Building Change Detection Method to Support Register of Identified Changes on Buildings

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 the...

Full description

Bibliographic Details
Main Authors: Dušan Jovanović, Milan Gavrilović, Dubravka Sladić, Aleksandra Radulović, Miro Govedarica
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/16/3150
id doaj-28ffb988f5b046f3a2eec5e79c5b1e43
record_format Article
spelling doaj-28ffb988f5b046f3a2eec5e79c5b1e432021-08-26T14:17:27ZengMDPI AGRemote Sensing2072-42922021-08-01133150315010.3390/rs13163150Building Change Detection Method to Support Register of Identified Changes on BuildingsDušan Jovanović0Milan Gavrilović1Dubravka Sladić2Aleksandra Radulović3Miro Govedarica4Faculty of Technical Sciences, University of Novi Sad, 106314 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, 106314 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, 106314 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, 106314 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, 106314 Novi Sad, SerbiaBased 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.https://www.mdpi.com/2072-4292/13/16/3150image segmentationneural networkclassificationbuilding footprint extractioncadastrechange detection
collection DOAJ
language English
format Article
sources DOAJ
author Dušan Jovanović
Milan Gavrilović
Dubravka Sladić
Aleksandra Radulović
Miro Govedarica
spellingShingle Dušan Jovanović
Milan Gavrilović
Dubravka Sladić
Aleksandra Radulović
Miro Govedarica
Building Change Detection Method to Support Register of Identified Changes on Buildings
Remote Sensing
image segmentation
neural network
classification
building footprint extraction
cadastre
change detection
author_facet Dušan Jovanović
Milan Gavrilović
Dubravka Sladić
Aleksandra Radulović
Miro Govedarica
author_sort Dušan Jovanović
title Building Change Detection Method to Support Register of Identified Changes on Buildings
title_short Building Change Detection Method to Support Register of Identified Changes on Buildings
title_full Building Change Detection Method to Support Register of Identified Changes on Buildings
title_fullStr Building Change Detection Method to Support Register of Identified Changes on Buildings
title_full_unstemmed Building Change Detection Method to Support Register of Identified Changes on Buildings
title_sort building change detection method to support register of identified changes on buildings
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-08-01
description 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.
topic image segmentation
neural network
classification
building footprint extraction
cadastre
change detection
url https://www.mdpi.com/2072-4292/13/16/3150
work_keys_str_mv AT dusanjovanovic buildingchangedetectionmethodtosupportregisterofidentifiedchangesonbuildings
AT milangavrilovic buildingchangedetectionmethodtosupportregisterofidentifiedchangesonbuildings
AT dubravkasladic buildingchangedetectionmethodtosupportregisterofidentifiedchangesonbuildings
AT aleksandraradulovic buildingchangedetectionmethodtosupportregisterofidentifiedchangesonbuildings
AT mirogovedarica buildingchangedetectionmethodtosupportregisterofidentifiedchangesonbuildings
_version_ 1721190246039683072