BATS: Bat-aggregated time series

Doppler Radar Data Pipeline

BATS is a Python-based algorithm that identifies Mexican free-tailed bats in weather radar data. BATS downloads, processes, and classifies large amounts of NOAA NEXRAD Weather Radar data using a pre-trained neural network.

Background

Mexican free-tailed bats (commonly referred to as Brazillian free-tailed bats) are a common bat species found across much of North and South America. Due to their voracious appetite and large roosting numbers, free-tailed bats are believed to provide invaluable ecosystem services in the form of pest control.

This project highlights a computer vision algorithm based on an artificial neural network that quantifies the occurence of free-tailed bats over a given area within a given time frame.

Features
Prerequisites
Installation and Setup
Usage
Dataset
Model Information
Contributing
License
Acknowledgments

Features

Data Download: Automates the download of radar scans for specified dates.
Classification: Uses a trained model to classify radar scans.
Aggregation: Processes and aggregates radar data for analysis.

Prerequisites

Python 3.x
Conda (recommended for environment management)

Installation and Setup

Clone the Repository:

git clone https://github.com/yourusername/doppler-radar-analysis.git
cd doppler-radar-analysis

Set up the conda environment

conda env create -f environment.yml
conda activate doppler_env

Additional Setup -Download the pretrained neural network (and other models if interested). These models are hosted in the same repository as the sample data (Zenodo)

Usage

Downloading/Accessing
```
python main.py [start_date] [end_date] [tower] --hours [hours] --start_time [start_time]
```
Replace [start_date], [end_date], [tower], [hours], and [start_time] with your desired values.
Other Functionalities: ...

Dataset

The reference data used for model training and testing are hosted on in the data/reference directory hosted on Zenodo.
The radar data is sourced from the NOAA NEXRAD data repository and accessed through the Python packages Py-ART and NEXRADAWS. For the purposes of demonstration, a sample dataset is included in the data/ directory hosted on Zenodo.

Model Information

Our classification task leverages a traditional Artificial Neural Network (ANN) constructed using the Keras Python Deep Learning package from Google’s Tensorflow library. The classifier processes input as a single pixel from the mentioned cartesian grid, essentially a six-dimensional vector of radar data.

Following the methods of Chilson et al. (2019) and Zewdie et al. (2019) – who utilized neural networks for tracking purple martins and pollen via NEXRAD respectively – we've structured our classifier as a feed-forward, fully-connected network. It's comprised of:

A 6-unit input layer
Three intermediate 152-unit layers with ReLU activation functions
A concluding 2-unit layer with a SoftMax activation, outputting a scaled probability (between 0 and 1) signifying the likelihood of a pixel containing a bat swarm.

This architecture was chosen based on several metrics including precision, recall scores, AUC, and a qualitative assessment rooted in established bat dispersal strategies. The assessment was performed on a validation set extracted from the primary training set. For a deeper dive into the training methodology, refer to section 2.4.

The model underwent training for 20 epochs, utilizing mini-batches of size 32 and the Adam optimizer. The learning rate was pegged at 0.001, with Adam parameters set to the recommended defaults of β1 = 0.9 and β2 = 0.999. The training was done semi-supervised, employing the standard cross-entropy loss function.

Network Architecture:

[ f(x) = \mathrm{softmax}(W_3(\mathrm{relu}(W_2(\mathrm{relu}(W_1(\mathrm{relu}(W_0 x + b_0)) + b_1)) + b_2) + b_3) ]

Equation 1: This represents the network's architectural flow, where x is the radar data input vector. The symbols W0...W3 and b0...b3 denote the network's trainable parameters.

Contributing

Contributions are welcome! Please reach out here on github or via email at brianlee52@ucsb.edu

License

This project is licensed under the MIT License.

Acknowledgements

Thanks to Py-ART, an essential library used in this project.
Appreciation to the team or individuals who contributed to this project.

bhyleee
/

BATS