Novel Cluster Modeling for the Spatiotemporal Analysis of Coastal Upwelling

Susana Nascimento, Alexandre Martins, Paulo Relvas, Joaquim F. Luís, Boris Mirkin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This work proposes a spatiotemporal clustering approach for the analysis of coastal upwelling from Sea Surface Temperature (SST) grid maps derived from satellite images. The algorithm, Core-Shell clustering, models the upwelling as an evolving cluster whose core points are constant during a certain time window while the shell points move through an in-and-out binary sequence. The least squares minimization of clustering criterion allows to derive key parameters in an automated way. The algorithm is initialized with an extension of Seeded Region Growing offering self-tuning thresholding, the STSEC algorithm, that is able to precisely delineate the upwelling region at each SST instant map. Yet, the application of STSEC to the SST grid maps as temporal data puts the business of finding relatively stable “time windows”, here called “time ranges”, for obtaining the core clusters onto an automated footing. The experiments conducted with three yearly collections of SST data of the Portuguese coast shown that the core-shell clusters precisely recognize the upwelling regions taking as ground-truth the STSEC segmentations with Kulczynski similarity score values higher than 98%. Also, the extracted time series of upwelling features presented consistent regularities among the three independent upwelling seasons.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Proceedings
EditorsGoreti Marreiros, Bruno Martins, Ana Paiva, Alberto Sardinha, Bernardete Ribeiro
Place of PublicationCham
Number of pages12
ISBN (Electronic)978-3-031-16474-3
ISBN (Print)978-3-031-16473-6
Publication statusPublished - 2022
Event21st EPIA Conference on Artificial Intelligence, EPIA 2022 - Lisbon, Portugal
Duration: 31 Aug 20222 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13566 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st EPIA Conference on Artificial Intelligence, EPIA 2022


  • Coastal upwelling
  • Sequential clustering
  • Spatiotemporal clustering
  • Time window


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