Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery
Natural disasters continuously threaten populations worldwide, with hydrometeorological events standing out due to their unpredictability, rapid onset, and significant destructive capacity. However, developing countries often face severe budgetary constraints and rely heavily on international suppor...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/7/1041 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850213245104685056 |
|---|---|
| author | César Luis Moreno González Germán A. Montoya Carlos Lozano Garzón |
| author_facet | César Luis Moreno González Germán A. Montoya Carlos Lozano Garzón |
| author_sort | César Luis Moreno González |
| collection | DOAJ |
| description | Natural disasters continuously threaten populations worldwide, with hydrometeorological events standing out due to their unpredictability, rapid onset, and significant destructive capacity. However, developing countries often face severe budgetary constraints and rely heavily on international support, limiting their ability to implement optimal disaster response strategies. This study addresses these challenges by developing and implementing YOLOv8-based deep learning models trained on high-resolution satellite imagery from the Maxar GeoEye-1 satellite. Unlike prior studies, we introduce a manually labeled dataset, consisting of 1400 undamaged and 1200 damaged buildings, derived from pre- and post-Hurricane Maria imagery. This dataset has been publicly released, providing a benchmark for future disaster assessment research. Additionally, we conduct a systematic evaluation of optimization strategies, comparing SGD with momentum, RMSProp, Adam, AdaMax, NAdam, and AdamW. Our results demonstrate that SGD with momentum outperforms Adam-based optimizers in training stability, convergence speed, and reliability across higher confidence thresholds, leading to more robust and consistent disaster damage predictions. To enhance usability, we propose deploying the trained model via a REST API, enabling real-time damage assessment with minimal computational resources, making it a low-cost, scalable tool for government agencies and humanitarian organizations. These findings contribute to machine learning-based disaster response, offering an efficient, cost-effective framework for large-scale damage assessment and reinforcing the importance of model selection, hyperparameter tuning, and optimization functions in critical real-world applications. |
| format | Article |
| id | doaj-art-7e497641a37643b397e002e43aebe632 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-7e497641a37643b397e002e43aebe6322025-08-20T02:09:11ZengMDPI AGMathematics2227-73902025-03-01137104110.3390/math13071041Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite ImageryCésar Luis Moreno González0Germán A. Montoya1Carlos Lozano Garzón2Systems and Computing Engineering Department, Universidad de los Andes, Bogotá 111711, ColombiaSystems and Computing Engineering Department, Universidad de los Andes, Bogotá 111711, ColombiaSystems and Computing Engineering Department, Universidad de los Andes, Bogotá 111711, ColombiaNatural disasters continuously threaten populations worldwide, with hydrometeorological events standing out due to their unpredictability, rapid onset, and significant destructive capacity. However, developing countries often face severe budgetary constraints and rely heavily on international support, limiting their ability to implement optimal disaster response strategies. This study addresses these challenges by developing and implementing YOLOv8-based deep learning models trained on high-resolution satellite imagery from the Maxar GeoEye-1 satellite. Unlike prior studies, we introduce a manually labeled dataset, consisting of 1400 undamaged and 1200 damaged buildings, derived from pre- and post-Hurricane Maria imagery. This dataset has been publicly released, providing a benchmark for future disaster assessment research. Additionally, we conduct a systematic evaluation of optimization strategies, comparing SGD with momentum, RMSProp, Adam, AdaMax, NAdam, and AdamW. Our results demonstrate that SGD with momentum outperforms Adam-based optimizers in training stability, convergence speed, and reliability across higher confidence thresholds, leading to more robust and consistent disaster damage predictions. To enhance usability, we propose deploying the trained model via a REST API, enabling real-time damage assessment with minimal computational resources, making it a low-cost, scalable tool for government agencies and humanitarian organizations. These findings contribute to machine learning-based disaster response, offering an efficient, cost-effective framework for large-scale damage assessment and reinforcing the importance of model selection, hyperparameter tuning, and optimization functions in critical real-world applications.https://www.mdpi.com/2227-7390/13/7/1041machine learningdeep learningcomputer visiondetection modelsnatural disastershydrometeorological disasters |
| spellingShingle | César Luis Moreno González Germán A. Montoya Carlos Lozano Garzón Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery Mathematics machine learning deep learning computer vision detection models natural disasters hydrometeorological disasters |
| title | Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery |
| title_full | Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery |
| title_fullStr | Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery |
| title_full_unstemmed | Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery |
| title_short | Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery |
| title_sort | toward reliable post disaster assessment advancing building damage detection using you only look once convolutional neural network and satellite imagery |
| topic | machine learning deep learning computer vision detection models natural disasters hydrometeorological disasters |
| url | https://www.mdpi.com/2227-7390/13/7/1041 |
| work_keys_str_mv | AT cesarluismorenogonzalez towardreliablepostdisasterassessmentadvancingbuildingdamagedetectionusingyouonlylookonceconvolutionalneuralnetworkandsatelliteimagery AT germanamontoya towardreliablepostdisasterassessmentadvancingbuildingdamagedetectionusingyouonlylookonceconvolutionalneuralnetworkandsatelliteimagery AT carloslozanogarzon towardreliablepostdisasterassessmentadvancingbuildingdamagedetectionusingyouonlylookonceconvolutionalneuralnetworkandsatelliteimagery |