Dynamic programming for aligning sketch maps

Detalhes bibliográficos
Autor(a) principal: León, Violeta Ana Luz Sosa
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/94404
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Dynamic programming for aligning sketch mapsSketch mapMetric mapDynamic programmingTabu searchLearning algorithmLink analysisAlignmentDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesSketch maps play an important role in communicating spatial knowledge, particularly in applications interested in identifying correspondences to metric maps for land tenure in rural communities. The interpretation of a sketch map is linked to the users’ spatial reasoning and the number of features included. Additionally, in order to make use of the information provided by sketch maps, the integration with information systems is needed but is convoluted. The process of identifying which element in the base map is being represented in the sketch map involves the use of correct descriptors and structures to manage them. In the past years, different methods to give a solution to the sketch matching problem employs iterative methods using static scores to create a subset of correspondences. In this thesis, we propose an implementation for the automatic aligning of the sketch to metric maps, based on dynamic programming techniques from reinforcement learning. Our solution is distinctive from other approaches as it searches for pair equivalences by exploring the environment of the search space and learning from positive rewards derived from a custom scoring system. Scores are used to evaluate the likeliness of a candidate pair to belong to the final solution, and the results are back up in a state-value function to recover the best subset states and recovering the highest scored combinations. Reinforcement learning algorithms are dynamic and robust solutions for finding the best solution in an ample search space. The proposed workflow improves the outcoming spatial configuration for the aligned features compared to previous approaches, specifically the Tabu Search.Schwering, AngelaChipofya, Malumbo ChakaPainho, Marco Octávio TrindadeRUNLeón, Violeta Ana Luz Sosa2020-03-17T16:04:19Z2020-01-312020-01-31T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/94404TID:202459810enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:42:23Zoai:run.unl.pt:10362/94404Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:56.996226Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Dynamic programming for aligning sketch maps
title Dynamic programming for aligning sketch maps
spellingShingle Dynamic programming for aligning sketch maps
León, Violeta Ana Luz Sosa
Sketch map
Metric map
Dynamic programming
Tabu search
Learning algorithm
Link analysis
Alignment
title_short Dynamic programming for aligning sketch maps
title_full Dynamic programming for aligning sketch maps
title_fullStr Dynamic programming for aligning sketch maps
title_full_unstemmed Dynamic programming for aligning sketch maps
title_sort Dynamic programming for aligning sketch maps
author León, Violeta Ana Luz Sosa
author_facet León, Violeta Ana Luz Sosa
author_role author
dc.contributor.none.fl_str_mv Schwering, Angela
Chipofya, Malumbo Chaka
Painho, Marco Octávio Trindade
RUN
dc.contributor.author.fl_str_mv León, Violeta Ana Luz Sosa
dc.subject.por.fl_str_mv Sketch map
Metric map
Dynamic programming
Tabu search
Learning algorithm
Link analysis
Alignment
topic Sketch map
Metric map
Dynamic programming
Tabu search
Learning algorithm
Link analysis
Alignment
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2020
dc.date.none.fl_str_mv 2020-03-17T16:04:19Z
2020-01-31
2020-01-31T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/94404
TID:202459810
url http://hdl.handle.net/10362/94404
identifier_str_mv TID:202459810
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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