Classification of Large-Scale Mobile Laser Scanning Data in Urban Area with LightGBM

Creative Commons License

Sevgen E., ABDİKAN S.

Remote Sensing, cilt.15, sa.15, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 15
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/rs15153787
  • Dergi Adı: Remote Sensing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, Compendex, INSPEC, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: classification, deep learning, hand-crafted features, LiDAR, LightGBM, point cloud, random forest, semantic segmentation, urban
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Automatic point cloud classification (PCC) is a challenging task in large-scale urban point clouds due to the heterogeneous density of points, the high number of points and the incomplete set of objects. Although recent PCC studies rely on automatic feature extraction through deep learning (DL), there is still a gap for traditional machine learning (ML) models with hand-crafted features, particularly after emerging gradient boosting machine (GBM) methods. In this study, we are using the traditional ML framework for the problem of PCC in large-scale datasets following the steps of neighborhood definition, multi-scale feature extraction, and classification. Different from others, our framework takes advantage of the fast feature calculation with multi-scale radius neighborhood and a recent state-of-the-art GBM classifier, LightGBM. We tested our framework using three mobile urban datasets, Paris–Rau–Madame, Paris–Rue–Cassette and Toronto3D. According to the results, our framework outperforms traditional machine learning models and competes with DL-based methods.