Skip to content
Showcase/xView Satellite Object Detection
advancedimage

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+−

配置文件config.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"

示例数据sample-data.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

获取此设计

View on GitHub

Clone or download from the repository

快速开始：

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

详情

标注类型

multiselectradiotext

领域

Remote SensingSatellite Imagery

应用场景

Object DetectionSatellite AnalysisGeospatial Intelligence

标签

xviewsatelliteoverheadobject-detectiongeospatial

发现问题或想改进此设计？

提交 Issue

相关设计

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

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