The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 571–576, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-571-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 571–576, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-571-2022
 
30 May 2022
30 May 2022

HEURISTIC GENERATION OF MULTISPECTRAL LABELED POINT CLOUD DATASETS FOR DEEP LEARNING MODELS

L. J. Comesaña Cebral, J. Martínez Sánchez, E. Rúa Fernández, and P. Arias Sánchez L. J. Comesaña Cebral et al.
  • CINTECX, Universidade de Vigo, Applied Geotechnology Group, 36310 Vigo, Spain

Keywords: Point Cloud, Heuristic Generation, Deep Learning, Road, Forest, Multispectral, Dataset

Abstract. Deep Learning (DL) models need big enough datasets for training, especially those that deal with point clouds. Artificial generation of these datasets can complement the real ones by improving the learning rate of DL architectures. Also, Light Detection and Ranging (LiDAR) scanners can be studied by comparing its performing with artificial point clouds. A methodology for simulate LiDAR-based artificial point clouds is presented in this work in order to get train datasets already labelled for DL models. In addition to the geometry design, a spectral simulation will be also performed so that all points in each cloud will have its 3 dimensional coordinates (x, y, z), a label designing which category it belongs to (vegetation, traffic sign, road pavement, …) and an intensity estimator based on physical properties as reflectance.