Mohit Chaudhary @GeniusM30

India is urbanizing faster than any government can manually track. Informal settlements emerge overnight, transit corridors get encroached upon, and agricultural land silently converts to concrete. Satellite imagery analyzed by AI is the only scalable answer. This project fine-tunes Qwen2.5-VL-72B-Instruct on AMD MI300X using LoRA (r=16) on 8,000 curated Sentinel-2 satellite images from the NuTonic geospatial dataset, filtered specifically for high urban fraction tiles including 437 India-specific examples. The AMD MI300X with 192GB HBM3 is what made 72B multimodal fine-tuning feasible at all. The system analyzes satellite tiles and returns annotated images with bounding boxes drawn over detected urban clusters, built area fraction estimates, and plain-language descriptions that urban planners and policy makers can actually read and act on. A corridor analysis mode accepts multiple tiles and generates a PDF report synthesizing findings across a transit route. The motivation is India's RRTS infrastructure. The Delhi-Meerut corridor is operational. Whether transit investment is actually generating expected urban densification around stations is a policy question that currently has no scalable monitoring answer. This model is a proof of concept for satellite AI filling that gap.

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