Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives

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Collaborative location-based collecting systems (CLCS) are a particular case of collaborative systems where a community of users collaboratively collect geo-referenced data. Each CLCS sets its territory coverage objectives, commonly defined as to guarantee that all the affected territory is surveyed...

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Autores principales: Dalponte Ayastuy, María Nieves, Torres, Diego, Lattanzio, Bruno J.
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2022
Materias:
Ciencias Informáticas
Collaborative Location-based Collecting Systems
Meshing, Decision-making
Spatial Crowdsourcing
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/158787
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-158787
record_format dspace
spelling I19-R120-10915-1587872024-02-22T18:00:42Z http://sedici.unlp.edu.ar/handle/10915/158787 Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives Dalponte Ayastuy, María Nieves Torres, Diego Lattanzio, Bruno J. 2022 2023 2023-10-11T17:48:13Z en Ciencias Informáticas Collaborative Location-based Collecting Systems Meshing, Decision-making Spatial Crowdsourcing Collaborative location-based collecting systems (CLCS) are a particular case of collaborative systems where a community of users collaboratively collect geo-referenced data. Each CLCS sets its territory coverage objectives, commonly defined as to guarantee that all the affected territory is surveyed with a particular coverage criterium. This paper presents a three-step pipeline to recommend the subareas that require observations dynamically. The first step generates a disjoint and adjacent set of areas -a mesh- covering the sampling territory. The second step sets a priority and coverage objective for each area. Finally, the third step considers the project’s objectives and the area coverage situation to recommend the areas that need surveys. The output of this last step is an input for a user-task distribution process where the user’s profile is taken into account. Moreover, an example of meshing strategy and task generation is proposed. This paper is partially supported by funding provided by the STIC AmSud program, Project 22STIC-01. Facultad de Informática Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 76-87
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Collaborative Location-based Collecting Systems
Meshing, Decision-making
Spatial Crowdsourcing
spellingShingle Ciencias Informáticas
Collaborative Location-based Collecting Systems
Meshing, Decision-making
Spatial Crowdsourcing
Dalponte Ayastuy, María Nieves
Torres, Diego
Lattanzio, Bruno J.
Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives
topic_facet Ciencias Informáticas
Collaborative Location-based Collecting Systems
Meshing, Decision-making
Spatial Crowdsourcing
description Collaborative location-based collecting systems (CLCS) are a particular case of collaborative systems where a community of users collaboratively collect geo-referenced data. Each CLCS sets its territory coverage objectives, commonly defined as to guarantee that all the affected territory is surveyed with a particular coverage criterium. This paper presents a three-step pipeline to recommend the subareas that require observations dynamically. The first step generates a disjoint and adjacent set of areas -a mesh- covering the sampling territory. The second step sets a priority and coverage objective for each area. Finally, the third step considers the project’s objectives and the area coverage situation to recommend the areas that need surveys. The output of this last step is an input for a user-task distribution process where the user’s profile is taken into account. Moreover, an example of meshing strategy and task generation is proposed.
format Objeto de conferencia
Objeto de conferencia
author Dalponte Ayastuy, María Nieves
Torres, Diego
Lattanzio, Bruno J.
author_facet Dalponte Ayastuy, María Nieves
Torres, Diego
Lattanzio, Bruno J.
author_sort Dalponte Ayastuy, María Nieves
title Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives
title_short Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives
title_full Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives
title_fullStr Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives
title_full_unstemmed Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives
title_sort dynamic spatial task generation for collaborative location-based collecting systems coverage objectives
publishDate 2022
url http://sedici.unlp.edu.ar/handle/10915/158787
work_keys_str_mv AT dalponteayastuymarianieves dynamicspatialtaskgenerationforcollaborativelocationbasedcollectingsystemscoverageobjectives
AT torresdiego dynamicspatialtaskgenerationforcollaborativelocationbasedcollectingsystemscoverageobjectives
AT lattanziobrunoj dynamicspatialtaskgenerationforcollaborativelocationbasedcollectingsystemscoverageobjectives
_version_ 1807221530760839168

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