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.
GEDI L3 and L4 Tutorials
pyGEDI is a Python Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) mission, data extraction, analysis, processing and visualization.
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".
SlideRule Earth Example Notebooks: On-demand, cloud-based processing of satellite mission data (NASA ICESat-2, GEDI, ArcticDEM/REMA, HLS)
a Python code completion plugin for gedit3+
A Julia package for working with ICESat-2 & GEDI data as part of my PhD research
Python tools to process spaceborne lidar (GEDI and ICESAT2) for land (pySL4Land) applications
CounterGeDi is a pipeline that aims at controlling the counter speech generated to make it emotional, polite and detoxified. Paper accepted at IJCAI 2022.
Global Ecosystem Dynamics Investigation (GEDI) L2A/L2B -> GeoParquet & GeoDataFrame/Xarray
Python package for analyzing NASA GEDI data. GEDI is a LiDAR dataset that is acquired using the International Space Station.
Aboveground Biomass Density Estimation Using Deep Learning: Insight from NEON Ground-Truth Data and Simulated GEDI Waveform
generates rasters of canopy height from the Global Ecosystem Dynamics Investigation (GEDI) mission
The CH-GEE app generates 10 m resolution canopy height maps by integrating GEDI Rh metrics with multi-source remote sensing data (radar, optical, and topographical features)
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