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Prithvi4QR is a fine-tuned foundation model for decision support in large-scale disaster response. It segments roads, buildings, and water from aerial images, aiding resource allocation.
Foundation model for Earth Observations. Semantic Segmentation for maps in the Landcover.ai dataset.
BurnScape: Empowering Wildfire and Land Management with AI-driven Burned Area Detection.
"It's a heavy lift and processing, but we have got this!" ~Atlas, the Greek God of strength and endurance.
Tagline:High-Precision Landcover Mapping Using UNet++: Optimizing Accuracy in Multi-Class Segmentation of Aerial Imagery
We built a high-resolution land cover mapping model with a Swin Transformer that classifies roads, buildings, and vegetation for urban planning and disaster response.
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