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This repository contains the dataset link and the code for our paper University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization. We collect 1652 buildings of 72 universities around the world. Thank you for your kindly attention.

Task 1: Drone-view target localization. (Drone -> Satellite)} Given one drone-view image or video, the task aims to find the most similar satellite-view image to localize the target building in the satellite view.

Task 2: Drone navigation. (Satellite -> Drone)} Given one satellite-view image, the drone intends to find the most relevant place (drone-view images) that it has passed by. According to its flight history, the drone could be navigated back to the target place.

• About Dataset
• Code Features
• Prerequisites
• Getting Started
  • Installation
  • Dataset Preparation
  • Train Evaluation
• Citation

The dataset split is as follows:

We note that there are no ovelaps between 33 univeristies of training set and 39 univeristies of test set.

Now we have supported:

• Float16 to save GPU memory based on apex
• Multiple Query Evaluation
• Re-Ranking
• Random Erasing
• ResNet/VGG-16
• Visualize Training Curves
• Visualize Ranking Result
• Linear Warm-up

• Python 3.6
• GPU Memory >= 8G
• Numpy > 1.12.1
• Pytorch 0.3+
• [Optional] apex (for float16)

• Install Pytorch from http://pytorch.org/
• Install Torchvision from the source

git clone https://github.com/pytorch/vision
cd vision
python setup.py install

• [Optinal] You may skip it. Install apex from the source

git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

Download [University-1652] upon request. You may use the request template.

Or download CVUSA.

python train.py --name three_view_long_share_d0.75_256_s1_google  --extra --views 3  --droprate 0.75  --share  --stride 1 --h 256  --w 256 --fp16; 
python test.py --name three_view_long_share_d0.75_256_s1_google

python prepare_cvusa.py
python train_cvusa.py --name usa_vgg_noshare_warm5_lr2 --warm 5 --lr 0.02 --use_vgg16 --h 256 --w 256  --fp16 --batchsize 16;
python test_cvusa.py  --name usa_vgg_noshare_warm5_lr2 

The following paper uses and reports the result of the baseline model. You may cite it in your paper.

@article{zheng2020joint,
  title={University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization},
  author={Zhedong Zheng, Yunchao Wei, Yi Yang},
  journal={arXiv 2002.12186},
  year={2020}
}

• Instance Loss Code
• Lending Orientation to Neural Networks for Cross-view Geo-localization Code
• Predicting Ground-Level Scene Layout from Aerial Imagery Code