Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles-Reference-Cited by-同舟云学术

Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles

Published:2021-11 Issue: Volume:90 Page:101692
ISSN:0198-9715
Container-title:Computers, Environment and Urban Systems
language:en
Short-container-title:Computers, Environment and Urban Systems

Author:

Bravo Daniel Trevisan,Lima Gustavo Araujo,Alves Wonder Alexandre Luz,Colombo Vitor Pessoa,Djogbénou Luc,Pamboukian Sergio Vicente Denser,Quaresma Cristiano Capellani,Araujo Sidnei Alves de

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Urban Studies,General Environmental Science,Ecological Modeling,Geography, Planning and Development

Reference56 articles.

1. Detection of potential mosquito breeding sites based on community sourced geotagged images;Agarwal,2014

2. Understanding of a convolutional neural network;Albawi,2017

3. Deep learning approach for car detection in UAV imagery;Ammour;Journal Remote Sensing,2017

4. São Paulo urban heat islands have a higher incidence of dengue than other urban areas;Araujo;Brazilian Journal of Infectious Diseases,2015

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