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Showcase/xView Satellite Object Detection
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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.

Labels:outdoornatureurbanpeopleanimal+−

Configuration Fileconfig.yaml

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

yaml
# xView Satellite Object Detection Configuration
# Based on Lam et al., arXiv 2018

annotation_task_name: "xView Satellite Object Detection"
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: "vehicles"
    description: "Select visible vehicle types"
    labels:
      - name: "small_car"
        tooltip: "Passenger vehicles"
      - name: "bus"
        tooltip: "Buses"
      - name: "truck"
        tooltip: "Trucks and cargo vehicles"
      - name: "cargo_plane"
        tooltip: "Cargo aircraft"
      - name: "helicopter"
        tooltip: "Helicopters"
      - name: "small_aircraft"
        tooltip: "Small planes"
      - name: "ship"
        tooltip: "Ships and vessels"
      - name: "motorboat"
        tooltip: "Motorboats"
      - name: "train"
        tooltip: "Trains"

  - annotation_type: "multiselect"
    name: "structures"
    description: "Select visible structures"
    labels:
      - name: "building"
        tooltip: "Buildings"
      - name: "storage_tank"
        tooltip: "Storage tanks"
      - name: "shipping_container"
        tooltip: "Shipping containers"
      - name: "tower"
        tooltip: "Towers, antennas"
      - name: "helipad"
        tooltip: "Helipads"
      - name: "facility"
        tooltip: "Industrial facilities"

  - annotation_type: "multiselect"
    name: "equipment"
    description: "Select visible equipment"
    labels:
      - name: "crane"
        tooltip: "Cranes"
      - name: "construction_vehicle"
        tooltip: "Construction vehicles"
      - name: "agricultural_vehicle"
        tooltip: "Farm equipment"
      - name: "engineering_vehicle"
        tooltip: "Engineering vehicles"

  - annotation_type: "radio"
    name: "image_quality"
    description: "Rate the image quality"
    labels:
      - name: "clear"
        tooltip: "Clear, high-resolution"
      - name: "moderate"
        tooltip: "Some noise or blur"
      - name: "poor"
        tooltip: "Significant quality issues"

  - annotation_type: "text"
    name: "bbox_list"
    description: "List bounding boxes: class,x,y,w,h (one per line)"

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 Datasample-data.json

json
[
  {
    "id": "xview_001",
    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/1/10/Empire_State_Building_%28aerial_view%29.jpg/800px-Empire_State_Building_%28aerial_view%29.jpg",
    "context": "Overhead satellite imagery. Detect all visible objects including vehicles, buildings, and equipment."
  },
  {
    "id": "xview_002",
    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/1/1e/San_Francisco_from_the_Marin_Headlands_in_March_2019.jpg/1200px-San_Francisco_from_the_Marin_Headlands_in_March_2019.jpg",
    "context": "Satellite view. Mark all xView object categories visible."
  }
]

// ... and 1 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

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

Dataset & paper

Lam et al., arXiv 2018

Official dataset ↗Read the paper ↗

Citation (BibTeX)

bibtex
@article{lam2018xview,
    title = "{xView}: Objects in Context in Overhead Imagery",
    author = "Lam, Darius  and Kuzma, Richard  and McGee, Kevin  and Dooley, Samuel  and Laiber, Michael  and Ber, Matthew  and Manjunatha, Varun  and Naber, Jonathan",
    journal = "arXiv preprint arXiv:1802.07856",
    year = "2018",
    url = "https://arxiv.org/abs/1802.07856"
}

Details

Annotation Types

multiselectradiotext

Domain

Remote SensingSatellite Imagery

Use Cases

Object DetectionSatellite AnalysisGeospatial Intelligence

Tags

xviewsatelliteoverheadobject-detectiongeospatial

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