Drone Road dataset

This dataset is the result of applying a Pipeline described in the paper "A GIS pipeline for the production of GeoAI datasets from drone imagery" in revision for publishing.

The idea behind the dataset is to run deep learning segmentation models to compare which buffer size or buffer distance allows the model to learn better road characteristics to be extracted from drone aerial orthomosaics. In the problem a tradeoff between obtaining imbalance binary masks and masks overloaded with outliers is studied.

本数据集为应用已处于出版修订阶段的论文"A GIS pipeline for the production of GeoAI datasets from drone imagery"中所述地理信息系统（Geographic Information System, GIS）工作流所得的成果。

本数据集的设计初衷为通过部署深度学习分割模型，对比何种缓冲区大小或缓冲区距离能够使模型更精准地学习道路特征，进而从无人机航空正射镶嵌影像中提取道路信息。

本研究围绕获取不平衡二值掩码与生成异常值过载的掩码之间的权衡问题展开探讨。