# acgeospatial/awesome-earthobservation-code

A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery

Repository: https://github.com/acgeospatial/awesome-earthobservation-code
Canonical: https://ross.abutalabs.com/products/awesome-earthobservation-code
Language: HTML
License: CC0-1.0
License Family: permissive
Topics: awesome-list, awesome, earth-observation, satellite-imagery, google-earth-engine, geospatial-data, satellite-data, remote-sensing
Last push: 2026-05-13T19:36:13+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 82, release rhythm 35, longevity 100
- inputs: {"age_days": 2332, "days_push": 112, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1378, forks 251 (observed 2026-08-28T04:04:33.644950+00:00)

## What it is
A curated awesome-list of tools, tutorials, code, projects, and links for Earth Observation and geospatial satellite imagery. It aggregates resources across Python, R, Google Earth Engine, GDAL, QGIS, deep learning, SAR, LiDAR, and cloud-native geospatial topics.

## Use cases
- find python libraries for processing satellite imagery
- learn remote sensing and earth observation from tutorials
- discover google earth engine resources and examples
- find tools for SAR and LiDAR data processing
- locate open satellite imagery datasets
- get started with geospatial deep learning
- find cloud-native geospatial tools like STAC and COG

## When to choose
- starting out in earth observation and needing a curated entry point
- looking for a broad catalog of EO tools, tutorials, and datasets in one place
- exploring resources across multiple EO ecosystems like GEE, Open Data Cube, and QGIS

## When to avoid
- needing actively maintained, up-to-date tooling rather than a link list
- wanting a single cohesive library or application instead of curated links
- requiring guaranteed working links, since many listed resources are years old

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools, data-science, geospatial, image-processing, machine-learning
- domain: awesome-lists, education
- platform: cross-platform, python
- tags: awesome-list, earth-observation, satellite-imagery, remote-sensing, google-earth-engine, gdal, qgis, curated-links, geospatial, web-server

## Member repositories
- acgeospatial/awesome-earthobservation-code (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:33.644950+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:40:21.325598+00:00, confidence not recorded.
  - readme: https://github.com/acgeospatial/awesome-earthobservation-code (fetched 2026-08-28T04:04:33.644950+00:00, sha a24e87ee4ce3)
- Data as of 2026-08-30T08:39:29.467469+00:00.