Today, solar energy has become an indispensable element in providing energy infrastructure. Estimating the solar energy potential of building roofs in residential areas is important for the effective use of this energy. Nowadays, thanks to the developments in artificial intelligence algorithms, these tasks are performed automatically by computers. In this study, a solution is proposed by using deep learning architectures, which are the most advanced artificial intelligence algorithms. Convolutional Neural Network model called Roof-KSA with less parameters was proposed for semantic segmentation of building roofs in this research. A total of 3400 satellite images in 224×224×3 pixels size were used for semantic segmentation. Roof-CNN model has a total of 10 layers and 104,450 updated parameters. Within the scope of comparative analysis, Roof-CNN model has less parameters compared to U-Net models. In addition, Roof-KSA model stands out with 0.91404 global accuracy, 0.73092 mean accuracy, 0.65537 mean intersection over union, 0.84918 weighted intersection over union and 0.67244 mean BF score. As a result, it is seen that Roof-CNN model is more successful in accordance with obtained semantic segmentation results.
: May 23, 2020
|APA||Özkaya, U , Öztürk, Ş . (2020). Roof-KSA: Binaların Semantik Bölütlemesi İçin Az Parametreye Sahip Konvolüsyonel Sinir Ağı Modeli . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 7 (2) , 1094-1105 . DOI: 10.35193/bseufbd.741729|