This repository contains code and dataset for the paper titled Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments. In this paper, we propose a method for accurately self-positioning unmanned aerial vehicles (UAVs) in challenging low-altitude urban environments using vision-based techniques. We provide the DenseUAV dataset and a Baseline model implementation to facilitate research in this task. Thank you for your kind attention.

• 2025/9/22: Released a new memory-efficient paradigm, SWA-PF, the video demo is Here.
• 2024/8/28: Code and model released for a novel UAV self-localization paradigm named DRL.
• 2023/12/18: Our paper is accepted by IEEE Trans on Image Process.
• 2023/8/14: Our dataset and code are released.

• News
• Table of contents
• About Dataset
• Prerequisites
• Installation
• Dataset & Preparation
• Train & Evaluation
  • Training and Testing
  • Evaluation
• Supported Methods
• License
• Citation
• Related Work

The dataset split is as follows:

More detailed file structure:

├── DenseUAV/
│   ├── Dense_GPS_ALL.txt           /* format as: path latitude longitude height
│   ├── Dense_GPS_test.txt
│   ├── Dense_GPS_train.txt
│   ├── train/
│       ├── drone/                   /* drone-view training images
│           ├── 000001
│               ├── H100.JPG
│               ├── H90.JPG
│               ├── H80.JPG
|           ...
│       ├── satellite/               /* satellite-view training images
│           ├── 000001
│               ├── H100_old.tif
│               ├── H90_old.tif
│               ├── H80_old.tif
│               ├── H100.tif
│               ├── H90.tif
│               ├── H80.tif
|           ...
│   ├── test/
│       ├── query_drone/             /* UAV-view testing images
│       ├── query_satellite/         /* satellite-view testing images

• Python 3.7+
• GPU Memory >= 8G
• Numpy 1.21.2
• Pytorch 1.10.0+cu113
• Torchvision 0.11.1+cu113

It is best to use cuda version 11.3 and pytorch version 1.10.0. You can download the corresponding version from this website and install it through pip install. Then you can execute the following command to install all dependencies.

pip install -r requirments.txt

Create the directory for saving the training log and ckpts.

mkdir checkpoints

Download DenseUAV HF_DATA.

You could execute the following command to implement the entire process of training and testing.

bash train_test_local.sh

The setting of parameters in train_test_local.sh can refer to Get Started.

The following commands are required to evaluate Recall and SDM separately.

cd checkpoints/<name>
python test.py --name <name> --test_dir <dir/to/testdir/of/dataset> --gpu_ids 0 --num_worker 4

the <name> is the dir name in your training setting, you can find in the checkpoints/.

For Recall

python evaluate_gpu.py

For SDM

python evaluateDistance.py --root_dir <dir/to/root/of/dataset>

We also provide the baseline checkpoints, quark one-drive.

unzip <file.zip> -d checkpoints
cd checkpoints/baseline
python test.py --test_dir <dataset_root>/test
python evaluate_gpu.py
python evaluateDistance.py --root_dir <dataset_root>

This project is licensed under the Apache 2.0 license.

@ARTICLE{DenseUAV,
  author={Dai, Ming and Zheng, Enhui and Feng, Zhenhua and Qi, Lei and Zhuang, Jiedong and Yang, Wankou},
  journal={IEEE Transactions on Image Processing},
  title={Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments},
  year={2024},
  volume={33},
  number={},
  pages={493-508},
  doi={10.1109/TIP.2023.3346279}}

• University-1652 https://github.com/layumi/University1652-Baseline
• FSRA https://github.com/Dmmm1997/FSRA