Object Based Building Extraction and Building Period Estimation From Unmanned Aerial Vehicle Data

Çömert, Resul; Kaplan, Onur

doi:10.5194/isprs-annals-iv-3-71-2018

Cited by 7 publications

(9 citation statements)

References 15 publications

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“…The proposed approach uses remote sensing techniques to estimate the building heights from satellite images that can be provided anywhere on the earth, so the proposed methodology can be applied regardless of having an inventory database that includes building height information for the region. The reliability of retrieving building heights from remote sensing data using shadows has been successfully proven in various studies with high accuracy [41,48].…”

Section: Discussionmentioning

confidence: 99%

Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

Kaplan

2021

Buildings

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Effective post-event emergency management contributes substantially to communities’ earthquake resilience, and one of the most crucial actions following an earthquake is building damage assessment. On-site inspections are dangerous, expensive, and time-consuming. Remote sensing techniques have shown great potential in localizing the most damaged regions and thus guiding aid and rescue operations in recent earthquakes. Furthermore, to prevent post-earthquake casualties, heavily damaged, unsafe buildings must be identified immediately since in most earthquakes, strong aftershocks can cause such buildings to collapse. The potential of the response spectrum concept for being associated with satellite-based remote sensing data for post-earthquake structural damage estimation was investigated in this study. In this respect, a response spectra-based post-earthquake structural damage estimation method aided by satellite-based remote sensing data was proposed to classify the buildings after an earthquake by prioritizing them based on their expected damage levels, in order to speed up the damage assessment process of critical buildings that can cause casualties in a possible strong aftershock. A case study application was implemented in the Bayrakli region in Izmir, Turkey, the most affected area by the Samos earthquake, on 30 October 2020. The damage estimations made in this research were compared with the in situ damage assessment reports prepared by the Republic of Turkey Ministry of Environment and Urbanization experts. According to the accuracy assessment results, the sensitivity of the method is high (91%), and the necessary time spent by the in situ damage assessment teams to detect the critical buildings would have been significantly reduced for the study area.

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Section: Discussionmentioning

confidence: 99%

Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

Kaplan

2021

Buildings

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“…As an accuracy assessment, the obtained height values with the applied method in this study were compared with terrestrial survey results. Thus, thirty-five out of thirty-seven building heights were extracted and these building heights were measured with laser distance meter at the field [7].…”

Section: Methodsmentioning

confidence: 99%

“…Remote sensing data and techniques have been successfully used for building information extraction [5], and post-earthquake evaluation of collapsed buildings [6]. Also, high-resolution imagery from Unmanned Aerial Vehicle (UAV) in combination with elevation models [7] for extracting building inventory information. Remote sensing data has also been used for seismic risk assessment [8,9].…”

Section: Introductionmentioning

confidence: 99%

PlanetScope Imagery for Extracting Building Inventory Information

Kaplan¹,

Kaplan²

2020

Iecg 2020

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In order to prevent serious damages from a possible earthquake and to determine the possible losses, in settlements under earthquake risk, it is very important to extract building inventory information for further determination of the performance of existing buildings. As conventional methods, such as field investigations, can be time-consuming and costly on an urban scale, approaches that are able to speed up these processes and reduce the costs are required. Determining at least some of the data required to determine the seismic performance of an existing building using alternative methods instead of conventional methods will provide a significant advantage. The study aims to investigate the potential of PlanetScope satellite imagery for extracting building inventory information. Thus, the main objectives of the study are; to extract buildings using deep learning methods, and to determine the height and the construction period of the buildings. For this purpose, two 3-m PlanetScope satellite images were used over the study area located in Eskisehir, Turkey. Over thirty buildings were located in the study area. The results showed that with PlanetScope Imagery detached buildings can be detected with high accuracy using deep learning methods, their heights can be calculated, and the construction period can be determined. For future studies, the obtained information is planned to further be processed in a Geographical Information System (GIS) for building inventory and to be used for seismic vulnerability assessment studies of existing buildings.

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“…The use of all types of Digital Elevation Models has improved in recent years. The LiDAR (Light Detection and Ranging) technology, which is based on scanning the terrain using aerial laser telemeters, enables for the quick creation of digital Surface Models (DSM) via simple data interpolation (Comert and Kaplan 2018). In the study, area is initially separated into three slope classes: (a) zero to five degrees, (b) six to ten degrees, and (c) eleven to fifteen degrees.…”

Section: Generation Of Demmentioning

confidence: 99%

Automatic Generation of Dem Using SVM Classifier

Dubey

Bharadwaj

Biswas

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. People are moving towards an automated strategy for building a Digital Elevation Model (DEM) from a high-resolution Google Earth photograph as technology advances. Manually terrestrial measurements are both time and money costly. The automatic creation of DEMs became possible because to recent advances in image matching algorithms and intelligent filtering. The goal of this work is to give an overview of how to create DEMs automatically in the tough situation of a build-up area with the extraction of building points. The calculation of building heights, is to produce a digital elevation model, which may be easily obtained by collecting automatic Lidar point cloud data The estimation of building height with its shadow plays important role in determination. In this detection of shadow and removal of false shadow is also very important. During picture segmentation, shadow detection is performed. Statistical features are used to extract the suspected shadow. Support vector machine on IOOPL matching could effectively remove the shadow for shadow removal. The approach can successfully detect shadows in high-resolution remote sensing photos of cities and can effectively repair shadows at a rate of over 95%. SVM classifier with the accuracy of 92% as compared to others on lower end. The average variation in height estimation comes out to be 1.64 m. First, the building with height more than 20 m and slope more than 3 m, give significantly improved precision in determining building height. Second, the strategy is cost-effective because most RS images from Google Earth are free to use with less knowledge of RS is needed for computation.

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Object Based Building Extraction and Building Period Estimation From Unmanned Aerial Vehicle Data

Cited by 7 publications

References 15 publications

Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

PlanetScope Imagery for Extracting Building Inventory Information

Automatic Generation of Dem Using SVM Classifier

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