Granite Geospatial Canopyheight

--- license: apache-2.0 library_name: terratorch pipeline_tag: image-feature-extraction --- # Model Card for granite-geospatial-canopy-height [<b><i>>>Try it on Colab<<</i></b>](https://colab.research.google.com/github/ibm-granite/granite-geospatial-canopyheight/blob/main/notebooks/canopyheight_inference_getting_started.ipynb) <p align="center" width="100%"> <img src="canopyheight_map.png" width="600"> </p> The granite-geospatial-canopy-height model is a fine-tuned geospatial foundation model for predicting the canopy height (i.e., height of trees and vegetationon the Earth's surface) using optical satellite imagery. Canopy Height is an important component to quantify the carbon cycle and is crucial for estimating crop yields, monitoring forest timber production, and quantifying the carbon sequestered by nature-based actions. The model predicts canopy height from the Harmonized Landsat and Sentinel-2 (HLS) L30 optical satellite imagery and is fine-tuned using training labels from the Global Ecosystem Dynamics Investigation (GEDI) L2A. Uniquely, the model has been fine-tuned using HLS and GEDI data collected from 15 biomes across the globe. Please see the Model Description below for more details. ## How to Get Started with the Model This model was trained using [Terratorch](https://github.com/IBM/terratorch). We make the weights as well as the configuration file that defines it available. You can use it easily with Terratorch through: ```python from terratorch.cli_tools import LightningInferenceModel ckpt_path = hf_hub_download(repo_id="ibm-granite/granite-geospatial-canopyheight", filename="pytorch_model.bin") config_path = hf_hub_download(repo_id="ibm-granite/granite-geospatial-canopyheight", filename="config.yaml") model = LightningInferenceModel.from_config(config_path, ckpt_path) inference_results, input_file_names = model.inference_on_dir(<input_directory>) ``` For more details, check out the [Getting Started Notebook](https://github.com/ibm-granite/granite-geospatial-canopyheight/blob/main/notebooks/canopyheight_getting_started.ipynb) which guides the user through three experiments: 1. Zero-shot for all biomes 2. Zero-shot for a single biome 3. Few-shot for a single biome ## Model Description The granite-geospatial-canopyheight model is a geospatial foundation model that has been fine-tuned using HLS and GEDI data to perform regress...

Source context: 14 downloads · 19 likes · Pipeline image-feature-extraction · Library terratorch · Repo ibm-granite/granite-geospatial-canopyheight