Landcover classification on sentinel-2 data with Prithvi, EfficientNet-Unet and OSM / CNES Landcover labels.
Fine-tuning Geospatial Foundation Models (Prithvi, TerraMind) for building footprint segmentation from Sentinel-2 using TerraTorch — Algiers case study
Geospatial-AI corn yield forecasting for the U.S. Corn Belt. Team project fine-tuning NASA/IBM Prithvi-EO-2.0-600M with LoRA, fused with weather/soil/drought features and calibrated uncertainty cones. State-level RMSE ~3–5 bu/ac — competitive with USDA WASDE. CSU Geospatial AI Hackathon 2026.
Deployment and explainability analysis of NASA-IBM's Prithvi-EO-2.0 for flood segmentation, evaluated on Sen1Floods11 against an NDWI baseline.
Temporal crop analysis and multi-cropping detection using Prithvi EO 2.0 embeddings, Sentinel-2 imagery, and unsupervised learning.
Modifications to the mining-focused Prithvi code shared by Emmanuel and Aditya
NYC Hurricane Ida pluvial-flood pattern detector. Fine-tune of NASA-IBM Prithvi-EO 2.0 (300M params). Apache-2.0.
Geospatial foundation models, side by side — run SAM, Grounding DINO, Prithvi-EO & Clay on satellite/aerial imagery and score them against ground truth. Inference-only, runs locally.
Geo-MLOps: PEFT Benchmark for Geospatial Foundation Models | 75 experiments on Prithvi-100M — LoRA fails, Houlsby dominates, modality > method
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