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.

Archivo de configuraciónconfig.yaml

# DOTA Aerial Image Object Detection Configuration
# Based on Xia et al., CVPR 2018

annotation_task_name: "DOTA Aerial 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: "object_classes"
    description: "Select all object classes visible"
    labels:
      - name: "plane"
        tooltip: "Airplanes"
      - name: "ship"
        tooltip: "Ships and vessels"
      - name: "storage_tank"
        tooltip: "Storage tanks"
      - name: "baseball_diamond"
        tooltip: "Baseball diamonds"
      - name: "tennis_court"
        tooltip: "Tennis courts"
      - name: "basketball_court"
        tooltip: "Basketball courts"
      - name: "ground_track_field"
        tooltip: "Running tracks"
      - name: "harbor"
        tooltip: "Harbors"
      - name: "bridge"
        tooltip: "Bridges"
      - name: "large_vehicle"
        tooltip: "Large vehicles (trucks, buses)"
      - name: "small_vehicle"
        tooltip: "Small vehicles (cars)"
      - name: "helicopter"
        tooltip: "Helicopters"
      - name: "roundabout"
        tooltip: "Roundabouts"
      - name: "soccer_field"
        tooltip: "Soccer fields"
      - name: "swimming_pool"
        tooltip: "Swimming pools"

  - annotation_type: "radio"
    name: "difficulty"
    description: "Annotation difficulty"
    labels:
      - name: "easy"
        tooltip: "Clear, large objects"
      - name: "difficult"
        tooltip: "Small, occluded, or crowded"

  - annotation_type: "text"
    name: "oriented_bbox"
    description: "Oriented bounding box: x1,y1,x2,y2,x3,y3,x4,y4,class,difficulty"

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"

Datos de ejemplosample-data.json

[
  {
    "id": "dota_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": "Aerial image. Detect objects with oriented bounding boxes. Objects may appear at any angle."
  },
  {
    "id": "dota_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": "Aerial view. Mark all DOTA categories: planes, ships, vehicles, sports facilities, etc."
  }
]

// ... and 1 more items

Obtener este diseño

View on GitHub

Clone or download from the repository

Inicio rápido:

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

Detalles

Tipos de anotación

multiselectradiotext

Dominio

Remote SensingAerial Imagery

Casos de uso

Object DetectionOriented DetectionAerial Analysis

Etiquetas

dotaaerialoriented-bboxremote-sensingcvpr2018

¿Encontró un problema o desea mejorar este diseño?

Abrir un issue

Diseños relacionados

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

CUB-200-2011 Fine-Grained Bird Classification

Fine-grained visual categorization of 200 bird species (Wah et al., 2011). Annotate bird images with species labels, part locations, and attribute annotations.

multiselectradio