Remote Sensing Object Segmentation Dataset

Project Overview:

Objective

To establish a comprehensive dataset dedicated to the segmentation of various objects in remote sensing images, the aim is to enhance advancements in satellite imagery analysis, land cover classification, environmental monitoring, and urban planning.

Scope

Curate an extensive array of high-resolution satellite images showcasing diverse landscapes, including urban, rural, coastal, forest, and desert regions. Each image features pixel-wise annotations delineating specific objects such as buildings, roads, water bodies, vegetation, and vehicles. By maintaining a high level of detail and integrating sophisticated annotation techniques, this dataset accurately identifies and labels each object within the images. Additionally, advanced machine learning algorithms enhance the precision of these annotations, creating a comprehensive and valuable dataset suitable for a wide range of applications, from environmental monitoring to urban planning.

Remote Sensing Object Segmentation Dataset
Remote Sensing Object Segmentation Dataset
Remote Sensing Object Segmentation Dataset
Remote Sensing Object Segmentation Dataset

Sources

  • Collaborations with space agencies and satellite imaging companies.
  • Utilization of open-source satellite imagery platforms.
  • Procurement of specialized images capturing disaster zones, agricultural plots, and urban development.
case study-post
Remote Sensing Object Segmentation Dataset
Remote Sensing Object Segmentation Dataset

Data Collection Metrics

  • Total Satellite Images: 60,000
  • Urban Scapes: 15,000
  • Rural Regions: 12,000
  • Coastal Zones: 10,000
  • Forests: 10,000
  • Deserts: 8,000
  • Special Categories (e.g., disaster zones): 5,000

Annotation Process

Stages

  1. Image Pre-processing: involves enhancement techniques for clarity, as well as contrast adjustment and normalization to maintain uniformity.
  2. Pixel-wise Segmentation: Annotators utilize specialized software to accurately mark every pixel associated with distinct objects.
  3. Validation: Annotations undergo validation by remote sensing experts and are tested on preliminary segmentation models.
     

Annotation Metrics

  • Total Pixel-wise Annotations: 60,000 (one for each image).
  • Additionally, the average annotation time per image is 25 minutes due to the complex and varied nature of the content.
Remote Sensing Object Segmentation Dataset
Remote Sensing Object Segmentation Dataset
Remote Sensing Object Segmentation Dataset
Remote Sensing Object Segmentation Dataset

Quality Assurance

Stages

Automated Model Evaluation: Use preliminary segmentation models to compare their results with human annotations, identifying potential mismatches.
Expert Review: Every segmented image is scrutinized by remote sensing specialists for validation.
Inter-annotator Agreement: Some images are annotated by multiple individuals to ensure standardization in the segmentation process.

QA Metrics

  • Annotations validated using segmentation models: 30,000 (50% of total images)
  • Expert-reviewed annotations: 20,000 (33% of total images)
  • Inconsistencies identified and rectified: 2,400 (4% of total images)
 

Conclusion

The Remote Sensing Object Segmentation Dataset stands as a landmark contribution to the realm of geospatial analytics and environmental monitoring. By offering meticulously segmented high-resolution images from diverse terrains, the dataset promises to be a cornerstone for breakthroughs in satellite imagery analysis, aiding endeavors ranging from urban planning to environmental conservation.

Technology

Quality Data Creation

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Guaranteed TAT

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ISO 9001:2015, ISO/IEC 27001:2013 Certified

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HIPAA Compliance

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GDPR Compliance

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Compliance and Security

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