Remote Sensing Object Segmentation Dataset

Project Overview:

Objective

To create a comprehensive dataset dedicated to the segmentation of various objects in remote sensing images. This dataset aims to bolster advancements in satellite imagery analysis, land cover classification, environmental monitoring, and urban planning.

Scope

Amass a diverse collection of high-resolution satellite images capturing urban, rural, coastal, forest, and desert environments. Each image will have pixel-wise annotations to highlight specific objects such as buildings, roads, water bodies, vegetation, and vehicles.

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.

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

Image Pre-processing: Enhancement for clarity, contrast adjustment, and normalization to maintain uniformity.
Pixel-wise Segmentation: Annotators utilize specialized software to accurately mark every pixel associated with distinct objects.
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)
Average Annotation Time per Image: 25 minutes (Due to the complex and varied nature of the content)

Quality Assurance

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.