Skip to content
intermediatetext

BigEarthNet Remote Sensing Classification

Multi-label land cover classification from Sentinel-2 imagery (Sumbul et al., IGARSS 2019). Classify satellite patches into 43 land cover classes following the CORINE taxonomy.

Try it live — no install

Boot the real Potato server in your browser (WebAssembly) and annotate with this exact config. Nothing leaves your machine.

Run live in your browser

Get This Design

View on GitHub(opens in a new tab)

Clone or download from the repository

Quick start:

bash
git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/image/aerial/bigearth-net
potato start config.yaml

Configuration File

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# BigEarthNet Remote Sensing Classification Configuration
# Based on Sumbul et al., IGARSS 2019

annotation_task_name: "BigEarthNet Land Cover Classification"
task_dir: "."

data_files:
  - "sample-data.json"

item_properties:
  id_key: "id"
  text_key: "image_url"
  context_key: "context"

user_config:
  allow_all_users: true

annotation_schemes:
  - annotation_type: "multiselect"
    name: "artificial_surfaces"
    description: "Select visible artificial surfaces"
    labels:
      - name: "urban_fabric"
        tooltip: "Continuous and discontinuous urban fabric"
      - name: "industrial"
        tooltip: "Industrial or commercial units"
      - name: "road_rail"
        tooltip: "Road and rail networks"
      - name: "port"
        tooltip: "Port areas"
      - name: "airport"
        tooltip: "Airports"
      - name: "construction"
        tooltip: "Construction sites"
      - name: "green_urban"
        tooltip: "Green urban areas"
      - name: "sport_leisure"
        tooltip: "Sport and leisure facilities"

  - annotation_type: "multiselect"
    name: "agricultural"
    description: "Select agricultural land types"
    labels:
      - name: "arable_land"
        tooltip: "Arable land (crops)"
      - name: "permanent_crops"
        tooltip: "Vineyards, fruit trees, olive groves"
      - name: "pastures"
        tooltip: "Pastures"
      - name: "heterogeneous"
        tooltip: "Mixed agricultural areas"

  - annotation_type: "multiselect"
    name: "forest"
    description: "Select forest types"
    labels:
      - name: "broad_leaved"
        tooltip: "Broad-leaved forest"
      - name: "coniferous"
        tooltip: "Coniferous forest"
      - name: "mixed_forest"
        tooltip: "Mixed forest"

  - annotation_type: "multiselect"
    name: "natural"
    description: "Select natural vegetation and open spaces"
    labels:
      - name: "natural_grassland"
        tooltip: "Natural grasslands"
      - name: "moors_heathland"
        tooltip: "Moors and heathland"
      - name: "sclerophyllous"
        tooltip: "Sclerophyllous vegetation"
      - name: "transitional_woodland"
        tooltip: "Transitional woodland-shrub"
      - name: "bare_rock"
        tooltip: "Bare rocks"
      - name: "sparsely_vegetated"
        tooltip: "Sparsely vegetated areas"

  - annotation_type: "multiselect"
    name: "water"
    description: "Select water bodies"
    labels:
      - name: "inland_waters"
        tooltip: "Inland waters"
      - name: "marine_waters"
        tooltip: "Marine waters"
      - name: "coastal_lagoons"
        tooltip: "Coastal lagoons"
      - name: "estuaries"
        tooltip: "Estuaries"

interface_config:
  item_display_format: "<img src='{{text}}' style='max-width:100%; max-height:500px;'/><br/><small>{{context}}</small>"

output_annotation_format: "json"
output_annotation_dir: "annotations"

Sample Data

json
[
  {
    "id": "ben_001",
    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Everest_North_Face_toward_Base_Camp_Tibet_Luca_Galuzzi_2006.jpg/1200px-Everest_North_Face_toward_Base_Camp_Tibet_Luca_Galuzzi_2006.jpg",
    "context": "Sentinel-2 satellite patch (120x120 pixels). Classify all visible land cover types according to CORINE Land Cover taxonomy."
  },
  {
    "id": "ben_002",
    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/1/1a/24701-nature-702704.jpg/1200px-24701-nature-702704.jpg",
    "context": "Satellite image patch. Select all applicable land cover classes."
  }
]

// ... and 1 more items

Dataset & paper

Sumbul et al., IGARSS 2019

Official dataset(opens in a new tab)Read the paper(opens in a new tab)

Citation (BibTeX)

bibtex
@inproceedings{sumbul2019bigearthnet,
    title = "{BigEarthNet}: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding",
    author = "Sumbul, Gencer  and Charfuelan, Marcela  and Demir, Beg{\"u}m  and Markl, Volker",
    booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)",
    pages = "5901--5904",
    year = "2019",
    organization = "IEEE",
    url = "https://ieeexplore.ieee.org/document/8900532"
}

Details

Annotation Types

multiselect

Domain

Remote SensingEarth Observation

Use Cases

Land Cover ClassificationSatellite Image AnalysisEnvironmental Monitoring

Tags

bigearthsatellitesentinel-2land-coverremote-sensingigarss2019

Found an issue or want to improve this design?

Open an Issue(opens in a new tab)

Related Designs

DOTA Aerial Image Object Detection

Oriented bounding box detection in aerial images (Xia et al., CVPR 2018). Detect 15 object categories with arbitrary orientations including planes, ships, vehicles, and sports facilities.

multiselectradio

FLAIR: French Land Cover from Aerospace Imagery

Land use and land cover classification from high-resolution aerial imagery. Annotators classify the primary land use category of aerial image patches and identify any secondary land uses present. Based on the FLAIR dataset from the French National Institute of Geographic and Forest Information (IGN).

multiselectradio

xView Satellite Object Detection

Large-scale overhead imagery object detection (Lam et al., arXiv 2018). Detect 60 object classes including vehicles, buildings, and infrastructure from satellite images.

multiselectradio