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Articles

Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7

  • 1* Universitas Pendidikan Indonesia

* Corresponding Author: Rangga Gelar Guntara

Open Access
DOI
https://doi.org/10.59431/jmasif.v2i1.156
Published
Pages
9-18

Issue

Vol. 2 No. 1 (2023)

Jurnal Manajemen Sistem Informasi (JMASIF)
April 2023

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.

Keywords

Aerial imagery Deep learning Google colab Roof detection YOLOV7

How to Cite

Gelar Guntara, R. (2023). Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7. Jurnal Manajemen Sistem Informasi (JMASIF), 2(1), 9-18. https://doi.org/10.59431/jmasif.v2i1.156

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Author Biographies

  • Rangga Gelar Guntara, Universitas Pendidikan Indonesia

    Program Studi Bisnis Digital

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