Segmentation of Buildings Using U-Net Model from Göktürk-1 Satellite Images

Year 2023, Volume: 5 Issue: 1, 50 - 58, 30.06.2023

Duygu Arıkan , Ferruh Yıldız

https://doi.org/10.51489/tuzal.1300939

Abstract

The increase in population has led to unplanned urbanization in urban areas, becoming a global issue. The identification and detection of these areas are of great importance for urban management and redevelopment planning. However, these processes can be costly and time-consuming when conducted on-site. Automatic detection and characterization of unplanned buildings in urban and rural areas using remote sensing imagery is a challenging task. Recently, with the advancements in deep learning methods, the detection of complex buildings has become possible. In this study, the building extraction process of a region from the Etimesgut district of Ankara was performed using the U-Net deep learning architecture. The Inria Aerial Image Labeling dataset, a publicly available dataset, was used for the process. Different numbers of images (500, 1000, 2500, 5000) were selected for the training process. The best learning outcome was tested with Göktürk-1 satellite imagery with a spatial resolution of 0.5 m. According to the results, the U-Net model achieved a Jaccard coefficient of 0.862 and a Dice similarity coefficient of 0.813 for building segmentation.The effectiveness and potential of deep learning methods were demonstrated using the U-Net model with the available dataset. This study showcased the efficiency and potential of deep learning methods in the detection and mapping of buildings in urban areas.

Keywords

Deep Learning, U-Net Model, Gokturk-1 Satellite Image, Building Detection

Göktürk-1 Uydu Görüntülerinden U-Net Modeli Kullanılarak Binaların Segmentasyonu

Abstract

Nüfus artışı, kentsel bölgelerde plansız yapılaşmanın ortaya çıkmasına yol açmaktadır. Bu durum dünya genelinde bir sorun haline gelmiştir. Bu alanların belirlenmesi ve tespit edilmesi, kentsel yönetim ve yeniden yapılanma planlaması için büyük öneme sahiptir. Ancak bu işlemler, arazide maliyetli ve zaman alıcı olabilmektedir. Uzaktan algılama görüntüleri kullanarak kentsel ve kırsal bölgelerde plansız yapılan binaları otomatik olarak tespit etmek ve karakterize etmek oldukça zordur. Son zamanlarda, derin öğrenme yöntemleri sayesinde karmaşık binaların tespiti mümkün hale gelmiştir. Bu çalışmada, Ankara'nın Etimesgut ilçesinden bir bölgenin bina çıkarımı işlemi, U-Net derin öğrenme mimarisi kullanılarak gerçekleştirilmiştir. İşlem için Inria Aerial Image Labeling adlı hazır bir veri seti kullanılmıştır. Eğitim işlemi için farklı sayıda görüntü (500, 1000, 2500, 5000) seçilmiştir. En iyi öğrenme sonucu, 0.5 m uzamsal çözünürlüğe sahip Göktürk-1 uydu görüntüleriyle test edilmiştir. Sonuçlara göre, U-Net modelinin bina segmentasyonunda Jaccard katsayısı 0.862, Dice benzerlik oranı 0.813 olarak bulunmuştur. Hazır veri seti kullanılarak U-Net modelinin derin öğrenme yöntemleri için kullanılabilir olduğu kanıtlanmıştır. Bu çalışma, kentsel alanlardaki binaların tespiti ve haritalanmasında derin öğrenme yöntemlerinin etkinliğini ve potansiyelini göstermiştir.

Keywords

Derin Öğrenme, U-Net mimarisi, Göktürk-1 Uydu Görüntüsü, Bina çıkarımı

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