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Agriculture-Vision

A large aerial image dataset for agricultural pattern analysis

Agriculture-Vision

We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. We collected 94, 986 high-quality aerial images from 3, 432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition.

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UIUC
https://illinois.edu
Task
3D Semantic Segmentation
Annotation Types
Semantic Segmentation
3432
Items
Classes
94986
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Unknown
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