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.

本数据集系基于一篇正在审稿待刊的论文《面向无人机影像地理AI（GeoAI）数据集生产的地理信息系统（GIS, Geographic Information System）工作流》中所述流程的应用成果。本数据集的设计初衷为：通过部署深度学习分割模型，对比何种缓冲区尺寸或缓冲区距离可使模型更精准地学习到可从无人机航空正射影像中提取的道路特征。本次研究围绕该道路提取任务中两类问题的权衡展开探讨：一类是生成不均衡的二值掩码，另一类是生成异常值过载的掩码。