Artykuł  

Building Semantic Segmentation Using UNet Convolutional Network on SpaceNet Public Data Sets for Monitoring Surrounding Area of Chan Chan (Peru)

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creativeworkseries.issn1898-1135
dc.contributor.authorChicchon, Miguel
dc.contributor.authorMalinverni, Eva Savina
dc.contributor.authorSanità, Marsia
dc.contributor.authorPierdicca, Roberto
dc.contributor.authorColosi, Francesca
dc.contributor.authorTrujillo, Francisco James León
dc.date.issued2024
dc.description.abstractThe amount of damage to cultural heritage sites is increasing rapidly every year. This is due to inadequate heritage management and uncontrolled urban growth as well as unpredictable seismic and atmospheric events that manifest themselves in a continuously deteriorating ecosystem. Thus, applications of artificial intelligence (AI) in remote-sensing (RS) techniques (machine-learning and deep-learning algorithms) for monitoring archaeological sites have increased in recent years. This research involves the surrounding area of the archaeological site of Chan Chan in Peru in particular. An approach that is based on the use of AI algorithms for building footprint segmentation and change-detection analysis by means of RS images is proposed. It involves a UNet convolutional network based on an EfficientNet B0 to B7 encoder. The network was trained on two public data sets from SpaceNet that were based on WV2 and WV3 satellite images: SpaceNet V1 (Rio), and SpaceNet V2 (Shanghai). In the pre-processing phase, the images from the two data sets have been equalized in order to improve their quality and avoid overfitting. The building segmentation has been performed on HRV images of the study area that were downloaded from Google Earth Pro. The value that was achieved in the IoU metric was around 70% in both experiments. The purpose of this proposed methodology is to assist scientists in drafting monitoring and conservation protocols based on already-recorded data in order to prevent future disasters and hazards.en
dc.description.placeOfPublicationKraków
dc.description.versionwersja wydawnicza
dc.identifier.doihttps://doi.org/10.7494/geom.2024.18.3.25
dc.identifier.eissn2300-7096
dc.identifier.issn1898-1136
dc.identifier.urihttps://repo.agh.edu.pl/handle/AGH/108418
dc.language.isoeng
dc.publisherWydawnictwa AGH
dc.rightsAttribution 4.0 International
dc.rights.accessotwarty dostęp
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcode
dc.subjectbuilding detectionen
dc.subjectneural networken
dc.subjectsegmentationen
dc.subjectHRV imagesen
dc.subjectSpaceNet data seten
dc.titleBuilding Semantic Segmentation Using UNet Convolutional Network on SpaceNet Public Data Sets for Monitoring Surrounding Area of Chan Chan (Peru)en
dc.title.relatedGeomatics and Environmental Engineering
dc.typeartykuł
dspace.entity.typePublication
publicationissue.issueNumberNo. 3
publicationissue.paginationpp. 25-43
publicationvolume.volumeNumberVol. 18
relation.isJournalIssueOfPublication3f63e1ed-b3b8-4abf-856c-745b6b138c47
relation.isJournalIssueOfPublication.latestForDiscovery3f63e1ed-b3b8-4abf-856c-745b6b138c47
relation.isJournalOfPublication102998b2-3fd0-4247-98bf-973d6a9ba2d9
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