Aurora GeoXAI Releases Geospatial AI Experimental MVP
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Impulso Digital Soluções em I.A. Ltda. releases Aurora Geointelligence XAI v0.1.0, an experimental geospatial image analysis MVP that runs locally on CPU with no GPU, tokens, database, or paid services required, and requires Python 3.11 or later. This version supports PNG, JPEG, TIFF, and GeoTIFF images, with a per-file limit of 100 MB and 50 million pixels, is built on NumPy, Rasterio, Pillow, and scikit-learn, contains no PyTorch deep learning engine, and does not implement model-level XAI. The author explicitly states that it is not suitable for production or judicial forensics scenarios. The reference CI workflow in the repository is only placed in the docs directory and has not been enabled in .github/workflows/, so there is no CI status badge to display.
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- Hacker News AIShow HN: Aurora GeoXAI – Sovereign geospatial AI built out of frustration
Aurora Geointelligence XAI is an experimental, CPU-first Python MVP (v0.1.0) that reads PNG, JPEG, TIFF and GeoTIFF imagery, records available raster metadata, applies scikit-learn MiniBatchKMeans to preview RGB pixels, optionally computes NDVI/NDWI from user-selected bands, and exports images with a traceable JSON report. It uses NumPy, Rasterio, Pillow, scikit-learn and Gradio, and explicitly does not use PyTorch, provide validated model-level explainability, or constitute a production or forensic system. The repository pairs the code with English and Portuguese manifestos on technological sovereignty, stating that no MLCommons benchmark, Finep/CDTI partnership, institutional endorsement or legal admissibility is claimed.
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