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

• Autorzy: Chicchon Miguel , Malinverni Eva Savina , Sanità Marsia , Pierdicca Roberto , Colosi Francesca , Trujillo Francisco James León

Identyfikatory

DOI

10.7494/geom.2024.18.3.25

• Języki publikacji: EN

Abstrakty

EN

The 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 changedetection 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.

Słowa kluczowe

EN

building detection; neural network; segmentation; HRV images; SpaceNet data set

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2024
• Tom: Vol. 18, no. 3
• Strony: 25--43
• Opis fizyczny: Bibliogr. 29 poz., fot., rys., tab.

Twórcy

autor

Chicchon Miguel

• Pontificia Universidad Católica del Perú, Escuela de Posgrado, San Miguel, Lima, Perú

• https://orcid.org/0000-0002-2228-8557

autor

Malinverni Eva Savina

• Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e Architettura, Ancona, Italy

• https://orcid.org/0000-0001-6582-2943

autor

Sanità Marsia

• Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e Architettura, Ancona, Italy

• https://orcid.org/0009-0002-9828-2626

autor

Pierdicca Roberto

• Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e Architettura, Ancona, Italy

• https://orcid.org/0000-0002-9160-834X

autor

Colosi Francesca

• Consiglio Nazionale delle Ricerche (CNR), Istituto di Scienze del Patrimonio Culturale, Rome, Italy

• https://orcid.org/0000-0002-1384-2560

autor

Trujillo Francisco James León

• Universidad de Lima, Instituto de Investigación Científica, Carrera de Ingeniería Civil, Lima, Perú

• https://orcid.org/0000-0003-1060-5938

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)