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PROMORE: a procedural modeler of virtual rural environments with artificial dataset generation capabilities for remote sensing contexts

2025Journal article Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg12:Produção e Consumo Sustentáveis
dc.contributor.authorAdão, Telmo
dc.contributor.authorCerqueira, João
dc.contributor.authorAdão, Miguel
dc.contributor.authorSilva, Nuno
dc.contributor.authorPascoal, David
dc.contributor.authorMagalhães, Luís G.
dc.contributor.authorBarros, Tiago
dc.contributor.authorPremebida, Cristiano
dc.contributor.authorNunes, Urbano J.
dc.contributor.authorPeres, Emanuel
dc.contributor.authorMorais, Raul
dc.date.accessioned2025-06-13T10:07:25Z
dc.date.available2025-06-13T10:07:25Z
dc.date.issued2025
dc.description.abstractRemote sensing (RS) is a rapidly evolving field that facilitates the study of phenomena on the Earth’s surface. Through various platforms, including satellites, manned aircraft, and remotely piloted aerial vehicles (RPAV), RS has been strategically applied to critical sectors like agriculture and forestry, which are essential for humanity’s sustenance. Key applications include crops classification, yield estimation and livestock monitoring and quantification. In the era of artificial intelligence (AI), the development of deep learning (DL) models for such applications often requires extensive field data collection and labor-intensive image labeling, which are both time-consuming and resource-intensive. To address these challenges, this paper presents Procedural Modeling of Rural Environments (PROMORE), a parameterizable, ontology driven system designed to generate 3D virtual environments encompassing forestry, farmland – mainly focused on vineyards – and village settings. This system also implements functionalities to automate the extraction of training data for deep learning applications in remote sensing, with the declared aim of providing complementary capabilities to data augmentation techniques, encompassing both traditional methods (e.g., flips, rotations, zooming) and advanced approaches such as generative adversarial networks (GANs). By simulating RPAV flights and managing virtual object visibility, PROMORE enables the automatic labeling, delineation, and highlighting of elements of interest (e.g., vine plants, trees, buildings), facilitating the generation of datasets tailored for tasks such as semantic segmentation, and object detection.eng
dc.description.sponsorshipThe authors would like to acknowledge the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia-FCT), under the projects UIDB/04033/2020, LA/P/0126/2020, GreenBotics (ref. PTDC/EEI-ROB/2459/2021), UIDB/00048/2020 and the PhD grant with reference 2021.06492.BD.
dc.identifier.citationAdão, Telmo; Cerqueira, João; Adão, Miguel; Silva, Nuno; Pascoal, David; Magalhães, Luís G.; Barros, Tiago; Premebida, Cristiano; Nunes, Urbano J.; Peres, Emanuel; Morais, Raul. (2025). PROMORE: A Procedural Modeler of Virtual Rural Environments With Artificial Dataset Generation Capabilities for Remote Sensing Contexts. IEEE Access. ISSN 2169-3536. 13, p. 47632-47652
dc.identifier.doi10.1109/access.2025.3548513
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10198/34594
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relationCentre for the Research and Technology of Agro-Environmental and Biological Sciences
dc.relationInstitute for innovation, capacity building and sustainability of agri-food production
dc.relation.ispartofIEEE Access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectComputer graphics
dc.subjectVirtual environments
dc.subject3D modeling
dc.subjectProcedural modeling
dc.subjectArtificial data engineering
dc.subjectArtificial dataset
dc.subjectDeep learning
dc.titlePROMORE: a procedural modeler of virtual rural environments with artificial dataset generation capabilities for remote sensing contextseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for the Research and Technology of Agro-Environmental and Biological Sciences
oaire.awardTitleInstitute for innovation, capacity building and sustainability of agri-food production
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04033%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0126%2F2020/PT
oaire.citation.endPage47652
oaire.citation.startPage47632
oaire.citation.titleIEEE Access
oaire.citation.volume13
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isProjectOfPublicationac4fb709-719a-450b-8c96-17592d46f5e9
relation.isProjectOfPublication3c81413c-6a3a-453a-bb53-9e883a270537
relation.isProjectOfPublication.latestForDiscoveryac4fb709-719a-450b-8c96-17592d46f5e9

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