covering 9 years and comprising roughly 3.8 million tiles, each containing embeddings and scale-factor files.
GPU acceleration is the default assumption for machine learning inference. But Intel’s AMX (Advanced Matrix Extensions) may close the gap. AMX is built into recent Xeon processors, which are available from Azure. Can they compete with similarly priced GPU-based machines for the Tessera pipeline?
My throw-away comment at the end of my earlier post shows my scepticism that the JSON file approach was really viable.
