Granite Geospatial Canopy Height Model
| Property | Value |
|---|---|
| Framework | PyTorch |
| License | Apache-2.0 |
| Paper | Research Paper |
| Downloads | 1,040 |
What is granite-geospatial-canopyheight?
The granite-geospatial-canopyheight is an advanced AI model designed to predict tree and vegetation heights using satellite imagery. Built on IBM's Terratorch framework, it leverages the Harmonized Landsat and Sentinel-2 (HLS) L30 optical satellite imagery to make accurate predictions of canopy heights, crucial for environmental monitoring and carbon cycle analysis.
Implementation Details
The model is built upon a Swin-B transformer backbone, chosen specifically for its advantages in handling geospatial data. It implements a fine-tuned UPerNet architecture adapted for pixel-wise regression, trained on data from 15 different global biomes.
- Uses SimMIM self-supervised learning with masked input reconstruction
- Features windowed attention for improved computational efficiency
- Implements hierarchical merging for better inductive bias
- Includes Pixel Shuffle layers for precise upscaling
Core Capabilities
- Zero-shot prediction across multiple biomes
- Few-shot learning capabilities for specific regions
- Cloud-free snapshot analysis of spectral bands
- Integration with NASA's HLS and GEDI datasets
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines Swin-B transformer architecture with UPerNet for geospatial analysis, offering superior resolution and computational efficiency compared to traditional ViT models. It's specifically trained on data from 15 global biomes, making it highly versatile for different geographical regions.
Q: What are the recommended use cases?
The model is ideal for estimating crop yields, monitoring forest timber production, quantifying carbon sequestration, and supporting nature-based climate action initiatives. It's particularly useful for environmental scientists, forestry managers, and climate researchers.
