# Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)

arXiv: https://arxiv.org/abs/2105.00924 · HTML: https://mlsanity.com/paper/2105.00924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 68 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 1 of 1)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)

## Abstract (arXiv)

Avian botulism is a paralytic bacterial disease in birds often leading to high fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA, PCR are usually non-preventive, post-mortem in nature, and require invasive sample collection from affected sites or dead birds. In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i.e. fused satellite images of warm-water lakes/marshy-lands or similar water-body sites that are important for avian fauna) backed by on-ground reporting evidence of outbreaks. BAWD consists of 16 topographically diverse global sites monitored over a time-span of 4 years (2016-2021). We propose a first-ever Artificial Intelligence based (AI) model to predict potential outbreak of Avian botulism called AVI-BoT (Aerosol Visible, Infra-red (NIR/SWIR) and Bands of Thermal). We also train and investigate a simpler (5-band) Causative-Factor model (based on prominent physiological factors reported in literature) to predict Avian botulism. AVI-BoT demonstrates a training accuracy of 0.96 and validation accuracy of 0.989 on BAWD, far superior in comparison to our model based on causative factors. We also perform an ablation study and perform a detailed feature-space analysis. We further analyze three test case study locations - Lower Klamath National Wildlife Refuge and Langvlei and Rondevlei lakes where an outbreak had occurred, and Pong Dam where an outbreak had not occurred and confirm predictions with on-ground reportings. The proposed technique presents a scale-able, low-cost, non-invasive methodology for continuous monitoring of bird-habitats against botulism outbreaks with the potential of saving valuable fauna lives.