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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References
  1. Chidi C.L.: Urbanization and Soil Erosion in Kathmandu Valley, Nepal. [in:] Pradhan P.K., Leimgruber W. (eds.), Nature, Society, and Marginality: Case Studies from Nepal, Southeast Asia and Other Regions, Perspectives on Geographical Marginality, vol. 8, Springer, Cham 2022, pp. 67–83. https://doi.org/10.1007/978-3-031-21325-0_5.
  2. Pansoni S., Tiribelli S., Paolanti M., Frontoni E., Giovanola B.: Design of an ethical framework for artificial intelligence in cultural heritage. [in:] 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS 2023): West Lafayette, Indiana, USA, 18–20 May 2023, IEEE, Piscataway 2023, pp. 1–5. https://doi.org/10.1109/ETHICS57328.2023.10155020.
  3. Jung H., Choi H.-S., Kan M.: Boundary enhancement semantic segmentation for building extraction from remote sensed image. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, pp. 1–12. https://doi.org/10.1109/TGRS.2021.3108781.
  4. Cardellicchio A., Ruggieri S., Leggieri V., Uva G.: View VULMA: Data set for training a machine-learning tool for a fast vulnerability analysis of existing buildings. Data, vol. 7(1), 2022, 4. https://doi.org/10.3390/data7010004.
  5. Ruggieri S., Cardellicchio A., Leggieri V., Uva G.: Machine-learning based vulnerability analysis of existing buildings. Automation in Construction, vol. 132, 2021, 103936. https://doi.org/10.1016/j.autcon.2021.103936.
  6. Yuan X., Shi J., Gu L.: A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Systems with Applications, vol. 169, 2021, 114417. https://doi.org/10.1016/j.eswa.2020.114417.
  7. Wang L., Li R., Zhang C., Fang S., Duan C., Meng X., Atkinson P.M.: UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 190, 2022, pp. 196–214. https://doi.org/10.1016/j.isprsjprs.2022.06.008.
  8. Bazila F., Ankush M.: A comparative study of deep learning and traditional methods for environmental remote sensing. ITM Web of Conferences, vol. 56, 2023, 03002. https://doi.org/10.1051/itmconf/20235603002.
  9. Fu Y., Li J., Weng Q., Zheng Q., Li L., Dai S., Guo B.: Characterizing the spatial pattern of annual urban growth by using time series Landsat imagery. Science of The Total Environment, vol. 666, 2019, pp. 274–284. https://doi.org/10.1016/j.scitotenv.2019.02.178.
  10. Mikrut S., Papuci-Wladyka E., Struś A., Puntos J.K., Głowienka E.: The use of photogrammetry in archaeology and multimedia open-air performance in the Castle Square of Kato Paphos. [in:] BGC-Geomatics 2018: 2018 Baltic Geodetic Congress: 21–23 June 2018, Olsztyn, Poland: Proceedings, IEEE, Piscataway 2018, pp. 353–358. https://doi.org/10.1109/BGC-Geomatics.2018.00073.
  11. Das S., Angadi D.P.: Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: A micro-level study. GeoJournal, vol. 87(3), 2022, pp. 2101–2123. https://doi.org/10.1007/s10708-020-10359-1.
  12. Krishnaveni K.S., Anilkumar P.P.: Managing urban sprawl using remote sensing and GIS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-3/W11, 2020, pp. 59–66. https://doi.org/10.5194/isprs-archives-XLII-3-W11-59-2020.
  13. Coulter L., Hall T., Guzman L., Kasahara I.: Satellite Image building detection using U-Net convolutional neural network. https://www.luisjguzman.com/media/EE5561/building_detection.pdf [access: 31.10.2023].
  14. Ronneberger O., Fischer P., Brox T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. [in:] Navab N., Hornegger J., Wells W.M., Frangi A.F. (eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III, Lecture Notes in Computer Science, vol. 9351, Springer, Cham 2015, pp. 234–241. https://doi.org/10.1007/978-3-319-24574-4_28.
  15. Gomroki M., Hasanlou M., Reinartz P.: STCD-EffV2T Unet: Semi transfer learning EfficientNetV2 T-Unet network for urban/land cover change detection using Sentinel-2 satellite images. Remote Sensing, vol. 15(5), 2023, 1232. https://doi.org/10.3390/rs15051232.
  16. Vasantrao C.P., Gupta N.: Wader hunt optimization based UNET model for change detection in satellite images. International Journal of Information Technology, vol. 15(3), 2023, pp. 1611–1623. https://doi.org/10.1007/s41870-023-01167-0.
  17. Singh N.J., Nongmeikapam K.: Semantic segmentation of satellite images using deep-unet. Arabian Journal for Science and Engineering, vol. 48(2), 2023, pp. 1193–1205. https://doi.org/10.1007/s13369-022-06734-4.
  18. Colosi F., Gabrielli R., Orazi R., Malinverni E.S.: Discovering Chan Chan: Modern technologies for urban and architectural analysis. Archeologia e Calcolatori, vol. 24, 2013, pp. 187–207. https://api.semanticscholar.org/CorpusID:60929933.
  19. Malinverni E.S., Pierdicca R., Colosi F., Orazi R.: Dissemination in archaeology: A GIS-based StoryMap for Chan Chan. Journal of Cultural Heritage Management and Sustainable Development, vol. 9(4), 2019, pp. 500–519. https://doi.org/10.1108/JCHMSD-07-2018-0048.
  20. Deng J., Dong W., Socher R., Li L.-J., Li K., Fei-Fei L.: ImageNet: A large-scale hierarchical image database. [in:] 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009): Miami, Florida, USA, 20–25 June 2009, IEEE, Piscataway, pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848.
  21. Van Etten A., Lindenbaum D., Bacastow T.M.: SpaceNet: A remote sensing dataset and challenge series. 2018. https://doi.org/10.48550/arXiv.1807.01232.
  22. ImageNet. https://www.image-net.org/ [access: 20.03.2023].
  23. Tan M., Le Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. [in:] 36th International Conference on Machine Learning (ICML 2019): Long Beach, California, USA, 9–15 June 2019, Proceedings of Machine Learning Research, vol. 97, International Machine Learning Society, Stroudsburg 2019, pp. 10691–10700. https://doi.org/10.48550/arXiv.1905.11946.
  24. Chicchon M., Bedon H., Del-Blanco C.R., Sipiran I.: Semantic segmentation of fish and underwater environments using deep convolutional neural networks and learned active contours. IEEE Access, vol. 11, 2023, pp. 33652–33665. https://doi.org/10.1109/ACCESS.2023.3262649.
  25. Sørensen T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Kongelige Danske videnskabernes selskabs. Biologiske skrifter, bd. 5(4), Ejnar Munksgaard, København 1948.
  26. Rizwan I Haque I., Neubert J.: Deep learning approaches to biomedical image segmentation. Informatics in Medicine Unlocked, vol. 18, 2020, 100297. https://doi.org/10.1016/j.imu.2020.100297.
  27. Chen F., Wang N., Yu B., Wang L.: Res2-Unet: A new deep architecture for building detection from high spatial resolution images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, 2022, pp. 1494–1501. https://doi.org/10.1109/JSTARS.2022.3146430.
  28. Sun Y., Bi F., Gao Y., Chen L., Feng S.: A multi-attention UNet for semantic segmentation in remote sensing images. Symmetry, vol. 14(5), 2022, 906. https://doi.org/10.3390/sym14050906.
  29. Abdollahi A., Pradhan B., Shukla N., Chakraborty S., Alamri A.: Multi-object segmentation in complex urban scenes from high-resolution remote sensing data. Remote Sensing, vol. 13(18), 2021, 3710. https://doi.org/10.3390/rs13183710.
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