Geomatic techniques for utilities consumption analysis in urban areas during emergency periods

Authors

  • Sara Zollini DICEAA, Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, Via G. Gronchi 18, 67100, L’Aquila, Italy
  • Maria Alicandro DICEAA, Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, Via G. Gronchi 18, 67100, L’Aquila, Italy
  • Donatella Dominici

DOI:

https://doi.org/10.48258.4

Keywords:

uav, photogrammetry, remote sensing, change detection, covid-19, eumap, superview-i

Abstract

This paper has the main purpose of proposing a methodology to understand the occupation of parking spots by using the synergy of different geomatic techniques. Aerial, satellites, and UAV data are studied through the OBIA to analyse, by change detection, the main differences pre-, during and post-lockdown due to Covid-19.

The first results are really promising and pave the ground for a future automation of the proposed procedure. The results can be also integrated in BIM and GIS to help the management of utilities consumption in emergency periods, and they create a dataset to enhance and increase consumption efficiency in residential areas.

References

Alicandro, M., Dominici, D., Pascucci, N., Quaresima, R., & Zollini, S. (2023). Enhanced Algorithms to Extract Decay Forms of Concrete Infrastructures from Uav Photogrammetric Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 9-15.

Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2-16. https://doi.org/10.1016/j.

isprsjprs.2009.06.004

Boston, G. (2014). Upper Town or Ano Poli: Historical Thessaloniki https://www.greekboston.com/travel/ano-poli-thessaloniki/ (Accessed 9-11-23).

Chen, X., Shen, X., Cao, L. (2023). Tree Species Classification in Subtropical Natural Forests Using High-Resolution UAV RGB and SuperView-1

Multispectral Imageries Based on Deep Learning Network Approaches: A Case Study within the Baima Snow Mountain National Nature Reserve, China.

Remote Sens. 15, 2697. https://doi.org/10.3390/rs15102697 European Space Agency. (2016). GaoJing / SuperView Earth Observation Constellation - eoPortal https://www.eoportal.org/satellite-missions/gaojing#Gaojing-Superview.html.1 (Accessed 9-11-23).

Froidevaux, A., Julier, A., Lifschitz, A., Pham, M. T., Dambreville, R., Lefèvre, S., ... & Huynh, T. L. (2020, September). Vehicle detection and counting

from VHR satellite images: Efforts and open issues. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 256-

. IEEE.

Khryaschev, V., Ivanovsky, L. (2019). Urban areas analysis using satellite image segmentation and deep neural network. E3S Web Conf. 135, 01064. https://doi.org/10.1051/e3sconf/201913501064

Li, D., Wang, M., Jiang, J. (2021). China’s high-resolution optical remote sensing satellites and their mapping applications. Geo-Spat. Inf. Sci. 24, 85-94. https://doi.org/10.1080/10095020.2020.1838957

Prokopyeva, K.O. (2022). The Use of Multi-Temporal High-Resolution Satellite Images to Soil Salinity Assessment of the Solonetzic Complex

(Republic of Kalmykia). Arid Ecosyst. 12, 394-406. https://doi.org/10.1134/S2079096122040163

Sun, W., Chen, B., Messinger, D. (2014). Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images. Opt. Eng. 53, 013107. https://doi.org/10.1117/1.OE.53.1.013107

Teodoro, A.C., Araujo, R. (2016). Comparison of performance of object based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data. J. Appl. Remote Sens. 10, 016011. https://doi.org/10.1117/1.JRS.10.016011

Xia, W., Chen, J., Liu, J., Ma, C., Liu, W. (2021). Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional

Spectral–Topographic Fusion Network. Remote Sens. 13, 5116. https://doi.org/10.3390/rs13245116

Zollini, S., Alicandro, M., Dominici, D., Quaresima, R., Giallonardo, M. (2020). UAV Photogrammetry for Concrete Bridge Inspection Using Object-Based Image Analysis (OBIA). Remote Sens. 12, 3180. https://doi.org/10.3390/rs12193180

Published

2023-10-16

How to Cite

Zollini, S., Alicandro, M., & Dominici, D. (2023). Geomatic techniques for utilities consumption analysis in urban areas during emergency periods. GEOmedia, 27(3). https://doi.org/10.48258.4

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