Derin öğrenme yöntemleri ve uydu görüntü verileri kullanılarak deprem sonrası ağır hasarlı alanların tespiti: Kahramanmaraş örneği
Using deep learning methods and satellite imagery identifying heavily damaged areas after an earthquake: The case of Kahramanmaraş
Yazar: EMİNE SARIALİOĞLU
Danışman: PROF. DR. ŞİNASİ KAYA
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Geomatik Mühendisliği Ana Bilim Dalı / Geomatik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry

Özet:
Doğal afetler insan yaşamı için oldukça ciddi ve öngörülemez sonuçlara yol açar.
Türkiye diri faylar nedeniyle doğal afetlerden biri olan depreme maruz kalan bir
ülkedir. Son yıllarda artan teknoloji ile birlikte uydu görüntüleri, afet öncesi, afet
sırasında ve afet sonrasında afet yönetim süreçlerinde oldukça aktif olarak
kullanılmaktadır. Derin öğrenme makine öğrenmesinin bir alt dalıdır ve son yıllarda
uydu görüntüleri ile yapılan çalışmalarda aktif olarak kullanılmaktadır.
Bu çalışma kapsamında 6 Şubat 2023 tarihinde meydana gelen depremde ağır hasar
gören Kahramanmaraş il merkezine ait deprem sonrası uydu görüntüleri kullanılarak,
derin öğrenmenin uygulama alanlarından biri olan nesne tabanlı sınıflandırma yöntemi
kullanılmıştır. Afet bölgesine ait uydu görüntülerinden hasarlı ve hasarsız binalardan
etiketli veri seti üretilmiştir. Dengesiz veri seti problemini çözmek için veri setine veri
arttırma işlemi yapılmıştır.
Çalışma kapmasında ilk olarak modelin eğitilmesi süreci yapılmıştır. Bunun için
ArcGIS Pro 3.1 yazılımının Görüntü Analiz aracına bağlı Derin Öğrenme aracı
kullanılmıştır. Model eğitilme sürecinde transfer öğrenme Restnet-32 derin öğrenme
mimarisi ve işlemci tipi olarak GPU kullanılmıştır. Modelin eğitilme süresi yaklaşık 2
saattir. Modelin eğitilmesi sonucunda modelin genel kesinlik değeri yaklaşık olarak
0.96, hassasiyeti yaklaşık olarak 0.95, duyarlılığı 0.93, F1 skoru 0,94 olarak elde
edilmiştir. ArcGIS Pro yazılımı üzerinden Nesne Sınıflandırma için Derin Öğrenme
aracı ve elde edilen model kullanılarak Kahramanmaraş'a ait uydu görüntüleri
sınıflandırılmıştır.
Elde edilen model Kahramanmaraş'a ait 224x224x4 boyutlarındaki parçalara bölünmüş
uydu görüntülerine hasarlı alan ya da hasarsız alan olarak ikili sınıflandırma tahmini
yapılarak sınıflandırma sonuçları elde edilen güven aralığı skorlarına göre
değerlendirilmiştir. Güven aralığı eşik değeri olarak 0.5 kabul edildi.
İkinci derin öğrenme yöntem de ise aynı veri seti kullanılmıştır. Python
kütüphanelerinden tensorflow ve keras kütüphaneleri ile CNN (Convolutional Neural
Networks) derin öğrenme mimarisi kullanılmıştır. Model Kaggle ortamında
eğitilmiştir. İşlemci tipi GPU P100'dür ve eğitim süresi yaklaşık 2 saattir. Model
eğitilmesi sonucunda hata matrisi kullanılarak model performans metrikleri
hesaplanmıştır. Genel hassasiyet yaklaşık olarak 0.90, doğruluk 0.95, duyarlılık 0,96
ve, F1 skora 0.93 olarak bulunmuştur. Elde edilen model sınıflandırma için yeterli
performansa sahiptir. Fakat model her zaman geliştirilmeye açıktır. Roc (Receiver
Operating Characteristic Curve) eğrisi kullanılarak sınıflandırmada kullanılacak
modelin threshold değeri 0.6 olarak hesaplanmıştır. Model kullanılarak
Kahramanmaraş'a ait uydu görüntülerinde sınıflandırma işlemi yapılmıştır.
İki farklı yöntemle elde edilen sınıflandırma sonuçları karşılaştırılmıştır. İki farklı
yöntem ile elde edilen sınıflandırma sonuçları kendi içinde karşılaştırılmıştır. Bu
çalışma, her iki derin öğrenme yöntemi modeli ve yüksek çözünürlüklü uydu
görüntüleri kullanılarak elde edilen sınıflandırma sonuçlarının deprem sonrası ağır
hasarlı alanların tespitinde kullanılabileceğini göstermiştir.

Abstract:
Natural disasters have a serious and unpredictable impact on the lives of human beings.
Turkey is a country that is exposed to earthquakes, one of the natural disasters, due to
its active faults. In terms of disaster management, rapid access and intervention in the
region after the earthquake is important. The processes of planning, coordination and
implementation in order to reduce the effects of natural disasters, improve societies,
save and protect human life are disaster management. The process of collecting
information by remotely observing the objects on the earth is called remote sensing.
Many techniques such as satellite imaging, thermal imaging, radar technology and the
use of sensors are used to obtain remote images. As technology has advanced in recent
years, remote imagery has been used actively in disaster management processes
before, during and after disasters.
Deep learning is a subset of machine learning. It works in a similar way to the human
brain. It is the use of at least one artificial neural network and a large number of
algorithms in order to obtain new data from the existing data. Convolutional neural
networks, a sub-branch of deep learning, are a powerful tool for image processing,
classification and segmentation. It has been actively applied to satellite imagery studies
in recent years.
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In this study, the object-based classification method, which is one of the application
areas of deep learning, was used by using the post-earthquake satellite images of the
city centre of Kahramanmaraş, which was severely damaged in the earthquake that
occurred on 6 February 2023. A labelled dataset of damaged and undamaged buildings
was created from satellite images of the area. Data augmentation was applied to the
small sample dataset to overcome the problem of an unbalanced dataset. Several
operations were applied to enhance the data, including horizontal and vertical image
rotation, brightness enhancement, and band combination diversification.
The first method in the study was to train the model. This was done using the deep
learning tool linked to the Image Analysis tool of the ArcGIS Pro 3.1 software.
Transfer learning Restnet-34 deep learning architecture was used in the model training
process. Transfer learning speeds up training time. It allows better generalisation and
can better identify under-represented classes. GPU was used as the processor type and
the model training time was approximately 2 hours. As a result of training the model,
the overall accuracy value of the model was approximately 0.96, precision was
approximately 0.95, sensitivity was 0.93 and F1 score was 0.94. Model performance
metrics were also evaluated within the two classes of the model. The satellite images
of Kahramanmaras were classified using the Deep Learning for Object Classification
tool, and the model was obtained using the ArcGIS Pro software. The obtained model
was binary classified as damaged area or undamaged area in 224x224x4 satellite
images of Kahramanmaraş. The classification results were evaluated according to the
confidence interval value. The confidence interval value determined by the tool was
0.5
In the second-deep learning method, the same data set was used. Using the Tensorflow
and Keras libraries from the Python library, the CNN (Convolutional Neural
Networks) deep learning architecture was used. The image dataset to be used was
normalised and resized before training the model. The new image data set obtained
was divided as 20% test data set and 80% training data set. The model input shape was
set to (256x256x4). This represents an image with (256x256) dimensions and 4 bands.
The model activation function was chosen as a 2-neuron Sigmoid function. Sigmoid
function shows good performance especially in binary classification problems. The
model loss function is chosen as binary_crossentropy and the optimiser is chosen as
"adam".
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The Kaggle environment is used to train the model. The processor type is GPU P100
and the training time is about 2 hours. As a result of the model training, the model
performance metrics were calculated by means of the error matrix. The overall
precision was found to be around 0.90, the accuracy was 0.95, the sensitivity was 0.96
and the F1 score was 0.93. The model has sufficient accuracy for solving the
classification problem, but is always open to improvement. The sigmoid function
provides a confidence interval value for each data point, which is the preferred model
activation function. This confidence interval is between 0-1. The threshold value is
generally accepted to be 0.5. However, the threshold can be calculated for the model
using various methods. The threshold value was calculated by means of the ROC curve
and the threshold value was found to be 0,6. In order to, perform classification using
the model the images to be classified by the model must have the same dimensions as
the input shape parameter of the model. For this reason, the satellite images of
Kahramanmaraş, which are to be classified, have been divided into 256 x 256 images
with 4 bands with the. tif file extension. The image data set to be classified to be
obtained was classified in Kaggle environment. A total of 4802 tiles of the same area
were the subject of the classification process. The classification results obtained by
both methods were evaluated in ArcGIS Pro 3.1.
Both models have similar difficulties with classification results The artificial pixels
created by the models for the input dimensions caused misclassification in the corners
of the image in both models. At the same time there are misclassification results in
areas such as empty land, forest and road in both models. On the other hand, both
models have advantages and disadvantages over each other, but the models perform
well in the classification. The results showed that high-resolution satellite imagery and
deep learning can analyse severely damaged areas after an earthquake.