# Geospatial Foundation Models for Agriculture & Environment
> Explore how large-scale AI foundation models like NASA's Prithvi are transforming geospatial science, precision agriculture, and environmental monitoring.

Tags: geospatial-ai, foundation-models, remote-sensing, precision-agriculture, environmental-science, machine-learning, satellite-imagery, nasa-prithvi
## Foundation Models for Geospatial Analysis
* Lecture on how large-scale AI models are transforming geospatial science in agriculture and environmental studies.

## Understanding Foundation Models
* Large-scale pre-trained AI models adaptable to many tasks using self-supervised learning.
* Reduces the need for large task-specific labelled datasets.
* Key applications: Crop mapping, flood detection, land use, and soil analysis.

## Geospatial Data & Remote Sensing
* Data types: Optical (Sentinel-2, Landsat), Synthetic Aperture Radar (SAR), LiDAR, and Hyperspectral imagery.
* Includes time-series stacks for change detection and phenology.

## AI Architectures for Geospatial Data
* **Vision Transformer (ViT):** Uses self-attention on image patches.
* **Masked Autoencoder (MAE):** Pre-trains by reconstructing masked pixels.
* **Multi-modal Transformers:** Fuses optical, SAR, and time-series data.

## Precision Agriculture Applications
* Crop type mapping and yield prediction using Sentinel-2.
* **Soil Analysis:** Variable rate nitrogen (N) application based on digital soil maps and plant phenology.
* In-season nitrogen management using remote sensing at the Z31 growth stage.

## Environmental Monitoring
* Land Use & Land Cover (LULC) classification.
* Deforestation monitoring using SAR.
* Flood extent mapping and wildfire burn severity assessment.
* Carbon stock estimation.

## Case Study: NASA & IBM Prithvi Model
* Pre-trained on 6 years of Harmonized Landsat-Sentinel (HLS) data.
* 100M parameter model available on Hugging Face.
* Achieves SOTA results on flood mapping with only 10% of typical labelled data.

## Challenges and Future Trends
* Challenges: Data heterogeneity, lack of labelled benchmarks, and computational costs.
* Future: Integration with IoT and drones, and increased use in developing countries for low-cost monitoring.
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