Arsitektur U-Net Untuk Mendeteksi Titik Api Kebakaran Hutan Dan Lahan Di Kalimantan Tengah Menggunakan Satelit Himawari 8
DOI:
https://doi.org/10.30996/konv.v21i1.12030Keywords:
U-Net, Deteksi Titik Api, Kebakaran Hutan, Satelit Himawari 8, Kalimantan TengahAbstract
ABSTRACT
Early detection of fire hotspots is crucial to prevent the spread of fires and to mitigate the resulting environmental impacts. This study proposes the use of the U-Net model for detecting fire hotspots on Himawari 8 satellite imagery, specifically utilizing Band 7, which is sensitive to thermal radiation. Fire hotspot data from MODIS and VIIRS were employed as the ground truth to validate the model predictions. The U-Net model was trained on satellite imagery with binary crossentropy as the loss function and was evaluated using Area Under Curve (AUC), Percentage Correct (PC), Omission Error (OE), and Commission Error (CE) as evaluation metrics. The training results demonstrate that the model is capable of detecting fire hotspots with high accuracy, achieving an AUC of over 0.85 on the validation data after 300 epochs. Moreover, PC increased and OE decreased, indicating an improvement in the model's ability to accurately detect fire hotspots. In conclusion, the U-Net model developed in this study has shown good performance in detecting fire hotspots.
Keywords: U-Net, Detecting Fire Hotspots, Forest Fires, Himawari 8 Satellite, Central Kalimantan.
ABSTRAK
Deteksi dini titik api menjadi sangat penting untuk mencegah penyebaran kebakaran yang lebih luas dan mengurangi dampak lingkungan yang ditimbulkan. Penelitian ini mengusulkan penggunaan model U-Net untuk mendeteksi titik api pada citra satelit Himawari 8, khususnya dengan menggunakan Band 7, yang sensitif terhadap radiasi termal. Data titik api dari MODIS dan VIIRS digunakan sebagai ground truth untuk memvalidasi prediksi model. Model U-Net dilatih menggunakan citra satelit dengan metric binary crossentropy sebagai fungsi loss, dan dievaluasi menggunakan metrik Area Under Curve (AUC), Percentage Correct (PC), Omission Error (OE), dan Commission Error (CE). Hasil pelatihan menunjukkan bahwa model dapat mendeteksi titik api dengan akurasi yang tinggi, dengan AUC mencapai lebih dari 0.85 pada data validasi setelah 300 epoch. Selain itu, PC meningkat dan OE menurun, yang menandakan peningkatan kemampuan model dalam mendeteksi titik api secara akurat. Kesimpulannya, model U-Net yang dikembangkan dalam penelitian ini mampu mendeteksi titik api dengan performa yang baik.
Kata Kunci: U-Net, Deteksi Titik Api, Kebakaran Hutan, Satelit Himawari 8, Kalimantan Tengah.
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