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18-Geom-B1 Digital Terrain Modelling · December 2014

Question 8 of 12: Filtering vs Smoothing

Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)

Notes on this paper

Paper format: National Exams, December 2014 — 3 hours, closed book (any non-communicating calculator permitted). TWELVE numbered questions constitute a complete paper; each is of varying value and the schedule totals 100 marks. Most answers are required in essay format, so clarity and organization are graded. All twelve questions are solved below for completeness.

Reference texts: Li, Zhu & Gold, Digital Terrain Modeling — Principles and Methodology (CRC Press, 2005); Maune (ed.), Digital Elevation Model Technologies and Applications: The DEM Users Manual (2nd ed., ASPRS, 2007); Wilson & Gallant, Terrain Analysis — Principles and Applications (Wiley, 2000); Wolf, Dewitt & Wilkinson, Elements of Photogrammetry with Applications in GIS (4th ed., McGraw-Hill); Natural Resources Canada High-Resolution DEM (HRDEM) and CDEM product specifications. Canadian datums throughout (NAD83(CSRS), CGVD2013).

Check / source note. The Question 1 header carries 9 marks but its annotation reads “(3 × 2 marks)” = 6. The header value is authoritative because it is the figure that makes the printed schedule sum to the stated 100 Total marks; the “3 × 2” is a typographic error for 3 × 3. Answers are graded on all three parts of Q1 equally.

Question 8: Filtering vs Smoothing (5 marks)

Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.

Given. Two processing operations applied to raw elevation data.

Find. How filtering differs from smoothing.

Difference. Filtering is a classification/removal operation: it decides which measured points to keep or reject. In DTM production the dominant case is ground filtering—separating bare-earth returns from non-ground (vegetation, buildings, birds) in a Lidar/DSM point cloud so that only terrain points remain (e.g. progressive TIN densification, slope-based, or morphological filters). More broadly, filtering also removes noise and blunders. Its output is a subset of the data; the surviving heights are typically unchanged.

Smoothing is an averaging/modification operation that reduces high-frequency noise and roughness by adjusting the height values—e.g. a moving-window mean or median, a low-pass (Gaussian) filter, or spline fitting. It keeps all points but changes their values to produce a gentler surface. The key contrast: filtering selects points (which stay/go) and is largely about which surface is being modelled; smoothing alters values to suppress noise and improve visual/derivative quality, at the risk of blurring genuine sharp features (breaklines) if applied indiscriminately.