Official implementation for the ICASSP 2024 paper.

Building Lane-Level Maps from Aerial Images
Jiawei Yao*, Xiaochao Pan*, Tong Wu, Xiaofeng Zhang

* equal contribution

AErial Lane (AEL) Dataset is a first large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road. The dataset contains 7,763 images and over 150,000 lanes covering different lane standards, terrain and regions, providing a comprehensive resource for researchers in this field.

we chose 11 regions, and each region consists of a road between 3 and 27 kilometers long with various backgrounds and terrain such as desert by the coastline in Dubai, urban area of Valencia and forest region in Perak State.

Our split ratio of training set:validation set:test set is 7:2:1.

Please download our dataset, scripts and qgis_annotation_data

1. Create conda environment:

$ conda create -y -n AerialLaneNet python=3.7
$ conda activate AerialLaneNet

2. This code is compatible with python 3.7, pytorch 1.7.1 and CUDA 10.2. Please install PyTorch:

$ conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch

If you have any problem or suggestion, please feel free to open an issue or send emails to the contributors.

@inproceedings{yao2024building,
  title={Building lane-level maps from aerial images},
  author={Yao, Jiawei and Pan, Xiaochao and Wu, Tong and Zhang, Xiaofeng},
  booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={3890--3894},
  year={2024},
  organization={IEEE}
}