Andy gardiner ordnance survey

A Semi-Automatic Method for Detecting Changes to Ordnance Survey速 Topographic Data in Rural Environments C. Gladstone, A. Gardiner, D. Holland Ordnance Survey - Research

Introduction to Ordnance Survey® • Founded in 1791 • National Mapping Agency for Great Britain • Not just maps!

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1200 employees Field Surveyors (300) Photogrammetry department (70) Research team (25)

Motivation • Updating the National Topographic Database is our main task

Urban – 1:1250 Rural – 1:2500

• 222,000km2 of ‘Rural’ and ‘Mountain and Moorland’ data • Capture 70,000 km2 of aerial imagery per year • Manual photogrammetric data capture – hugely labour intensive • Identifying change is a big task • Semi-automation = faster update

Mountain and Moorland – 1:10000

Proposed Change Detection Flowline 1. Manual search for change Current manual flowline

Image capture & pre-processing

and 2. Manual topographic map update

Proposed Semi-automatic flowline

Image capture & pre-processing

Automatically identify ‘Change Candidates’

Manual update using only ‘Change Candidates’

Transferable

• Works in all types of geographical areas • No training data or manual ruleset calibration Efficient and easy to use

• Operated by expert photogrammetrists, not GEOBIA experts • Uses commercially available software • Compatible with current photogrammetric flowline

Changes in Rural Areas • Focus on changes within rural areas • Features dictated by OS topographic data capture specification; • New/demolished/significantly changed buildings, • Roads, tracks and paths, • Field boundaries – hedges, walls, fences, • Vegetation – trees, scrub, grass/crops • Sealed and unsealed surfaces, • Inland water – rivers, lakes, ponds.

• Line and area features

Input Data

Vexcel UltraMap

BAE Systems SOCET SET

Nonpansharpened 4-Band Imagery

Panchromatic Imagery

4-band Orthomosaic (50cm GSD)

DSM

ESRI速 ArcGIST M 10

eCognition 速

nDSM

Classification and Change Detection

Land-Form PROFILE速 DTM

Topographic Data

General Approach •

Classification and change detection achieved in eCognition®

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Segmentation uses both spectral and height data Tend to oversegment - refined as classification progresses

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Classification is more reliant on height than spectral data nDSM used as a guide only. Relative DSM difference more reliable Normalized ratios of spectral bands used whenever possible…

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Change Detection is raster to vector (classification to pre-existing topographic data)

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A few more detailed examples…

Spectral Thresholds Set from Ortho-mosaic Image •

Preliminary Tree and Building classification

“Dark” segments neighbouring “tall” objects temporarily classified as shadow candidates Temporary shadow candidates used to calculate brightness thresholds for shadow class • Shadow classification used to help filter ‘Change Candidates’

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4-band Ortho-mosaic seamlines are used to constrain spectral threshold calculations

Detecting New Linear Features • Linear features can’t be readily classified…

• Canny Edge layer automatically calculated within eCognition® • Edges filtered according to length and width to find significant lines • Multi-threshold segmentation used to generate significant line segments

• Line segments filtered and classified by context, shape and spectral criteria

• Intersect classified lines with topographic data to detect new linear features

Use of Existing Topographic Vector Data • •

Road edges in existing topographic data used to guide segmentation Known road segments used as initial candidates for ground surface in classification

• Existing topographic data for water used to calculate thresholds for water classification

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Existing topographic vector data for buildings used as comparison to test for demolished buildings

Aerial Image - May 2010

Digital Surface Model

Automatic Classification Result

Change Detection Result

Production Trial – Remote Sensing, Autumn 2012

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Two 3x3km trial areas in West Sussex “Typical” rural geography, containing a range of “typical” features

Results - Efficiency • Each trial area was updated twice • Once using the “traditional” manual method • Once using the automatic change detection method • Efficiency savings when using automatic method = 25% - 35% Examples of changes identified:

New building and path

“New” area of trees

Sealed to unsealed surface

Results - Correctness • Correctness (% of auto predicted changes that were genuine): • Correctness = 35% - 45% • Acceptable because it is quick/easy to reject incorrect changes

Typical false alarms:

Crop boundaries as New Lines

Leaf-off trees as New Buildings

Small, low buildings as Demolished Buildings

Results - Completeness • Completeness (% of the real changes that were found): • Completeness = 79% • •

Only 1 high priority (category A) change missed on each site 404 genuine changes across both trial sites

Typical missed features:

Subtle, new short fence

Small, low new farm building

Part-demolished short fence

Next steps Move from research project to a production tool • Designing a robust, production strength flowline

Process improvements • Investigate use of pan-sharpened 4-band imagery • More detailed land cover classes (heath, rough grass, marsh)

Other uses for the classification • Input to potential land cover product • Classification used to filter DSM to DTM

Thank you for listening

Andy.Gardiner@ordnancesurvey.co.uk +44 (0)23 8005 5759 www.ordnancesurvey.co.uk

Ordnance Survey, Adanac Drive, Southampton, United Kingdom, SO16 0AS