Agricultural property monitoring using geospatial foundation models.

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dc.contributorANTONIO GOULART, UNIVERSIDADE DE SÃO PAULO; MATHEUS PAPA; IVAN BERGIER TAVARES DE LIMA, CNPTIA; DURVAL DOURADO, UNIVERSIDADE DE SÃO PAULO.
dc.creatorGOULART, A.
dc.creatorPAPA, M.
dc.creatorBERGIER, I.
dc.creatorDOURADO, D.
dc.date2026-03-30T17:51:49Z
dc.date2026-03-30T17:51:49Z
dc.date2026-03-30
dc.date2025
dc.date.accessioned2026-07-07T04:18:37Z
dc.descriptionIn this paper we present a preliminary study on the application of geospatial foundation models to the satellite-based monitoring of agricultural properties. Images from the Sentinel-2 mission are processed with the Clay foundation model in order to produce embeddings in the model’s latent space, which are later reduced in dimension with a principal component analysis and plotted in 2 dimensions to enable visual inspection. A case study was carried out considering scenes from a property located in the Guia Lopes da Laguna agrotechnological district, a partner of Semear Digital. Two management situations were taken into account: a) traditional pasture; and b) initial preparation for an integrated livestock-forest system. Differences in the embeddings from both scenarios were analyzed, and early observations indicate that the method has potential and may become a consolidated practice in the area.
dc.descriptionOrganização: Silvia Maria Fonseca Silveira Massruhá, Durval Dourado Neto, Luciana Alvim Santos Romani, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Ivan Bergier, Maria Angelica de Andrade Leite, Vitor Del Alamo Guarda, Catarina Barbosa Careta.
dc.identifierIn: WORKSHOP CIENTÍFICO DO CENTRO DE CIÊNCIA PARA O DESENVOLVIMENTO EM AGRICULTURA DIGITAL – SEMEAR DIGITAL, 2., 2025, Campinas. Anais [...]. Piracicaba: ESALQ/USP, 2025. p. 33-40.
dc.identifier978-85-86481-94-9
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185893
dc.identifier.urihttp://hdl.handle.net/123456789/457380
dc.languageeng
dc.rightsopenAccess
dc.subjectAprendizado de máquina
dc.subjectAprendizado profundo
dc.subjectIntegração pecuária-floresta
dc.subjectProjeto Semear Digital
dc.subjectDeep machine learning
dc.subjectLivestock-forest integration
dc.subjectSensoriamento Remoto
dc.subjectRemote sensing
dc.titleAgricultural property monitoring using geospatial foundation models.
dc.typeArtigo em anais e proceedings

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