Geomatics and Environmental Engineering, vol. 18, no. 3

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Vol. 18 No. 3 (2024): Geomatics and Environmental Engineering

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Submitted
Nov 30, 2023
Accepted
Feb 29, 2024
Published
Apr 18, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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

https://doi.org/10.7494/geom.2024.18.3.25
Miguel Chicchon
Pontificia Universidad Católica del Perú, Escuela de Posgrado, San Miguel, Lima, Perú
https://orcid.org/0000-0002-2228-8557
Eva Savina Malinverni
Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e Architettura, Ancona, Italy
https://orcid.org/0000-0001-6582-2943
Marsia Sanità
Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e Architettura, Ancona, Italy
https://orcid.org/0009-0002-9828-2626
Roberto Pierdicca
Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e Architettura, Ancona, Italy
https://orcid.org/0000-0002-9160-834X
Francesca Colosi
Consiglio Nazionale delle Ricerche (CNR), Istituto di Scienze del Patrimonio Culturale, Rome, Italy
https://orcid.org/0000-0002-1384-2560
Francisco James León Trujillo
Universidad de Lima, Instituto de Investigación Científica, Carrera de Ingeniería Civil, Lima, Perú
https://orcid.org/0000-0003-1060-5938

Corresponding Author(s) : Marsia Sanità

m.sanita@pm.univpm.it

Geomatics and Environmental Engineering, Vol. 18 No. 3 (2024): Geomatics and Environmental Engineering

Abstract

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.

Keywords

building detection neural network segmentation HRV images SpaceNet data set
Chicchon, M., Malinverni, E. S., Sanità, M., Pierdicca, R., Colosi, F., & Trujillo, F. J. L. (2024). Building Semantic Segmentation Using UNet Convolutional Network on SpaceNet Public Data Sets for Monitoring Surrounding Area of Chan Chan (Peru). Geomatics and Environmental Engineering, 18(3), 25–43. https://doi.org/10.7494/geom.2024.18.3.25
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