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awesome-earthobservation-code

A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery
https://github.com/acgeospatial/awesome-earthobservation-code

Last synced: 24 days ago
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A footnote on awesome

GEDI

ARD links

GEDI

  • S1_S2_ARD_code_list - A curated list supporting the use of Sentinel-1 and Sentinel-2 analysis-ready data (ARD) via application programming interface (API)

Climate and weather based resources

EUMETlab

  • EUMETlab - This page contains groups of code repositories that have been made open to the public by EUMETSAT and our collaborators.
  • atmosphere - LTPy - Learning tool for Python on Atmospheric Composition Data is a Python-based training course on Atmospheric Composition Data. The training course covers modules on data access, handling and processing, visualisation as well as case studies.
  • sentinel-downloader - Python-based Sentinel satellite data downloader. This script allows for batch downloading of Sentinel data selected by various criteria include date, location, sensor, child products, flags and more.
  • olci-iop-processor - Code to produce Inherent Optical Properties from Level-2 OLCI data.

Testing your code

  • unidata on GOES-16 - This notebook shows how to make a true color image from the GOES-16 Advanced Baseline Imager (ABI) level 2 data. We will plot the image with matplotlib and Cartopy.Python
  • MetPy docs
  • s3 tools - A collection of sentinel 3 processing tools Python
  • eumetsat -python - Shows how to read and plot satellite data from EUMETSAT NETCDF files Python
  • MetPy - MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data. Python
  • aqua-monitor - Monitoring surface water changes from space at global scale. Also checkout the app Python
  • Ocean Color - Modis - introduction to accessing and plotting ocean color satellite data from MODIS Python
  • Climate data science - Climate Data Science and Earth Observation with Python
  • COST-EUMETSAT-Training - Material, data and presentations for the COST-EUMETSAT training school
  • eumetsat - Tools for downloading and processing satellite images from EUMETSAT
  • coda_eumetsat - Coda Eumetsat (coda.eumetsat.int) client for downloading data
  • ai4eo-forecast - Developing an open source library to compare Earth Observation and weather forecast services with the actual measurements and assess the accuracy of the forescast Python

Data

GEDI

  • gibs - This is EO
  • awesome-gee-community-datasets - Community Datasets added by users and made available for use at large
  • Environmental_Intelligence - Data for Environmental Intelligence: A mega list of Earth System Datasets covering earth observations, climate, water, forests, biodiversity, ecology, protected areas, natural hazards, marine and the tracking of UN's Sustainable Development Goals

Deep learning and Machine Learning

Testing your code

DEM projects

EUMETlab

  • The Stereo Pipeline (NASA) - The NASA Ames Stereo Pipeline (ASP) is a suite of free and open source automated geodesy and stereogrammetry tools designed for processing stereo imagery captured from satellites
  • Tin Terrain - A command-line tool for converting heightmaps in GeoTIFF format into tiled optimized meshes.
  • TauDEM - Terrain Analysis Using Digital Elevation Models (TauDEM) software for hydrologic terrain analysis and channel network extraction. Docs
  • DEM.net - Digital Elevation model library in C#. 3D terrain models, line/point Elevations, intervisibility reports. Docs
  • DSM2DTM - Code for the paper - Comparison of Digital Building Height Models Extracted from AW3D, TanDEM-X, ASTER, and SRTM Digital Surface Models over Yangon City Python
  • dsm2dtm - Python library for converting Digital Surface Models (DSMs) to Digital Terrain Models (DTMs).

Earth Engine

Testing your code

Earth Observation coding on YouTube

Testing your code

  • xArray at PyConUK2018 - Robin Wilson - Processing thousands of satellite images to understand air quality in the UK - it's efficient and easy with XArray
  • Visualizing & Analyzing Earth Science Data Using PyViz & PyData - Julia Signell - In this talk, we'll work through some specific workflows and explore how various tools - such as Intake, Dask, Xarray, and Datashader - can be used to effectively analyze and visualize these data. Working from within the notebook, we'll iteratively build a product that is interactive, scalable, and deployable.
  • Hands on Satellite Imagery 2019 edition - Sara Safavi - In this tutorial, gain hands-on experience exploring Planet’s publicly-available satellite imagery and using Python tools for geospatial and time-series analysis of medium- and high-resolution imagery data. Using free & open source libraries, learn how to perform foundational imagery analysis techniques and apply these techniques to real satellite data.
  • Python from space - Katherine Scott - In this talk we will work through a jupyter notebook that covers the satellite data ecosystem and the python tools that can be used to sift through and analyze that data. Topics include python tools for using Open Street Maps data, the Geospatial Data Abstraction Library (GDAL), and OpenCV and NumPy for image processing.
  • Remote Sening with Python in Jupyter - In this video we're looking at using Google Earth Engine in Jupyter with the Python API.
  • Writing Image Processing Algorithms with ArcGIS/ArcPy - Jamie Drisdelle - learn how your algorithms can integrate with the raster processing and visualization pipelines in ArcGIS. We’ll demonstrate the concept and discuss the API by diving deep into a few interesting examples with a special focus on multidimensional scientific rasters.
  • Google Earth Engine Python - Qiusheng Wu - Introducing the geemap Python package for interactive mapping with Google Earth Engine and ipyleaflet.
  • Google Earth Engine EE101 Condensed - Noel Gorelick - Introduction to the Earth Engine API and a conceptual overview of key functionality such as compositing, reducing, mapping, zonal statistics and cluminating with building a small app.
  • Image classification with RandomForests using the R language
  • GeoPython 2019 stream - 17:23 Machine Learning for Land Use/Landcover Statistics of Switzerland (Adrian Meyer), 50:58 How to structure geodata, 1:18:13 Terrain segmentation with label bootstrapping for lidar datasets, case of doline detection (Rok Mihevc), 2:34:41 Bias in machine learning, 3:06:23 Software for planning research aircraft missions (Reimar Bauer), 3:32:38 How Technology Moves Fast (PJ Hagerty) , 5:02:05 Spotting Sharks with the TensorFlow Object Detection API (Andrew Carter), 5:40:23 Center for Open Source Data and AI Technologies (CODAIT), 6:03:40 Bayesian modeling with spatial data using PyMC3 (Shreya Khurana) (Sound at 6:04:23 ^^), 7:02:45 Understanding and Implementing Generative Adversarial Networks(GANs) (Anmol Krishan Sachdeva), 7:37:00 Messaging with Satellites from Anywhere on the Planet (Andrew Carter), 8:04:52 Automation of the definition and optimizatino of census sampling areas using AREA (GRID3) (Freja Hunt), 8:35:26 Coastline Mapping with Python, Satellite Imagery and Computer Vision (Rachel Keay)
  • Google Earth Engine in QGIS - This playlist looks at the GEE plugin for QGIS
  • Handling and analysing vector and raster data cubes with R - Edzer Pebesma (Institute for Geoinformatics, University of Münster) Summary: vector and raster data cubes include vector and raster data as special cases, but extend this to vector time series, OD matrices, multi-band raster data, multi-band raster time series, multi-attribute vector or raster time series, and more general to array data where one ore more dimensions are associated with space and/or with time. Examples come from pretty much all areas dealing with spatiotemporal data. This tutorial will go through a large number of examples to illustrate this idea, mostly focusing on the packages stars and sf and those supporting their classes (like tmap, mapview, gstat, ggplot2).
  • QiushengWu's youtube - This youtube channel has pretty much everything you need Earth Engine, git, colab, Python, Geoscience. Highest quality stuff.
  • The OpenDataCube Conference 2021 - Playlist from the 2021 conference
  • Dask and Geopandas - Scalable geospatial data analysis with Dask| Dask Summit 2021
  • Visualizing & Analyzing Earth Science Data Using PyViz & PyData - Julia Signell - In this talk, we'll work through some specific workflows and explore how various tools - such as Intake, Dask, Xarray, and Datashader - can be used to effectively analyze and visualize these data. Working from within the notebook, we'll iteratively build a product that is interactive, scalable, and deployable.
  • Python from space - Katherine Scott - In this talk we will work through a jupyter notebook that covers the satellite data ecosystem and the python tools that can be used to sift through and analyze that data. Topics include python tools for using Open Street Maps data, the Geospatial Data Abstraction Library (GDAL), and OpenCV and NumPy for image processing.
  • Google Earth Engine in QGIS - This playlist looks at the GEE plugin for QGIS
  • The OpenDataCube Conference 2021 - Playlist from the 2021 conference

Earth Observation Introduction

EO code Competitions

GEDI

EO Geospatial companies or orgs making big contributions

GEDI

GDAL of course

Testing your code

Great Github accounts

GEDI

InSAR

GEDI

  • Pyrocko - Can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, modelling of InSAR, GPS data and dynamic waveforms, or for seismic source characterization.
  • ISCE - InSAR Scientific Computing Environment version 3 alpha
  • LiCSBAS - LiCSBAS package to carry out InSAR time series analysis using LiCSAR products
  • MintPy - Miami InSAR time-series software in Python
  • InSARFlow - Parallel InSAR processing for Time-series analysis
  • PyRate - A Python tool for estimating velocity and time-series from Interferometric Synthetic Aperture Radar (InSAR) data.
  • ARIRA-tools - Tools for exploiting ARIA standard products Python
  • ISCE_utils - Small utility scripts for working with InSAR Scientific Computing Environment (ISCE) products Python
  • ROI_PAC-Sentinel1 - InSAR processing of Sentinel-1 using ROI_PAC
  • insar_scripts - Useful scripts for working with roipac data Python
  • isce2 - InSAR Scientific Computing Environment version 2 Python
  • snap2stamps - Using SNAP as InSAR processor for StaMPS

Interesting Non EO parts other languages

GEDI

Interesting Non EO parts Python

GEDI