SkySeaLand: High-Resolution Satellite Imagery for Object Detection

SkySeaLand is a high-resolution satellite imagery collection formatted for object detection and classification tasks. The dataset is provided in the COCO JSON format, making it compatible with frameworks like Detectron2, MMDetection, and YOLO. It was authored by zahid510643 and last updated on Hugging Face in February 2026.

Use Cases

• Train object detection models based on the high-resolution satellite imagery mentioned in the description.
• Perform aerial surveillance analysis based on the dataset's stated purpose.
• Benchmark classification algorithms using the COCO-formatted annotations.
• Develop models for maritime and terrestrial object recognition based on the dataset's bridging of these domains.

Strengths

• Data is provided in the COCO format, ensuring native compatibility with major object detection frameworks.
• The description explicitly states the dataset is designed for high-resolution satellite imagery analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Freshness

Last updated 2026-02-14 12:13:21; freshness should be verified.