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Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7 Gelar Guntara, Rangga
Jurnal Manajemen Sistem Informasi (JMASIF) Vol. 2 No. 1 (2023): April 2023
Publisher : Divisi Riset, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/jmasif.v2i1.156

Abstract

This article presents a deep learning-based approach for roof building detection in aerial imagery using the Yolov7 algorithm and Google Colab. We trained the Yolov7 model on a dataset of annotated aerial images and tested its performance on a testing set of 200 images. Our experimental results show that the Yolov7 model achieved an F1 score of 80% for roof building detection on the testing set, indicating that our approach can accurately detect roof buildings in aerial imagery. This approach can be useful in various applications, such as urban planning, disaster management, and infrastructure development. The availability of aerial imagery and the ability to use deep learning algorithms make it possible to automate the process of roof building detection, which can save time and resources. This research can be extended by investigating the use of other deep learning algorithms or combining multiple algorithms to improve the accuracy of roof building detection in aerial imagery.

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