The repo contains all source-code for our proposed approach in the paper entitled "Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System"
Location: Andong, Republic of Korea, in April 2020
Date: from April 24, 2020 to April 26, 2020
Captured location: Mount 112, Ingeum-ri, Pungcheon-myeon, Andong-si, Gyeongsangbuk-do, 15-3 Haari, Namhu-myeon, Andong-si, Gyeongsangbuk-do
Devices: Phantom 4 Pro V2.0
Date: May 6, 2020
Install minianaconda
conda install -r requirements.txt
The sample dataset shows how the implementation is carried out.
sample_data/sample_location_1_data
|
+-- Img
| |
| +-- img (1).png
| +-- img (2).png
| +-- ...
+-- Label
| |
| +-- label (1).png
| +-- label (2).png
| +-- ...
+-- Orig
| |
| +-- orig.JPG
sample_data/sample_location_2_data
|
+-- Img
| |
| +-- img (1).png
| +-- img (2).png
| +-- ...
+-- Label
| |
| +-- label (1).png
| +-- label (2).png
| +-- ...
+-- Orig
| |
| +-- orig.JPG
Use train_models to train the dual models.
With pretrained weights from this link , use predict_dual_models to predict sample testing dataset.
After receiving the predicted results, post processing functions can be used for postprocessing.
The EXIF information can be extracted by using extract EXIF function
If you use this code for your research, please cite our papers
@article{tran_damage-map_2020, title = {Damage-{Map} {Estimation} {Using} {UAV} {Images} and {Deep} {Learning} {Algorithms} for {Disaster} {Management} {System}}, volume = {12}, issn = {2072-4292}, url = {https://www.mdpi.com/2072-4292/12/24/4169}, doi = {10.3390/rs12244169}, number = {24}, journal = {Remote Sensing}, author = {Tran, Dai Quoc and Park, Minsoo and Jung, Daekyo and Park, Seunghee}, year = {2020} }