LEE- Uydu Haberleşme ve Uzaktan Algılama-Yüksek Lisans
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Konu "deep learning" ile LEE- Uydu Haberleşme ve Uzaktan Algılama-Yüksek Lisans'a göz atma
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ÖgeEarthquake damage detection with satellite imagery and deep learning approaches: A case study of the february 2023, Kahramanmaraş, Turkey earthquake sequence(Graduate School, 2023-08-14) Elik, Fatma ; Sertel, Elif ; 705201004 ; Satellite Communication and Remote SensingIn 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.
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ÖgeVessel detection from very high-resolution satellite images with deep learning methods(Graduate School, 2022-06-22) Büyükkanber, Furkan ; Yanalak, Mustafa ; 705181008 ; Satellite Communication and Remote SensingVessel detection from remote sensing images is becoming increasingly important component in marine surveillance applications such as maritime traffic control, anti-illegal fishing applications, oil discharge control, marine pollution and marine safety. Increasingly, very high and medium resolution (VHR and MR) earth observation satellites both significantly increase the detectability of many terrestrial objects and shorten recurring visit times in orbit like never before, making the use of this technology attractive for a variety of maritime monitoring missions. However, the difficulty and complexity of object detection in huge satellite images that cover hundreds of square kilometers and derive results under near real-time constraints cause traditional methods to face many difficulties when processing satellite images of this size. Processing these images and applying them to deep learning methods makes it possible to minimize unforeseen errors that can be made by analysts, and to save labor, time and cost. In order to create the artificial neural network and make it successful by determining the deep learning method, it is necessary to train using as much as possible examples of the objects targeted to be detected. By using the designed convolutional neural networks, it is possible to detect more than one object in a given test input image and perform change analysis as well. The weights are updated in each layer for the input image processed in the multilayer convolutional neural network, and the error rate is found by looking at the difference between the detected value and the actual ground truth value. Many vessels for commercial, military and civil purposes are observed in international maritime areas, usually in areas close to ports and coasts. High resolution satellite images, which provide wide field of view and altitude monitoring, are very useful for vessel detection. Vessel detection from satellite images plays a significant role for inspecting maritime areas, controlling maritime transport traffic and applications for defense purposes. Open source datasets are widely used in object detection applications, since it takes a substantial amount of time and cost to build a dataset for object recognition and detection from satellite images. Within the scope of this thesis, models developed using convolutional neural networks including single-stage and two-stage deep learning methods were used by applying our own dataset images that we build with the open source DOTA dataset selected for vessel detection. For the purposes of the experiments in this research, three separate datasets were built. All the images were labelled with YOLO annotation format, then in accordance of use for various models, they have been converted to COCO and Pascal VOC annotation format. Both inshore and offshore vessel images have been collected with having wide variety of scales, shapes, orientations and weather conditions (fuzzy, cloudy, sunny, etc.). Experiments were performed by using Faster R-CNN, YOLOv3, YOLOv5 and YOLOX deep learning models on all three different datasets. Any dataset containing various examples of the target object considerably improves the accuracy of outcomes in deep learning applications by implementing various data augmentation techniques, such as mosaic, mixup, and rotating images, are utilized for remote sensing. In some experiments, more than one augmentation approach is being used simultaneously to improve the accuracy of the results. Not all data augmentation approaches had the same effect on the experiment outcomes. As a result, there is no logical answer to the question of which data augmentation strategy is the most effective. The outcomes of the studies were compared using the mean average precision metric (mAP), and the YOLOv5 model achieved on top results. All of the experiments have yielded the same result: raising the depth of the network by increasing the size of the input images. mAP value results improved as the input sizes were increased, however this caused the selected models longer to train. Experiments in deep learning studies are made easier by machines that have powerful graphics cards. Faster R-CNN, YOLOv3, YOLOv5 and YOLOX model trainings were conducted on a local machine workstation equipped with NVIDIA GeForce RTX 2080Ti graphics card and Intel® Core™ i9-9900K 3.60 GHz CPU processor. Deep learning applications were carried out using Python programming language and PyTorch framework deep learning library.