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, 409–415, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-409-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 409–415, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-409-2022
 
30 May 2022
30 May 2022

BULLDOZER: AN AUTOMATIC SELF-DRIVEN LARGE SCALE DTM EXTRACTION METHOD FROM DIGITAL SURFACE MODEL

D. Lallement1, P. Lassalle1, Y. Ott2, R. Demortier2, and J. Delvit1 D. Lallement et al.
  • 1Campus de la Donnée, Centre National d’Etudes Spatiales (CNES), 31400, Toulouse, France
  • 2Thales Services SAS, 31670 Labège, 31400, France

Keywords: Digital Surface Model, Digital Terrain Model, cloth simulation, scalability, self-driven

Abstract. This study presents a Digital Terrain Model extraction method called Bulldozer. The only required input of Bulldozer is a Digital Surface Model generated from any sensors (usually optical or LIDAR) with any kind of software. After reviewing both the initial DrapCloth algorithm (Zhang et al., 2016) and its multi scale implementation (Leotta et al., 2019), some issues have been highlighted when extracting DTM from stereo satellite images such as the lost of ground adhesion under rising terrain areas, the appearance of sinks due to correlation issues when computing the DSM and finally the lack of scalability when processing large input data. Bulldozer has been developed to tackle all these issues and proposes a full automatic scalable pipeline composed of a pre-processing step to clean noisy DSMs by detecting and smoothing disturbed areas, a DTM extraction step based on a modified DrapCloth algorithm to stick to the ground under rising terrain and a post-processing step to smooth sharp sinks. The scalability has been solved using a tiling strategy and the definition of a stability margin that ensures identical results to those obtained if the whole DSM would have been processed at once in memory. As a result, Bulldozer outperforms its concurrent with respect to runtime execution while providing high quality DTMs over various types of landscapes.