[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121084-en":3,"doc-seo-121084-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121084,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data","Dust storms are closely linked to respiratory illnesses worldwide, motivating extensive research on dust aerosol characterization and monitoring. This review summarizes machine learning-driven studies that analyze dust aerosols from satellite observations, considering historical progress and common modeling challenges across datasets and sensors. Results indicate that multi-spectral strategies using linear and non-linear combinations of spectral bands support strong visualization and quantitative analysis, while machine learning advances performance and enables new solutions to specialized problems in dust aerosol detection and modeling.","arXiv :2404 .09415v1 [ cs .CV] 15 Apr 2024  \nA Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data  \nNurul Rafi 1 and Pablo Rivas2   \nSchool of Engineering and Computer Science  \nDepartment of Computer Science  \nBaylor University, Texas, USA  \n1 Nurul [Rafi1@Baylor.edu](Rafi1@Baylor.edu2 Pablo)[2](Rafi1@Baylor.edu2 Pablo)[ Pablo](Rafi1@Baylor.edu2 Pablo) [Rivas@Baylor.edu](Rivas@Baylor.edu)  \nAbstract. Dust storms are associated with certain respiratory illnesses across different areas in the world. Researchers have devoted time and resources to study the elements surrounding dust storm phenomena. This paper reviews the efforts of those who have investigated dust aerosols using sensors onboard of satellites using machine learning-based approaches. We have reviewed the most common issues revolving dust aerosol modeling using different datasets and different sensors from a historical perspective.  \nOur findings suggest that multi-spectral approaches based on linear and non-linear combinations of spectral bands are some of the most successful for visualization and quantitative analysis; however, when researchers have leveraged machine learning, performance has been improved and new opportunities to solve unique problems arise.  \n1 Introduction  \nDust is the most common form of aerosol globally, affecting the water cycle, plants, public health and welfare, and climate [41] . It is generated at a microscale and can affect a wide area depending on wind flow and geomorphology [10] . Dust aerosols are non-spherical airborne particles with depolarization and can be found in large numbers, particularly in areas like Africa’s northwestern region. However, researchers discovered that dust aerosols could be found around different continents, regardless of their source [51] .  \nAccording to some reports, dust aerosol causes extreme air pollution, anda variety of respiratory diseases [53,55] . Dust storms, which contain toxic airborne particles such as organic contaminants, trace products, and cancer-causing bacteria, are deadly weather phenomena that mostly occur in deserts and bare land areas [60,2 , 19 ,26] . Dust storms directly impact the global environment by absorbing solar radiation and reducing visual acuity, resulting in dangerous traffic accidents [9,5] . To assess the level of activity of dust storms, researchers evaluate changes in different criteria, including dust day’s frequencies [58, 17], optical depth index of aerosols [11], and index of a dust storm [42, 16], among others [35] . While some of these dust events are evidently visible, as shown in Figure 1, low concentrations of dust at different altitudes can present challenges that  \n2 Nurul Rafi and Pablo Rivas   \nFig. 1. Dust event captured with the MODIS instrument over NASA’s Terra Satellite. Source: West Africa.  \nrequire leveraging machine learning methodologies for better results. This paper performs a succinct literature review of machine learning methodologies applied in dust aerosol modeling problems.  \nThis paper is organized as follows: Section 2 introduces the concept of remote sensing for dust aerosols, including the different satellites and sensors commonly used. Section 3 describes data availability and accessibility-related issues. Section 4 presents approaches based on the physical properties of dust, paving the way to understand the feature space of machine learning methodologies, which are discussed in Section 5 . Section 6 provides additional information on methods related to dust modeling, and finally, conclusions are drawn in Section 7 .  \nA Review on ML Algorithms for Dust Aerosol Detection using Satellite Data 3  \n2 Remote Sensing for Dust Aerosols  \nThe use of remote sensing (RS) to detect dust sources at both local and regional scales is a valuable tool [7] . Due to the high variability in spatial data, RS has become the standard method for determining the presence and movement of dust aerosols. In addition to AE","cbCaigoCKZCMnZM5","https://ap.wps.com/l/cbCaigoCKZCMnZM5","pdf",2337992,1,17,"English","en",105,"# Introduction\n## Remote Sensing for Dust Aerosols\n### Aqua and Terra: MODIS\n## Data and Method Overviews","[{\"question\":\"What is the main focus of this review paper?\",\"answer\":\"The paper reviews machine learning algorithms used for dust aerosol detection and modeling based on satellite data, highlighting issues across datasets and sensors.\"},{\"question\":\"How do multi-spectral approaches contribute to dust aerosol analysis?\",\"answer\":\"Multi-spectral methods that combine spectral bands linearly or non-linearly are reported as effective for both visualization and quantitative analysis.\"},{\"question\":\"Why does the paper discuss different satellite platforms and sensors?\",\"answer\":\"Different satellites and sensors provide varying spatial and temporal resolution and spectral channels, which influence how dust sources and storm activity are monitored.\"}]","A Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data | 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is the main focus of this review paper?","Question",{"text":75,"@type":76},"The paper reviews machine learning algorithms used for dust aerosol detection and modeling based on satellite data, highlighting issues across datasets and sensors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do multi-spectral approaches contribute to dust aerosol analysis?",{"text":80,"@type":76},"Multi-spectral methods that combine spectral bands linearly or non-linearly are reported as effective for both visualization and quantitative analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the paper discuss different satellite platforms and sensors?",{"text":84,"@type":76},"Different satellites and sensors provide varying spatial and temporal resolution and spectral channels, which influence how dust sources and storm activity are 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