# chrieke/awesome-satellite-imagery-datasets

🛰️ List of satellite image training datasets with annotations for computer vision and deep learning

Repository: https://github.com/chrieke/awesome-satellite-imagery-datasets
Canonical: https://ross.abutalabs.com/products/awesome-satellite-imagery-datasets
License: MIT
License Family: permissive
Topics: satellite-imagery, computer-vision, deep-learning, remote-sensing, earth-observation, machine-learning, object-detection, instance-segmentation
Archived: true
Last push: 2022-07-14T18:02:46+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3046, "days_push": 1511, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3915, forks 666 (observed 2026-08-28T04:08:28.913368+00:00)

## What it is
A curated awesome-list of annotated aerial and satellite imagery datasets for computer vision and deep learning, organized by task (instance segmentation, object detection, semantic segmentation, scene classification). The repository is now archived, with pointers to more recently maintained dataset lists.

## Use cases
- find satellite imagery datasets for training deep learning models
- locate annotated aerial images for object detection
- find remote sensing datasets for semantic segmentation
- search for building footprint datasets
- find datasets for land cover classification
- discover benchmark datasets for earth observation research

## When to choose
- you need a quick curated index of satellite/aerial CV datasets
- you are starting a remote sensing deep learning project and want dataset options
- you want links to dataset papers and sources in one place

## When to avoid
- you need the newest datasets, since the list is archived and no longer updated
- you need the datasets themselves rather than links to them
- you need non-imagery geospatial data like tabular or vector-only sources

## Facets
- artifact type: dataset
- maturity: maintenance
- function: machine-learning, computer-vision, data-science
- domain: machine-learning, computer-vision, data-science
- platform: cross-platform
- tags: awesome-list, satellite-imagery, remote-sensing, earth-observation, object-detection, semantic-segmentation, instance-segmentation, geospatial, training-data, archived

## Member repositories
- chrieke/awesome-satellite-imagery-datasets (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:28.913368+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 2026-08-29T18:25:03.040393+00:00, confidence not recorded.
  - readme: https://github.com/chrieke/awesome-satellite-imagery-datasets (fetched 2026-08-28T04:08:28.913368+00:00, sha ff1409c715e9)
- Data as of 2026-08-30T08:39:29.467469+00:00.