This is the PyTorch version for ASPDNet: "Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method" in TGRS 2020, which delivered a state-of-the-art, straightforward and end-to-end architecture for object counting tasks.
Prerequisites We strongly recommend Anaconda as the environment.
Python: 3.6
PyTorch: 1.0.1
CUDA: 10.0
Ground Truth Please follow the make_dataset.py to generate the ground truth. It shall take some time to generate the dynamic ground truth. Note you need to generate your own json file.
Training Process Try python train.py train.json val.json 0 0 to start training process.
Validation Follow the val.py to try the validation.
Paper link: http://arxiv.org/abs/2008.12470
Dataset link：https://pan.baidu.com/s/19hL7O1sP_u2r9LNRsFSjdA code：nwcx
or at the website https://drive.google.com/drive/my-drive but only including building subsets. Other three can be download at https://captain-whu.github.io/DOTA/ according to our provided filenames
References
If you find the ASPDNet useful, please cite our paper. Thank you!
@article{gao2020counting,
title={Counting from sky: A large-scale data set for remote sensing object counting and a benchmark method},
author={Gao, Guangshuai and Liu, Qingjie and Wang, Yunhong},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={59},
number={5},
pages={3642--3655},
year={2020},
publisher={IEEE}
}
@inproceedings{gao2020counting,
title={Counting dense objects in remote sensing images},
author={Gao, Guangshuai and Liu, Qingjie and Wang, Yunhong},
booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={4137--4141},
year={2020},
organization={IEEE}
}