This repository contains the code used in the paper: A high-resolution canopy height model of the Earth. Here, we developed a model to estimate canopy top height anywhere on Earth. The model estimates canopy top height for every Sentinel-2 image pixel and was trained using sparse GEDI LIDAR data as a reference.
This repository provides the code used to create the results presented in "Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles".
a python code completion plugin for gedit3+
Python tools to process spaceborne lidar (GEDI and ICESAT2) for land (pySL4Land) applications
A toolbox for processing and providing Global Ecosystem Dynamics Investigation (GEDI) L2A-B and L4A-C data
A versatile framework for GEDI geolocation correction at multiple correction modes and criteria.
Extract data from Global Ecosystem Dynamics Investigation (GEDI) L2A/L2B files into GeoParquet
Python package for analyzing NASA GEDI data. GEDI is a LiDAR dataset that is acquired using the International Space Station.
Deep learning for wall-to-wall forest biomass mapping: GEDI L4A footprints supervising Sentinel-1/2 fusion over NW Iberia.
Python scripts for extracting GEDI and ICESat-2 data from HDF to shapefiles
Reproducible fairness-aware and explainable student-risk modeling toolkit for online higher education, with robustness, Pareto governance, SHAP, GEDI, and FAIR controls.
Validation-first canopy-height model for wildfire fuels: GEDI + Sentinel-2 + XGBoost, spatial CV, cross-region transfer, conformal prediction intervals
Library for querying GEDI data stored in GeoParquet files
Cross-region forest aboveground biomass transfer with GEDI L4A and AlphaEarth embeddings: source-domain prediction, zero-shot geographic transfer, and target label-efficiency under USA-to-China domain shift.
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