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

05/03/2021
by   Narayani Bhatia, et al.
15

Avian botulism caused by a bacterium, Clostridium botulinum, causes a paralytic disease in birds often leading to high fatality, and is usually diagnosed using molecular techniques. Diagnostic techniques for Avian botulism include: Mouse Bioassay, ELISA, PCR, all of which are time-consuming, laborious and require invasive sample collection from affected sites. 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 water-body sites important for avian fauna) backed by on-ground reporting evidence of outbreaks. In the current version, BAWD covers a total ground area of 904 sq.km from two open source satellite projects (Sentinel and Landsat). BAWD consists of 17 topographically diverse global sites spanning across 4 continents, with locations monitored over a time-span of 3 years (2016-2020). Using BAWD and state-of-the-art deep-learning techniques 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). AVI-BoT uses fused multi-spectral satellite images of water-bodies (10-bands) as input to generate a spatial prediction map depicting probability of potential Avian botulism outbreaks. We also train and investigate a simpler (5-band) Causative-Factor model (based on prominent physiological factors reported in literature as conducive for outbreak) to predict Avian botulism. Using AVI-BoT, we achieve a training accuracy of 0.94 and validation accuracy of 0.96 on BAWD, far superior in comparison to our Causative factors model. 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 5

page 6

page 7

page 8

page 9

page 13

page 15

10/16/2021

Mapping illegal waste dumping sites with neural-network classification of satellite imagery

Public health and habitat quality are crucial goals of urban planning. I...
11/27/2020

Detection of Malaria Vector Breeding Habitats using Topographic Models

Treatment of stagnant water bodies that act as a breeding site for malar...
08/12/2021

Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery

Climate change has caused reductions in river runoffs and aquifer rechar...
07/22/2021

Power Plant Classification from Remote Imaging with Deep Learning

Satellite remote imaging enables the detailed study of land use patterns...
12/22/2016

Probabilistic graphical model based approach for water mapping using GaoFen-2 (GF-2) high resolution imagery and Landsat 8 time series

The objective of this paper is to evaluate the potential of Gaofen-2 (GF...
08/07/2018

Overhead Detection: Beyond 8-bits and RGB

This study uses the challenging and publicly available SpaceNet dataset ...
01/03/2019

Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many

Learning hydrologic models for accurate riverine flood prediction at sca...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.