Earthquake damage detection with satellite imagery and deep learning approaches: A case study of the february 2023, Kahramanmaraş, Turkey earthquake sequence

Abstract

In recent years, the fusion of deep learning techniques, remote sensing technology, and artificial intelligence (AI) has profoundly transformed the field of disaster management and damage assessment. The increased availability of high-resolution satellite imagery and advanced computer vision techniques now makes it possible to analyze Earth observation data at a large scale and with unparalleled precision. This thesis investigates the application of remote sensing and deep learning techniques to perform post-earthquake damage classification using computer vision and focuses specifically on the earthquakes that occurred on February 6th, with an emphasis on Kahramanmaraş province. The objective of this thesis is to investigate the potential of a variety of deep learning techniques, evaluate their accuracy in recognizing structurally compromised buildings, and utilize satellite imagery in conjunction with diverse open-source spatial data to enhance research on earthquakes. This master's thesis specifically delves into the integration of remote sensing, computer vision, and earth observation methods within the field of geophysics and earthquake studies. Thus, in this study it is aimed to showcase the application of computer vision in the analysis of post-earthquake damage and underscore the importance of rapid intervention in such critical situations. The thesis places significant emphasis on the use of satellite imagery and pixel-based classification for the classification of images in earthquake damage assessment. The UNet, DeepLabV3, and PSPNet architectures are implemented using the ArcGIS Pro API for Python, an innovative and supportive tool for scientific research. The primary data source for the investigation is RGB images from Maxar Technologies. The research examines three cities that were affected by the February 6, 2023, Kahramanmaraş earthquake sequences: Kahramanmaraş, Hatay, and Gaziantep. Damage-assessed data points are received thanks to Yer Çizenler Non-Governmental Organization (NGO), and recently modified building footprints are taken from Humanitarian OpenStreetMap (HOTOSM), and they are all used to analyze the damage. Labeled polygons are generated within a 5-meter distance of the damage points. However, assigning values for further and closer distances has a negative impact on the model accuracy. The training data, exported based on the satellite imagery and damage level assigned data points, provides a balanced dataset for Kahramanmaraş, where the building footprints match the images most effectively. In Hatay, the damage level assigned data distribution is the most balanced, but the building footprints do not align well with the images. Gaziantep presents a good match between the building footprints and images, but the distribution of the damaged data classes is highly imbalanced. Consequently, the decision is made to focus on training the model for Kahramanmaraş province due to the similarity in roof and building types, which has the potential to adapt the approach to other cities in the region as well as the earthquake-affected region under investigation. Image sizes of 256x256 pixels with 128 strides and 4 batches gave us the optimum model results among other options in the DeepLabV3 ResNet50 encoder. In conclusion, this master's thesis demonstrates the potential of combining remote sensing, computer vision, and earth observation techniques for geophysics and earthquake studies. Also, it is aimed to use different data types from open sources and use these different data types to make damage detection after earthquakes. The utilization of the ArcGIS Pro Python API, satellite imagery, pixel based classsification, and labeled training data provides insights into damage assessment after earthquakes, with Kahramanmaraş Province serving as the focal point for model training. The findings contribute to the development of efficient and accurate disaster management strategies and lay the foundation for further research in this field.