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Commit d43d17d7 authored by Leonard Wayne Hackel's avatar Leonard Wayne Hackel
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Adding License and updating README with Arxiv link and additional citation

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MIT License
Copyright (c) 2022 The Authors of The Paper, "Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images"
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
......@@ -3,12 +3,13 @@
This git page contains the code used in the paper "Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images", presented at the SPIE Image and Signal Processing conference in Berlin September 2022. This README only contains an overview of the repository. More detailed explanations can be found in each subfolder.
If you use the code from this repository in your research, please cite the following paper:
> T. Breitkopf, L. Hackel, M. Ravanbakhsh, A. Cooke, S. Willkommen, S. Broda and and B. Demir, "[Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images](https://arxiv.org/abs/2210.02071)", SPIE Image and Signal Processing for Remote Sensing 2022, Berlin, Germany, 2022.
```
@article{dlpipe2022,
title={Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images},
author={Tom-Lukas Breitkopf and Leonard Hackel and Mahdyar Ravanbakhsh and Anne-Karin Cooke and Sandra Willkommen and Stefan Broda and and Begüm Demir},
booktitle={SPIE Sensors + Imaging 2022},
booktitle={SPIE Image and Signal Processing for Remote Sensing 2022},
year={2022}
}
```
......@@ -33,5 +34,32 @@ The data used for this project is stored in a [Google Drive](https://drive.googl
## Weights
The weights of pre-trained models are stored within each individual folder.
## Acknowledgement
This work is funded by the European Research Council (ERC) through the ERC-2017-STG BigEarth Project under Grant 759764 and by the German Ministry for Education and Research as BIFOLD - Berlin Institute for the Foundations of Learning and Data (01IS18025A).
## License
The code in this repository is licensed under the **MIT License**:
```
MIT License
Copyright (c) 2022 The Authors of The Paper, "Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images"
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
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