Cloud system ArcGIS online as a managerial decision-making tool in agricultural production

Mariushko, Maksim; Pashchenko, Ruslan; Nechausov, Artem

doi:10.1109/dessert.2018.8409190

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…Early Solutions that came to put their mark on the sector include NASA WorldWind [33] and GOOGLE Maps/Google Earth [34]. Now GI Cloud services are offered by large cloud providers like Amazon-Amazon Web Services (AWS) or Microsoft-Microsoft Azure Functions for the management of geospatial big data [35] and also large GI providers like ESRI ArcGIS Online [36]. In Europe Data and Information Access Services (DIAS platforms) [37] are presently being built to support easier access to data from the European earth observation data initiative Copernicus and build new services on top of them [38].…”

Section: Technical Baselinementioning

confidence: 99%

Map Whiteboard Cloud Solution for Collaborative Editing of Geographic Information

Zadražil

Bergheim

Bērziņš³

et al. 2021

Cloud Computing and Data Science

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For the purpose of exploiting the potential of cloud connectivity in geographical information systems, the Map Whiteboard technology introduced in this article does for web mapping what Google Docs does for word processing; create a shared user interface where multiple parties collaboratively can develop maps and map data while seeing each other work in realtime. To develop the Map Whiteboard concept, we have applied a methodology whereby we have collected technical and functional requirements through a series of hackathons, implemented a prototype in several stages, and subjected this to rigorous testing in a lab environment and with selected users from relevant environments at intermediate scale. The work has resulted in a fully functional prototype that exploits WebSockets via a cloud service to reflect map and data changes between multiple connected clients. The technology has a demonstrated potential for use in a wide range of web GIS applications, something that is facilitated by the interfaces already implemented towards mainstream mapping frameworks like OpenLayers and QGIS-two of the most popular frameworks for Web GIS solutions. Further development and testing are required before operationalization in mission-critical environments. In conclusion, the Map Whiteboard concept offers a starting point for exploiting cloud connectivity within GIS to facilitate the digitalization of common processes within the government and private sector. The technology is ready for early adopters and welcomes the contribution of interested parties.

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Section: Technical Baselinementioning

confidence: 99%

Map Whiteboard Cloud Solution for Collaborative Editing of Geographic Information

Zadražil

Bergheim

Bērziņš³

et al. 2021

Cloud Computing and Data Science

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“…ArcGIS Online is a cloud-based GIS mapping software that uses interactive maps to connect spatial data to thousands of users at the same time (Kahvand et al, 2015). ArcGIS Online can be used for storing, sharing and managing spatial data (Mariushko et al, 2018). ArcGIS Online supports several data and service types such as WMS, KML, GPX, CSV and SHP.…”

Section: Developing Sdssmentioning

confidence: 99%

“…ArcGIS Online supports several data and service types such as WMS, KML, GPX, CSV and SHP. In general, the ArcGIS Online cloud allows the decision-makers to be able to increase the efficiency and reliability of the site selection and perform their monitoring role at all stages (Mariushko et al, 2018). So, ArcGIS Online was considered as a tool for making the decision support system more effective and executable by embedding it inside a web database that was developed for the study region.…”

Section: Developing Sdssmentioning

confidence: 99%

A spatial decision support system for municipal solid waste landfill sites (case study: The Mazandaran Province, Iran)

2021

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Landfilling is an inevitable step for the municipal solid waste (MSW) management system in developing countries. This article presents a Spatial Decision Support System (SDSS) that was developed for the monitoring of municipal landfills and siting the new places for waste disposal at Mazandaran province, south of Caspian Sea, Iran. The effective criteria and evaluation constraints were chosen according to the Iran waste management law. The ArcGIS 10.4.1 software was used for creating a geospatial database and the analytic hierarchy process (AHP) was used for ranking the criteria. By integrating the criteria, a suitability map was generated into four categories: high suitability, moderate suitability, low suitability and illegal areas. Using ArcGIS online, the maps were shared on a website that was specifically designed for this purpose. The decision-makers can check online the laws, the effective criteria and the results of spatial analysis for optimal siting. Also, the results of environmental evaluations for active landfills have been provided. Given the characteristics of active landfills and the optimal sites for the possible development, the SDSS can provide a mutually beneficial relationship between the experts, decision-makers and stakeholders to decide about the priority of actions required for the relocation of landfills, site closure or emergency care.

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“…З цією метою доцільно забезпечити постійний моніторинг стану вегетації на базі даних отриманих у результаті обробки космічних знімків. При цьому найбільш часто для визначення стану біомаси використовується нормалізований різницевий вегетаційний індекс -NDVI [15].…”

Section: використання космічних знімків Sentnel-2 для моніторингу сілunclassified

“…Кожен з перерахованих вище етапів включає роботи притаманні лише цьому проміжку часу. Більш детально моніторингові роботи та відповідні управлінські рішення, які виконуються на їх основі для кожного з етапів приведено в [15].…”

Section: використання космічних знімків Sentnel-2 для моніторингу сілunclassified

Моніторинг Сільськогосподарських Культур Із Застосуванням Космічних Знімків Sentinel-2

Марюшко¹,

Пащенко²,

Коблюк³

2019

reks

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The subject of the study in the article is the growing need for the use of spatial information for efficient agricultural production, due to the growing tendency of Earth remote sensing data accessibility, which, due to the spatial and temporal resolution improvement, can be used in the land cover analysis and other related jobs. The goal is to review the obtaining process of satellite multispectral space imagery from Sentinel-2 and to consider the possibility of their use for monitoring crops during the entire vegetation phase. The tasks: to study the modern needs of agricultural producers in the field of analysis of land cover occupied by agricultural crops; the analysis of the European Space Agency programs and the global land program Copernicus, which uses spatial information from Sentinel-2 for use in the agricultural sector; estimation of the constellation characteristics of Sentinel-2, imaging equipment and remote sensing data processing results by ground services received from Internet services; the use of Sentinel-2 multispectral space imagery for monitoring crops during the entire vegetation phase. The following results were obtained. After analyzing agricultural producers needs and the European Space Agency program, the feasibility of using multispectral space images taken by the Multispectral Instrument installed on satellites Sentinel-2 was established. Free access to the space imagery database is provided through the Copernicus Open Access Hub Internet Service. For the researched territory, Poltava region, Chutov district, the village of Vilkhovatka, various time space images were obtained and the normalized difference vegetation index (NDVI) was calculated. Histogram analysis of the obtained vegetation index values distribution within a single field (corn to grain) allows to reveal a quantitative and qualitative change in biomass, indicating a change in the vegetative phase. Conclusions. The approach described in this paper allows to conduct monitoring of the cropping state during the vegetation phase using both qualitative – visual analysis and quantitative – NDVI index, criteria. The change in the values of the normalized difference vegetation index can reveal a change in the biomass state. However, for calculating the NDVI index, data from near-infrared and red channels is needed, which complicates the acquisition of the original image. Therefore, in order to obtain the quantitative criteria in subsequent jobs, it is expedient to consider the possibility of using fractal dimension, which will reduce the amount of input data required for calculations.

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Cloud system ArcGIS online as a managerial decision-making tool in agricultural production

Cited by 5 publications

References 4 publications

Map Whiteboard Cloud Solution for Collaborative Editing of Geographic Information

Map Whiteboard Cloud Solution for Collaborative Editing of Geographic Information

A spatial decision support system for municipal solid waste landfill sites (case study: The Mazandaran Province, Iran)

Моніторинг Сільськогосподарських Культур Із Застосуванням Космічних Знімків Sentinel-2

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