Remote Sensing

Estes, J.; Kline, K.; Collins, E.

doi:10.1016/b978-0-08-097086-8.72046-0

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Remotely Sensed Data are derived from the remote sensing activities. Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft) [61] . Three parameters that are commonly included in the prediction that include Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST).…”

Section: Contents Summarizationmentioning

confidence: 99%

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

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Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning . The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

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Section: Contents Summarizationmentioning

confidence: 99%

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

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Revealing local adaptation of Quercus suber L. populations under climate change through Genome Scans and Environmental Association Analysis

Assemar,

Alami,

Rabeh

et al. 2024

Tree Genetics & Genomes

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Scrutinizing Urbanization in Kathmandu Using Google Earth Engine Together with Proximity-Based Scenario Modelling

Aryal¹,

Bhatta

Adhikari³

et al. 2022

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‘Urbanization’ refers to the expansion of built-up areas caused by several factors. This study focuses on the urbanization process in Kathmandu, the capital of Nepal. Supervised classification was conducted in Google Earth Engine by using Landsat data for years 2001, 2011 and 2021. The random forest classifier with 250 trees was used for classification to generate land-cover map. A land-cover map of 2021 was used as base map in the InVEST tool for scenario modelling. An accuracy assessment with 20% of sample points was conducted with different metrics, such as overall accuracy, kappa coefficient, producer accuracy, and consumer accuracy. The results show an increment of built-up areas by around 67 km2 over 20 years in a centrifugal pattern from the core district, converting agricultural and forest land. ‘Forest’ is still dominant land-use class, with an area of 177.97 km2. Agricultural land was highly converted to urban area. The overall accuracy of this classification process ranged 0.96–1.00 for different years. The scenario modelling further elaborated an amiability of drastic shift in land-use classes to ‘built-up’, especially forest and agriculture, by around 33 km2 and 66 km2, respectively. This study recommends the consideration of ecological approaches during the planning process.

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Remote Sensing

Cited by 3 publications

References 3 publications

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Revealing local adaptation of Quercus suber L. populations under climate change through Genome Scans and Environmental Association Analysis

Scrutinizing Urbanization in Kathmandu Using Google Earth Engine Together with Proximity-Based Scenario Modelling

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