SEMI-AUTOMATIC DATA ENRICHMENT FOR OPEN STREET MAP (OSM) USING DEEP LEARNING ALGORITHMS

Halil İbrahim Şenol

Makale detayları

SEMI-AUTOMATIC DATA ENRICHMENT FOR OPEN STREET MAP (OSM) USING DEEP LEARNING ALGORITHMS

Yazarlar:

Halil İbrahim Şenol

Yayıncı:

Mugla Sitki Kocman University

Ana Başlıklar:

Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi:

Mugla Journal of Science and Technology Vol.10 Issue 2, pp.78-87

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336593

pISSN:

2149-3596

Anahtar kelimeler:

Özet:

 It is crucial to obtain continuous data on unplanned urbanization regions in order to develop precise plans for future studies in these regions. An unplanned urbanization area was selected for analysis, and road extraction was performed using very high-resolution unmanned aerial vehicle UAV images. In this regard, the Sat2Graph deep learning model was employed, utilizing the object detection tool integrated within the deep learning package published by ArcGIS Pro software, for the purpose of road extraction from a very high-resolution UAV image. The high-resolution UAV images were subjected to analysis using the photogrammetry method, with the results obtained through the application of the Sat2Graph deep learning model. The resulting road extraction was employed for the purpose of data enhancement on OpenStreetMap OSM. This will facilitate the expeditious and precise implementation of data updates conducted by volunteers. It should be noted that the recall, F1 score, precision ratiouncertainty accuracy, average producer accuracy, and intersection over union of products were automatically extracted with the algorithm and determined to be 0.816, 0.827, 0.838, 0.792, and 0.597, respectively.

 It is crucial to obtain continuous data on unplanned urbanization regions in order to develop precise plans for future studies in these regions. An unplanned urbanization area was selected for analysis, and road extraction was performed using very high-resolution unmanned aerial vehicle UAV images. In this regard, the Sat2Graph deep learning model was employed, utilizing the object detection tool integrated within the deep learning package published by ArcGIS Pro software, for the purpose of road extraction from a very high-resolution UAV image. The high-resolution UAV images were subjected to analysis using the photogrammetry method, with the results obtained through the application of the Sat2Graph deep learning model. The resulting road extraction was employed for the purpose of data enhancement on OpenStreetMap OSM. This will facilitate the expeditious and precise implementation of data updates conducted by volunteers. It should be noted that the recall, F1 score, precision ratiouncertainty accuracy, average producer accuracy, and intersection over union of products were automatically extracted with the algorithm and determined to be 0.816, 0.827, 0.838, 0.792, and 0.597, respectively.

 It is crucial to obtain continuous data on unplanned urbanization regions in order to develop precise plans for future studies in these regions. An unplanned urbanization area was selected for analysis, and road extraction was performed using very high-resolution unmanned aerial vehicle UAV images. In this regard, the Sat2Graph deep learning model was employed, utilizing the object detection tool integrated within the deep learning package published by ArcGIS Pro software, for the purpose of road extraction from a very high-resolution UAV image. The high-resolution UAV images were subjected to analysis using the photogrammetry method, with the results obtained through the application of the Sat2Graph deep learning model. The resulting road extraction was employed for the purpose of data enhancement on OpenStreetMap OSM. This will facilitate the expeditious and precise implementation of data updates conducted by volunteers. It should be noted that the recall, F1 score, precision ratiouncertainty accuracy, average producer accuracy, and intersection over union of products were automatically extracted with the algorithm and determined to be 0.816, 0.827, 0.838, 0.792, and 0.597, respectively.

 Plansız kentleşme bölgeleri hakkında sürekli veri elde etmek, bu bölgelerde gelecekte yapılacak çalışmalar için kesin planlar geliştirmek açısından büyük önem taşımaktadır. Analiz için bir çarpık kentleşme alanı seçilmiş ve çok yüksek çözünürlüklü insansız hava aracı İHA görüntüleri kullanılarak yol çıkarımı yapılmıştır. Bu bağlamda, çok yüksek çözünürlüklü İHA görüntüsünden yol çıkarımı amacıyla ArcGIS Pro yazılımı tarafından yayınlanan derin öğrenme paketine entegre edilmiş nesne tespit aracı kullanılarak Sat2Graph derin öğrenme modeli kullanılmıştır. Yüksek çözünürlüklü İHA görüntüleri, Sat2Graph derin öğrenme modelinin uygulanmasıyla elde edilen sonuçlarla birlikte fotogrametri yöntemi kullanılarak analize tabi tutulmuştur. Elde edilen yol çıkarımı OpenStreetMap OSM üzerinde veri iyileştirme amacıyla kullanılmıştır. Bu, gönüllüler tarafından gerçekleştirilen veri güncellemelerinin hızlı ve hassas bir şekilde uygulanmasını kolaylaştıracaktır. Geri çağırma, F1 puanı, kesinlik oranıbelirsizlik doğruluğu, ortalama üretici doğruluğu ve ürünlerin birleşimi üzerindeki kesişimin algoritma ile otomatik olarak çıkarıldığı ve sırasıyla 0,816, 0,827, 0,838, 0,792 ve 0,597 olarak belirlendiği belirtilmelidir.

Makale detayları

SEMI-AUTOMATIC DATA ENRICHMENT FOR OPEN STREET MAP (OSM) USING DEEP LEARNING ALGORITHMS

Yazarlar:

Halil İbrahim Şenol

Yayıncı:

Mugla Sitki Kocman University

Ana Başlıklar:

Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi:

Mugla Journal of Science and Technology Vol.10 Issue 2, pp.78-87

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336593

pISSN:

2149-3596

GIS

OpenStreetMap

Deep learning

Data enrichment

PhotogrammetryCBS

OpenStreetMap

Derin öğrenme

Veri zenginleştirme

Fotogrametri

Sayı

Makale detayları

Yazarlar: Halil İbrahim Şenol

Yayıncı: Mugla Sitki Kocman University

Ana Başlıklar: Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi: Mugla Journal of Science and Technology Vol.10 Issue 2, pp.78-87

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001336593

pISSN: 2149-3596

Sayı

Yazarlar:

Halil İbrahim Şenol

Yayıncı:

Mugla Sitki Kocman University

Ana Başlıklar:

Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Dergi:

Mugla Journal of Science and Technology Vol.10 Issue 2, pp.78-87

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336593

pISSN:

2149-3596