wheelRuts_semanticSegmentation

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wheelRuts_semanticSegmentation

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# Drone based wheel-ruts semantic segmentation

This repo includes the scripts to replicate the methods developed in [Bhatnagar et al. (2022)](https://academic.oup.com/forestry/advance-article/doi/10.1093/forestry/cpac023/6627280?login=true) to perform a semantic segmentation of wheel-ruts caused by forestry machinery based on drone RGB imagery 📷.

![Picture1](https://user-images.githubusercontent.com/5663984/169524083-197f2a17-fbc9-4b87-b0fb-324217caade5.png)
Figure 1. Example of the input and output of the developed method.

# Installation

```
# clone repo
git clone https://github.com/SmartForest-no/wheelRuts_semanticSegmentation
cd wheelRuts_semanticSegmentation

# create new environment
conda env create -f environment_cnn_wheelRuts.yaml

# activate the created environment
conda activate wheelRuts

# install requirements
pip install -r requirements.txt
```
In addition:
- Download model weights and json file at: https://drive.google.com/drive/folders/1byb7jcAPiB9pr2gunJCbec7fRiwzI6BG?usp=sharing
- Unzip the two files and and place them in the model folder

# Usage 💻
## Input 🗺️ 
The algorithm takes as input one orthomosaic or a folder with several orthomosaics in GeoTiff format (.tif). The default version takes as input a single orthomosaic, to change see "How to run" section.

## Output 🚜
The output consist of a binary raster with the same extent as the input orthomosaic where pixels with value of 1 correspond to wheel ruts and of value 0 correspond to background.

## How to run 🏃
To run the segmentation on a new drone orthomosaic run:
```
python run.py
```
This will open a window where you can select one orthomosaic to predict on. The default version (file_mode) allows you to select a single file but if you want to switch to the mode where is possible to feed an entire directory where several othomsaics are stored, then you should edit the ```run.py``` file by replacing ```file_mode``` with ```directory_mode```.

### additional run options
#### Use different tile size
Select the tile size and buffer size to split the original orthomosaic into smaller tiles by using the arguments ```--tile_size_m``` (default is 20 m) and ```--buffer_size_m``` (default is 2 m), e.g.:
```
python run.py --tile_size_m 20 --buffer_size_m 2

```
#### Select a different model
Select the model to run by using the argument ```--model_name``` (see next section for available models), e.g.:
```
python run.py --model_name singleTrack_allData_25epochs

```

# Available models
As 🍒 on top of the 🎂, in addition to the default model, we will also provide additional models in the future as we develop the method further (see Table below). The default model (singleTrack_allData_49epochs) has been trained for 50 epoch on the entire dataset described by [Bhatnagar et al. (2022)](https://zenodo.org/record/5746878#.YoeAzKhBxaQ).

| model_name  | description |
| ------------- | ------------- |
| singleTrack_allData_25epochs  | output segmentation is a single track (model trained for 25 epochs) |
| singleTrack_allData_49epochs  | output segmentation is a single track (model trained for 25 epochs) |
| doubleTrack_32epochs  | output segmentation is a double track (work in progress) |

To select the different models edit in ```run.py``` the ```model_name``` variable to fit your preferred models. The doubleTrack model can be interesting for some as it produces a segmentation for each single track of the forestry machines (see image below).

# Training with your data :woman_scientist:
In case you want to re-train the model using your own data :sunglasses:
```
python train_owndata.py
```
An example dataset has been attached (data.zip) for understanding how the training and validation images should be formatted. TO use this folder unzip it.

For changing the training parameters, see wheelRuts_semanticSegmentation/keras_segmentation/train.py line 55 to make changes in batch size, optimizer, augmentation, etc.

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