Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods
Developments in Geographic Information Systems and Remote Sensing (RS) technologies and innovative approaches emerging in deep learning (DL) supported analysis methods have an important place in disaster research as in every field. Convolutional neural networks such as Mask RCNN, U-NET, one of the d...
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| Format: | Article |
| Language: | English |
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Hasan Eleroğlu
2025-03-01
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| Series: | Turkish Journal of Agriculture: Food Science and Technology |
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| Online Access: | https://agrifoodscience.com/index.php/TURJAF/article/view/7474 |
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| author | Aşır Yüksel Kaya |
| author_facet | Aşır Yüksel Kaya |
| author_sort | Aşır Yüksel Kaya |
| collection | DOAJ |
| description | Developments in Geographic Information Systems and Remote Sensing (RS) technologies and innovative approaches emerging in deep learning (DL) supported analysis methods have an important place in disaster research as in every field. Convolutional neural networks such as Mask RCNN, U-NET, one of the deep learning methods for disaster damage impact assessment and classification, have started to show successful results. However, high-resolution geospatial imagery and drones provide faster and more accurate detection of structural damage. In this study, damaged building detection was performed using Göktürk-1 satellite images from 6 February 2023 using Mask-RCNN architecture. In this study, deep learning methods were used to detect the collapsed buildings in the city of Malatya during the 6 February 2023 earthquakes. The study aims to emphasize the significance of GIS and remote sensing for the timely and efficient evaluation of building damage after a disaster. Considering this, high quality images of Malatya city before and after the earthquake were analyzed and data sets were created by masking using Mask RCNN deep learning method through ArcGIS Pro 3.4.0 software. According to the results of the research, it quickly detected damaged buildings with an accuracy rate of 70% according to satellite images after the earthquake. As a result, GIS and deep learning models were used to detect and map the initial damage after the earthquake. |
| format | Article |
| id | doaj-art-eec7447637c04ed69c19fbdb83707c42 |
| institution | OA Journals |
| issn | 2148-127X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Hasan Eleroğlu |
| record_format | Article |
| series | Turkish Journal of Agriculture: Food Science and Technology |
| spelling | doaj-art-eec7447637c04ed69c19fbdb83707c422025-08-20T02:22:25ZengHasan EleroğluTurkish Journal of Agriculture: Food Science and Technology2148-127X2025-03-0113368869610.24925/turjaf.v13i3.688-696.74746175Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing MethodsAşır Yüksel Kaya0https://orcid.org/0000-0003-0398-7069Firat University, Faculty of Human and Social Sciences, Department of Geography, 23119, Elazig, Türkiye Developments in Geographic Information Systems and Remote Sensing (RS) technologies and innovative approaches emerging in deep learning (DL) supported analysis methods have an important place in disaster research as in every field. Convolutional neural networks such as Mask RCNN, U-NET, one of the deep learning methods for disaster damage impact assessment and classification, have started to show successful results. However, high-resolution geospatial imagery and drones provide faster and more accurate detection of structural damage. In this study, damaged building detection was performed using Göktürk-1 satellite images from 6 February 2023 using Mask-RCNN architecture. In this study, deep learning methods were used to detect the collapsed buildings in the city of Malatya during the 6 February 2023 earthquakes. The study aims to emphasize the significance of GIS and remote sensing for the timely and efficient evaluation of building damage after a disaster. Considering this, high quality images of Malatya city before and after the earthquake were analyzed and data sets were created by masking using Mask RCNN deep learning method through ArcGIS Pro 3.4.0 software. According to the results of the research, it quickly detected damaged buildings with an accuracy rate of 70% according to satellite images after the earthquake. As a result, GIS and deep learning models were used to detect and map the initial damage after the earthquake.https://agrifoodscience.com/index.php/TURJAF/article/view/7474gisremote sensingdeep learningmask r-cnnsatellite imagesmalatya |
| spellingShingle | Aşır Yüksel Kaya Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods Turkish Journal of Agriculture: Food Science and Technology gis remote sensing deep learning mask r-cnn satellite images malatya |
| title | Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods |
| title_full | Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods |
| title_fullStr | Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods |
| title_full_unstemmed | Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods |
| title_short | Detection of Structural Damage After an Earthquake Using GIS and Remote Sensing Methods |
| title_sort | detection of structural damage after an earthquake using gis and remote sensing methods |
| topic | gis remote sensing deep learning mask r-cnn satellite images malatya |
| url | https://agrifoodscience.com/index.php/TURJAF/article/view/7474 |
| work_keys_str_mv | AT asıryukselkaya detectionofstructuraldamageafteranearthquakeusinggisandremotesensingmethods |