Detection of Malaria Vector Breeding Habitats using Topographic Models

• URL: http://arxiv.org/abs/2011.13714v2
• Date: Tue, 16 Jul 2024 10:05:39 GMT
• Title: Detection of Malaria Vector Breeding Habitats using Topographic Models
• Authors: Aishwarya Jadhav,
• Abstract summary: We propose a practical topographic model based on easily available, global, high-resolution DEM data to predict locations of potential vector-breeding water sites. We surveyed the Obuasi region of Ghana to assess the impact of various topographic features on different types of water bodies. Our best model significantly outperforms earlier attempts that employ topographic variables for detection of small water sites.
• Score: 0.7614628596146599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Treatment of stagnant water bodies that act as a breeding site for malarial vectors is a fundamental step in most malaria elimination campaigns. However, identification of such water bodies over large areas is expensive, labour-intensive and time-consuming and hence, challenging in countries with limited resources. Practical models that can efficiently locate water bodies can target the limited resources by greatly reducing the area that needs to be scanned by the field workers. To this end, we propose a practical topographic model based on easily available, global, high-resolution DEM data to predict locations of potential vector-breeding water sites. We surveyed the Obuasi region of Ghana to assess the impact of various topographic features on different types of water bodies and uncover the features that significantly influence the formation of aquatic habitats. We further evaluate the effectiveness of multiple models. Our best model significantly outperforms earlier attempts that employ topographic variables for detection of small water sites, even the ones that utilize additional satellite imagery data and demonstrates robustness across different settings.

Related papers

• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• Bird Distribution Modelling using Remote Sensing and Citizen Science data [31.375576105932442]
  Climate change is a major driver of biodiversity loss. There are significant knowledge gaps about the distribution of species. We propose an approach leveraging computer vision to improve species distribution modelling.
  arXiv  Detail & Related papers  (2023-05-01T20:27:11Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagery [2.342488890032597]
  We propose the GLH-water dataset that consists of 250 satellite images and manually labeled surface water annotations. Each image is of the size 12,800 $times$ 12,800 pixels at 0.3 meter spatial resolution. To build a benchmark for GLH-water, we perform extensive experiments employing representative surface water detection models, popular semantic segmentation models, and ultra-high resolution segmentation models.
  arXiv  Detail & Related papers  (2023-03-16T13:35:56Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Cross-Geography Generalization of Machine Learning Methods for Classification of Flooded Regions in Aerial Images [3.9921541182631253]
  This work proposes two approaches for identifying flooded regions in UAV aerial images. The first approach utilizes texture-based unsupervised segmentation to detect flooded areas. The second uses an artificial neural network on the texture features to classify images as flooded and non-flooded.
  arXiv  Detail & Related papers  (2022-10-04T13:11:44Z)
• Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms [77.25251419910205]
  Harmful algal blooms (HABs) cause significant fish deaths in aquaculture farms. Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope. We employ Generative Adversarial Networks (GANs) to generate synthetic images.
  arXiv  Detail & Related papers  (2022-08-03T20:15:55Z)
• Autonomous Mosquito Habitat Detection Using Satellite Imagery and Convolutional Neural Networks for Disease Risk Mapping [0.0]
  Mosquito vectors are known for disease transmission that cause over one million deaths globally each year. Modern approaches, such as drones, UAVs, and other aerial imaging technology are costly when implemented and are only most accurate on a finer spatial scale. The proposed convolutional neural network(CNN) approach can be applied for disease risk mapping and further guide preventative efforts on a more global scale.
  arXiv  Detail & Related papers  (2022-03-09T00:54:59Z)
• SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model [72.3183990520267]
  We propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution. Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate.
  arXiv  Detail & Related papers  (2021-06-24T17:29:42Z)
• Towards Adaptive Benthic Habitat Mapping [9.904746542801838]
  We show how a habitat model can be used to plan efficient Autonomous Underwater Vehicles (AUVs) surveys. A Bayesian neural network is used to predict visually-derived habitat classes when given broad-scale bathymetric data. We demonstrate how these structured uncertainty estimates can be utilised to improve the model with fewer samples.
  arXiv  Detail & Related papers  (2020-06-20T01:03:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.