Remote Sensing Object Segmentation Dataset

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Project Overview:

Objective

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

Scope

Curate an extensive array of satellite images effort diverse landscapes, including urban, rural, coastal, forest, and desert regions. By keeping a high level of detail and blend sophisticated comment techniques, this dataset accurately identifies and labels each object within the images.

Sources

Collaborations with space agencies and satellite imaging companies.
Utilization of open-source satellite imagery platforms.
Procurement of functional images taking disaster zones, agricultural plots, and urban development.

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

Annotation Process

Stages

Pixel-wise Segmentation: Annotators utilize specialized software to accurately mark every pixel associated with distinct objects.
Validation: Annotations undergo acceptance by remote sensing experts and are tested on preliminary segmentation models.

Annotation Metrics

Total Pixel-wise Annotations: 60,000 (one for each image).
The average annotation time per image is 25 minutes the complex and varied nature of the content.

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.