VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES

Manual vehicle counting is often tedious, expensive, and time-consuming. While automatic counting from CCTV allows for annual average daily traffic estimation, CCTV files in the Philippines are not available to the public and do not fully cover all road extents. In this study, Remote Sensing and Geo...

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Autores principales:	H. B. Banaag, M. S. Litana, R. V. Ramos
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/c90ba9fe59194fad8d6ac8f04e8f36e2
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id	oai:doaj.org-article:c90ba9fe59194fad8d6ac8f04e8f36e2
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spelling	oai:doaj.org-article:c90ba9fe59194fad8d6ac8f04e8f36e22021-11-19T00:00:22ZVEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES10.5194/isprs-archives-XLVI-4-W6-2021-57-20211682-17502194-9034https://doaj.org/article/c90ba9fe59194fad8d6ac8f04e8f36e22021-11-01T00:00:00Zhttps://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W6-2021/57/2021/isprs-archives-XLVI-4-W6-2021-57-2021.pdfhttps://doaj.org/toc/1682-1750https://doaj.org/toc/2194-9034Manual vehicle counting is often tedious, expensive, and time-consuming. While automatic counting from CCTV allows for annual average daily traffic estimation, CCTV files in the Philippines are not available to the public and do not fully cover all road extents. In this study, Remote Sensing and Geographic Information Systems (GIS) techniques are employed to use readily available satellite images to obtain vehicle count in selected road segments in the Central Business Districts of Quezon City before and after the COVID-19 lockdown. Using the existing Google Earth Images, a segmentation algorithm using ENVI Feature Classification was developed to allow remote counting of vehicles from the earliest image in 2018. The devised algorithm was able to delineate, identify, and classify according to the types of vehicles that are visible on the image. An average error rate of 12.24% was found by comparison of automated counts and manual counts on the images, while a regression analysis yielded a value of R<sup>2</sup> = 0.9227 that denoted a strong relationship between automated and manual counts. Vehicle density was calculated, and percent differences were obtained to determine the relative differences of the vehicle counts from the vehicle count of the earliest image taken in 2018. It was found that the vehicle density declined by at least 81% by March 25, 2020. The methodological framework presented in this study provides estimates of vehicle counts and vehicle density. It can be further improved if vehicle counts, on the same location and period, from field validation surveys are available.H. B. BanaagM. S. LitanaR. V. RamosCopernicus PublicationsarticleTechnologyTEngineering (General). Civil engineering (General)TA1-2040Applied optics. PhotonicsTA1501-1820ENThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-4-W6-2021, Pp 57-63 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
spellingShingle	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 H. B. Banaag M. S. Litana R. V. Ramos VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES
description	Manual vehicle counting is often tedious, expensive, and time-consuming. While automatic counting from CCTV allows for annual average daily traffic estimation, CCTV files in the Philippines are not available to the public and do not fully cover all road extents. In this study, Remote Sensing and Geographic Information Systems (GIS) techniques are employed to use readily available satellite images to obtain vehicle count in selected road segments in the Central Business Districts of Quezon City before and after the COVID-19 lockdown. Using the existing Google Earth Images, a segmentation algorithm using ENVI Feature Classification was developed to allow remote counting of vehicles from the earliest image in 2018. The devised algorithm was able to delineate, identify, and classify according to the types of vehicles that are visible on the image. An average error rate of 12.24% was found by comparison of automated counts and manual counts on the images, while a regression analysis yielded a value of R<sup>2</sup> = 0.9227 that denoted a strong relationship between automated and manual counts. Vehicle density was calculated, and percent differences were obtained to determine the relative differences of the vehicle counts from the vehicle count of the earliest image taken in 2018. It was found that the vehicle density declined by at least 81% by March 25, 2020. The methodological framework presented in this study provides estimates of vehicle counts and vehicle density. It can be further improved if vehicle counts, on the same location and period, from field validation surveys are available.
format	article
author	H. B. Banaag M. S. Litana R. V. Ramos
author_facet	H. B. Banaag M. S. Litana R. V. Ramos
author_sort	H. B. Banaag
title	VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES
title_short	VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES
title_full	VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES
title_fullStr	VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES
title_full_unstemmed	VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES
title_sort	vehicle density estimation in quezon city using object-based feature extraction on satellite images
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/c90ba9fe59194fad8d6ac8f04e8f36e2
work_keys_str_mv	AT hbbanaag vehicledensityestimationinquezoncityusingobjectbasedfeatureextractiononsatelliteimages AT mslitana vehicledensityestimationinquezoncityusingobjectbasedfeatureextractiononsatelliteimages AT rvramos vehicledensityestimationinquezoncityusingobjectbasedfeatureextractiononsatelliteimages
_version_	1718420694118694912

VEHICLE DENSITY ESTIMATION IN QUEZON CITY USING OBJECT-BASED FEATURE EXTRACTION ON SATELLITE IMAGES

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