https://github.com/ahestevenz/flood-detection-segmentation
Flood detector using effientnet as semantic segmentation model
https://github.com/ahestevenz/flood-detection-segmentation
computer-vision effientnet flood-detection floods machine-learning segmentation-models
Last synced: 3 months ago
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Flood detector using effientnet as semantic segmentation model
- Host: GitHub
- URL: https://github.com/ahestevenz/flood-detection-segmentation
- Owner: ahestevenz
- Created: 2023-07-02T01:14:21.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2026-03-22T10:05:43.000Z (3 months ago)
- Last Synced: 2026-03-23T00:59:07.525Z (3 months ago)
- Topics: computer-vision, effientnet, flood-detection, floods, machine-learning, segmentation-models
- Language: Python
- Homepage:
- Size: 120 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Flood Detector
A flood detector using effientnet as semantic segmentation model.
## Dataset
[Sen1Floods11](https://github.com/cloudtostreet/Sen1Floods11): a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1 (Example). This data was generated by Cloud to Street, a Public Benefit Corporation: https://www.cloudtostreet.info/. For questions about this dataset or code please email support@cloudtostreet.info.
Paper:
Bonafilia, D., Tellman, B., Anderson, T., Issenberg, E. 2020. [Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1](https://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Bonafilia_Sen1Floods11_A_Georeferenced_Dataset_to_Train_and_Test_Deep_Learning_CVPRW_2020_paper.html). The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 210-211.
## Training and Testing
### Requirements
#### Python environment
It is highly recomomended to run the training scrips inside of python environment, in case the system does not have any use the following procedure (check [Virtualenvwrapper Installation](https://virtualenvwrapper.readthedocs.io/en/latest/install.html) for more details).
```setup
$ pip install --user virtualenvwrapper virtualenv
```
Add three lines to your shell startup file (.bashrc, .profile, etc.) to set the location where the virtual environments should live:
```
export WORKON_HOME=$HOME/.virtualenvs
export PROJECT_HOME=$HOME/Devel
source /usr/local/bin/virtualenvwrapper.sh
```
After editing it, reload the startup file (e.g., run `source ~/.bashrc`) and create a python environment:
```
$ mkvirtualenv venv_py
$ workon venv_py
```
#### Installing the package
Once the python environment was configured, run the following procedure to install all the required packages
```setup
(venv_py)$ git clone https://github.com/ahestevenz/flood-detection-segmentation
(venv_py)$ cd flood-detection-segmentation
(venv_py)$ pip install .
```
### Training
The following script will be used to train the model.
```train
(venv_py)$ bn-run-train --help
usage: bn-run-train [-h] [-j JSON_FILE] [-v] [-p output.prof]
Welcome to the Bushfires Detector training script
optional arguments:
-h, --help show this help message and exit
-j JSON_FILE, --json_file JSON_FILE
JSON configuration (default: "/Users/ahestevenz/Desktop/tech-projects/1_code/flood-detection-segmentation/config.json")
-v, --verbose Increase logging output (default: INFO)(can be specified several times)
-p output.prof, --profile output.prof
Run with profiling and store output in given file
```
Run the above command to start the training (check the `data` and `artefacts` paths in `config.json` first)
```train
(venv_py)$ bn-run-train --json_file config.json
```
### Testing
To evaluate the model indicated in `config.json` file, run this command:
```test
(venv_py)$ bn-run-test --json_file config.json
```
## Results
Qualitative results using the validation dataset
In the plot below, you can observe quantitative results using a similarity metric between ground truth and prediction masks.
## TODO List
- [x] Add Docker container option to run/train models
- [x] Add release revision for [NASA_IIAC_IEEE_GRSS_ESITC_Flood_Data](https://nasa-impact.github.io/etci2021/) dataset from the NASA Interagency Implementation and Advanced Concepts Team
- [x] Notebook example