Transcript
Page 1: Automatic Generation Cleaning & Generalization of 50 Contours for NRCan's Geospatial Database

Automatic Generation, Cleaning and Generalization of 50k Contours for NRCan's Geospatial Database (GDB)

Johann LevesqueProject Officer

April 20, 2012

Etienne BonhommeProject Officer

Page 2: Automatic Generation Cleaning & Generalization of 50 Contours for NRCan's Geospatial Database

Overview

Background Objectives What’s the problem! How did we solve it? Workspaces Overview Examples Did we succeed? What’s next? Questions

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Background

14 000 map titles for NRCan’s National Topographic Series (NTS)

4000 of them still have imperial contours 800 don’t have contours (never published) Contours are the essence of a Topographic Map Essential to complete the mapping of the North =

National Coverage

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Objectives

Generate clean contours using one source = Canadian Digital Elevation Data (CDED)

Complete the Mapping of the North by April 2012 Increase productivity by automating the process

Reduce the manual editing by reversing the ratios… 80% editing 20% machine 90% machine 10% editing (ideally!!!)

And of course with limited resources!

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What’s the problem!

In fact, it’s many problems…

Self intersection

Contours crossing

Contours in water

Ugly contours

And many more…

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How did we solve it…

Using !

Validation

33 workspaces = 5-6 hours processing

14 workspaces = 1 hour processing

Opalis

EditionContourCreation

Inspection Delivery

GDAL: raw contours

ArcGIS ArcGIS SDE DB: clean contours

Automatic Cleaning

6 workspaces53 workspaces!!!

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Workspaces Overview

Load and delete Format conversion Report creation Fill and change attributes Simple data manipulation Loop and embedded custom transformer Complex data manipulation Embedded python script

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Include lakes into CDED

Problematic CDED and Hydrography does not fit all the time Hydrography is not always classified as lake

Solution Find lakes with statistics (area, length, CDED elevation) Set lakes flat with a buffer around them Recreate the CDED

Bonus Reduce to almost zero intersections with lakes

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Amalgamate contours

Problematic Small isolations = noise Bad data structure

Solution Restructure the data then amalgamate the

contours (Python and Shapely done by Centre for Topographic Information – Sherbrooke)

Bonus Gives clean data with less structure problems

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Neatline vertices cleaning

Problematic Data specification

No vertices within 5 meters of the neatline Small portion of contour

Surface creation problems Solution

Find vertices in error (list) Loop trough the list and remove them

Bonus Clean data Minimize support (following workspaces run fine)

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Surface creation

Problematic Not able to set elevation point attributes automatically Not able to set contour attributes automatically

Solution Create surfaces with contours and neatline Find type of surface with CDED

Bonus Attributes are set automatically We save hours of editing on huge file

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Create contours on summit

Problematic Can miss contour on summit to include elevation

point Use circle or same contour with negative buffer

Solution Find summit Use CDED to find the best shape

Bonus Better contours All attributes set automatically

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Adjust re-entrance

Problematic Linear rivers does not match the CDED

Solution Set linear rivers orientation with CDED Move contours to fit rivers (Python and Shapely

done by Centre for Topographic Information – Sherbrooke)

Bonus Correct small re-entrance problems automatically.

Re-entrance can be 25% of overall errors

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Neatline snapping

Problematic Contours does not connect to the corresponding

contours on adjacent map Solution

Find matching contours on both side Snap them and check for intersection

Bonus Operators does not have to tie them manually Can save moving hundreds of lines

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Did we succeed?

We think so! We are around 65% machine-35% editing. Pretty

good… We think we can do better We have beaten our April deadline by 3 weeks Analysis on 14 files (before and after)

Mean number of errors before: 1452 Mean number of errors after: 508 Reduction of 65%

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Did we succeed?

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What’s next?

We think we can achieve 80% machine 20% editing when we solve the hydro intersection problem (double line and single line rivers).

To reach 90% machine 10% would require better data. There is so much fudging we can do…

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Thank You!

Questions?

For more information: Johann Levesque [email protected] Natural Resources Canada WWW.GEOGRATIS.GC.CA WWW.GEOBASE.CA

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Error report sample

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Simple data manipulation

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Complex data manipulation

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Include Lake into CDED

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Amalgamate Contours

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Neatline vertices cleaning

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Create Surface

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Create Contour on Summit

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Adjust Reentrance

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Neatline Snapping


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