M. Rhif, Ali Ben Abbes, F. Chouikhi, N. Jarray, I. Farah
{"title":"为环境应用建立突尼斯地球观测数据立方体","authors":"M. Rhif, Ali Ben Abbes, F. Chouikhi, N. Jarray, I. Farah","doi":"10.1109/ICOTEN52080.2021.9493471","DOIUrl":null,"url":null,"abstract":"The analysis of environmental applications become a crucial global concern due to the continuous change in natural resources (climatic change, anthropogenic change, etc). Recently, a wide range of free and open accessible remote sensing earth observation (EO) data are investigated. However, these data still underutilized due to their complexity, volume, veracity, velocity, variety which make users spend an amount of effort into data preparation. To realize the full information potential of EO data, creative tools must be built to reduce the time and scientific expertise necessary to access and process these data. To deal with these challenges, Analysis Ready Data (ARD) are exploited to store big EO data on a formal and structured basis with modest hardware and low clouds expenses. Nevertheless, ARD necessitates a degree of knowledge that the majority of users is limited. Thus, the EO Data Cube (DC) is a modern concept that aims to make it a reality. In this paper, we propose a Tunisian EO Data Cube (TDC). The proposed TDC architecture is composed of four parts. The first and second part consists of data collection and ARD and multidimensional data cubes building from remote sensing EO data for Tunisia. Then, different web services are developed to create, integrate, discover, access, and process the data sets. Finally, various applications were presented such as vegetation change analysis based on machine learning methods.","PeriodicalId":308802,"journal":{"name":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Towards a Tunisian earth observation data cube for environmental applications\",\"authors\":\"M. Rhif, Ali Ben Abbes, F. Chouikhi, N. Jarray, I. Farah\",\"doi\":\"10.1109/ICOTEN52080.2021.9493471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The analysis of environmental applications become a crucial global concern due to the continuous change in natural resources (climatic change, anthropogenic change, etc). Recently, a wide range of free and open accessible remote sensing earth observation (EO) data are investigated. However, these data still underutilized due to their complexity, volume, veracity, velocity, variety which make users spend an amount of effort into data preparation. To realize the full information potential of EO data, creative tools must be built to reduce the time and scientific expertise necessary to access and process these data. To deal with these challenges, Analysis Ready Data (ARD) are exploited to store big EO data on a formal and structured basis with modest hardware and low clouds expenses. Nevertheless, ARD necessitates a degree of knowledge that the majority of users is limited. Thus, the EO Data Cube (DC) is a modern concept that aims to make it a reality. In this paper, we propose a Tunisian EO Data Cube (TDC). The proposed TDC architecture is composed of four parts. The first and second part consists of data collection and ARD and multidimensional data cubes building from remote sensing EO data for Tunisia. Then, different web services are developed to create, integrate, discover, access, and process the data sets. Finally, various applications were presented such as vegetation change analysis based on machine learning methods.\",\"PeriodicalId\":308802,\"journal\":{\"name\":\"2021 International Congress of Advanced Technology and Engineering (ICOTEN)\",\"volume\":\"191 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Congress of Advanced Technology and Engineering (ICOTEN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOTEN52080.2021.9493471\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Congress of Advanced Technology and Engineering (ICOTEN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOTEN52080.2021.9493471","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards a Tunisian earth observation data cube for environmental applications
The analysis of environmental applications become a crucial global concern due to the continuous change in natural resources (climatic change, anthropogenic change, etc). Recently, a wide range of free and open accessible remote sensing earth observation (EO) data are investigated. However, these data still underutilized due to their complexity, volume, veracity, velocity, variety which make users spend an amount of effort into data preparation. To realize the full information potential of EO data, creative tools must be built to reduce the time and scientific expertise necessary to access and process these data. To deal with these challenges, Analysis Ready Data (ARD) are exploited to store big EO data on a formal and structured basis with modest hardware and low clouds expenses. Nevertheless, ARD necessitates a degree of knowledge that the majority of users is limited. Thus, the EO Data Cube (DC) is a modern concept that aims to make it a reality. In this paper, we propose a Tunisian EO Data Cube (TDC). The proposed TDC architecture is composed of four parts. The first and second part consists of data collection and ARD and multidimensional data cubes building from remote sensing EO data for Tunisia. Then, different web services are developed to create, integrate, discover, access, and process the data sets. Finally, various applications were presented such as vegetation change analysis based on machine learning methods.