Skip to main content
Remote SensingVolume 13, Issue 16, 2 August 2021, Article number 3150

Building change detection method to support register of identified changes on buildings(Article)(Open Access)

  Save all to author list
  • Faculty of Technical Sciences, University of Novi Sad, Novi Sad, 106314, Serbia

Abstract

Based on a newly adopted “Rulebook on the records of identified changes on buildings in Serbia” (2020) that regulates the content, establishment, maintenance and use of records on identified changes on buildings, it is expected that the geodetic-cadastral information system will be extended with these records. The records contain data on determined changes of buildings in relation to the reference epoch of aerial or satellite imagery, namely data on buildings: (1) that are not registered in the real estate cadastre; (2) which are registered in the real estate cadastre, and have been changed in terms of the dimensions in relation to the data registered in the real estate cadastre; (3) which are registered in the real estate cadastre, but are removed on the ground. For this purpose, the LADM-based cadastral data model for Serbia is extended to include records on identified changes on buildings. In the year 2020, Republic Geodetic Authority commenced a new satellite acquisition for the purpose of restoration of official buildings registry, as part of a World Bank project for improving land administration in Serbia. Using this satellite imagery and existing cadastral data, we propose a method based on comparison of object-based and pixel-based image analysis approaches to automatically detect newly built, changed or demolished buildings and import these data into extended cadastral records. Our results, using only VHR images containing only RGB and NIR bands, showed object identification accuracy ranging from 84% to 88%, with kappa statistic from 89% to 96%. The accuracy of obtained results is satisfactory for the purpose of developing a register of changes on buildings to keep cadastral records up to date and to support activities related to legalization of illegal buildings, etc.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. © 2021, MDPI AG. All rights reserved.

Author keywords

Building footprint extractionCadastreChange detectionClassificationImage segmentationNeural networkVHR aerial images

Indexed keywords

Engineering controlled terms:AntennasBank protectionData flow analysisGeodesyGeodetic satellitesSatellite imagery
Engineering uncontrolled termsBuilding change detectionDemolished buildingsKappa statisticLand administrationNew satellitesObject basedObject identificationSwitzerland
Engineering main heading:Buildings

Funding details

Funding sponsor Funding number Acronym
Ministarstvo Prosvete, Nauke i Tehnološkog RazvojaMPNTR
  • 1

    Results presented in this paper are part of the research conducted within the Grant No. 37017, Ministry of Education and Science of the Republic of Serbia.

  • ISSN: 20724292
  • Source Type: Journal
  • Original language: English
  • DOI: 10.3390/rs13163150
  • Document Type: Article
  • Publisher: MDPI AG

  Jovanović, D.; Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;
© Copyright 2021 Elsevier B.V., All rights reserved.

Cited by 6 documents

Rahman, I.A. , Mohd Noor, M.F.I. , Ani, A.I.C.
Technologies in Urban Planning: Systematic Review on Unauthorized Building Modifications Monitoring
(2024) Paper Asia
Alrasheedi, K.G. , Dewan, A. , El-Mowafy, A.
A Spatiotemporal Ontology of Informal Settlements Using a Combination of OBIA-RF with Worldview-3 and Landsat Data
(2024) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Mudau, N. , Mhangara, P.
Assessment of Spatial Patterns of Backyard Shacks Using Landscape Metrics
(2023) Drones
View details of all 6 citations
{"topic":{"name":"Remote Sensing; Image Analysis; Vegetation","id":3686,"uri":"Topic/3686","prominencePercentile":89.53127,"prominencePercentileString":"89.531","overallScholarlyOutput":0},"dig":"d4d5128baf62725e58252ee6ed97971a92059f326c4d666e2a2791932906aed3"}

SciVal Topic Prominence

Topic:
Prominence percentile: