An adaptive iterative reweighted filtering methodology for urban MLS dataset

Süleymanoğlu B., Soycan A., Soycan M.

Journal of Spatial Science, cilt.0, ss.1-21, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 0
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/14498596.2024.2350588
  • Dergi Adı: Journal of Spatial Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1-21
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

This study presents a novel filtering methodology for Mobile LaserScanning (MLS) data using robust iterative reweighting. Initially, 3Dpoint clouds are projected onto a 2D grid to create surfaces fromthe lowest points. Weights are assigned based on the Height AboveGround (HAG) of these points. Ground points are distinguished byapplying a surface function to the dataset via iterative reweighting.Among the tested four robust weight functions, the Denmark andBeaton-Tukey functions outperformed others, achieving total errorvalues of 2.30 and 2.32 across three test areas, respectively. Thismethod efficiently filters MLS data, irrespective of ground pointproportions.