This repo contains the source code for reproducing the results presented in the paper: Using machine learning to count Antarctic shag (Leucocarbo bransfieldensis) nests on Remotely Piloted Aircraft Systems images .

It is recommended to create a new conda environment for this package, especially if you wish to work with QGIS (required for tiling images):

conda create -n nests python=3.10
conda activate nests

Install the project:

git clone https://github.com/Appsilon/antarctic-nests.git
cd antarctic-nests
pip install -r requirements.txt

From within the antarctic-nests directory, install YOLOv6:

git clone -b v3 https://github.com/meituan/YOLOv6
cd YOLOv6
pip install -r requirements.txt

Note that currently the repo works with the v3 release of YOLOv6.

Both training and inference are carried out within the YOLOv6 runs directory, which are then copied over to the main repositories results directory.

An additional weights file must be downloaded in order to carry out training, which YOLO will attempt to download automatically. However, if this does not work, create a YOLOv6/weights directory, download the weights file using this link, and place it into the folder.

During training the necessary folders will be created in the YOLOv6 directory, however for ad-hoc analysis an model folder will have to be placed in the YOLOv6/runs/train/ directory with the following structure:

model_name
├── weights
|   └── best_ckpt.pt

During inference a YOLOv6/runs/val/model_name folder will be created with prediction json and weights file.

To prepare datasets from original TIFF and ground truth sets, QGIS also needs to be installed:

conda install qgis --channel conda-forge

To use QGIS the conda_env_name in config.py must be the name of the environment in which QGIS is located. If you installed QGIS in the nests environment you do not need to update this variable.

The data used in this project can be found here. Download the .zip archive and unzip it into the data folder. You can use the following command for the download (assuming you are in the antarctic-nests/data directory):

gsutil cp gs://antarctic-nests-data/all_islands.zip

The predictions from the released model on all_islands, can be found here.

The end2end.ipynb notebook contains all of the steps required to preprocess datasets (using QGIS), train and evaluate models, and post-process results.

For more information on the individiual steps and options, please see the following README's:

• Preprocessing
• Training & Inference
• Postprocessing

New datasets should be added to the data directory in the following format:

dataset_name
├── raw
|   ├── shapefiles
|   |   ├── shapefile1.shp
|   |   ├── shapefile2.shp
|   |   └── ...
|   ├── tifs
|   |   ├── image1.tif
|   |   ├── image2.tif
|   |   └── ...

In the case of multiple locations contained within a single shapefile, the dataset should be split into multiple datasets, each containing the TIFF files that correspond to that location and the shapefile.

The shapefile column and identifier that separates the locations must be provided in the split_col and split_id parameters, see the end2end notebook for an example.

In the release you will find the weights for the model results from which we published in the paper. To make use of them, download the attached .zip file and unzip into YOLOv6/runs/train/....

To reproduce plots from the published paper, follow the plots_for_paper notebook.