Awesome
Drone based wheel-ruts semantic segmentation
This repo includes the scripts to replicate the methods developed in Bhatnagar et al. (2022) to perform a semantic segmentation of wheel-ruts caused by forestry machinery based on drone RGB imagery 📷.
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).
| 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.