Penggunaan Deep Learning dan Post-Processing Algoritma Douglas-Peucker untuk Ekstraksi Jaringan Jalan pada Area Urban dari Orthophoto
DOI:
https://doi.org/10.12962/geoid.v19i2.1127Keywords:
Deep Learning, Ekstraksi Objek, Orthophoto, Peta Skala Besar, Algoritma Douglas-PeuckerAbstract
Peta dasar skala besar sangat dibutuhkan oleh kota besar/metropolitan seperti Kota Surabaya untuk perencanaan kota dan menunjang pembangunan kota cerdas. Beberapa informasi utama yang paling dibutuhkan dari peta skala besar adalah fitur bangunan dan jaringan jalan. Ekstraksi jaringan jalan merupakan pekerjaan yang menantang karena banyak alasan, termasuk sifat heterogen dari geometri dan spektral, kompleksitas objek yang sulit dimodelkan, dan data sensor yang kurang baik. Intepretasi yang dilakukan oleh operator secara visual masih merupakan pendekatan yang umum digunakan untuk ekstraksi informasi dari orthophoto. Akurasi intepretasi yang dihasilkan tergantung pada keterampilan dan pengalaman dari operator. Sehingga, dapat terjadi inkonsistensi pada data yang dihasilkan oleh operator yang berbeda. Beberapa tahun terakhir ini, ekstraksi otomatis jalan dari orthophoto maupun CSRT menjadi isu penelitian penting dan menantang yang mendapat perhatian lebih besar. Dalam penelitian ini, penulis menerapkan metode deteksi objek berbasis Mask Region-based Convolutional Neural Network (Mask R-CNN) untuk ekstraksi jaringan jalan memanfaatkan orthophoto dan DSM LiDAR di daerah urban Kota Surabaya. Beberapa strategi dirancang dan digabungkan dengan model deteksi objek berbasis Mask R-CNN, termasuk post-processing yang terdiri dari regularisasi poligon algoritma Douglass-Peucker, remove overlap, fill gap, dan penghalusan poligon. Metode yang penulis terapkan menghasilkan kinerja yang cukup baik untuk ekstraksi jalan menghasilkan nilai presisi 90,28%; kelengkapan (recall) 85,85%; skor-F1 88,01%; dan IoU 78,59%; serta overall accuracy 95,25 % dan nilai kappa 90,5%.
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