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api-gedi

API-GEDI

autokeras

AutoML library for deep learning

awesomegedilidar

Awesome Gedi Lidar

digital-elevation-model

To transform, project, visualize, and read Digital Elevation Models (ASTERGDEMv2.0 and EUDEMv1.1). [简单实用] 数字高程模型的变换、投影、可视化和读取。

earthengine-community

Tutorials and content created by Earth Engine users, for Earth Engine users

gedi

An original package of the gammachirp envelope distortion index (GEDI) and the multi-resolution GEDI (mr-GEDI)

gedi-1

gedi-2

gedi-3

gedi-bdl

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".

gedi-biomass-mapping

gedi-experiment

gedi-l4a

gedi4r

An R package for NASA’s GEDI Level 4A data download, processing and visualization.

gedi_gee

GEDI processing in GEE

gedi_hdf5_dataset

Exploring data from Global Ecosystem Dynamics Investigation (GEDI) of the ISS

gedi_processing

Processing pipeline for downloading, analyzing, and gridding GEDI data

gedi_tools

gedi_tutorials

GEDI L3 and L4 Tutorials

gedi_workshop

GEDI_1B,GEDI_2A,GEDI_2B

gedicalibrator

Calibrate GEDI shot locations using reference data

gedienginer

Workflow to model GEDI PAI with Google Earth Engine and R

gedil2a_l4a

gee_s1_ard

Creates an analysis ready sentinel-1 SAR image collection in Google Earth Engine by applying additional border noise correction, speckle filtering and radiometric terrain normalization.

geebook

Earth Engine and Geemap: Geospatial Data Science with Python

geemap

A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets.

geopandas

Python tools for geographic data

geoviews

Simple, concise geographical visualization in Python

glcm

本实验的主要目的是基于遥感图像计算灰度共生矩阵，并基于该矩阵计算多种纹理特征。所有的计算结果已与ENVI结果进行对比，实验结果一致。

global-canopy-height-model

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.