Remote sensing, LIDAR, automated data capture processes & the VEPS project

Andrew Richman, Andy Hamilton, Yusuf Arayici, John Counsell, Besik TkhelidzeThe Environment Agency for England and Wales, University of Salford, Greater Manchester,

University of the West of England, Bristol{andrew.richman@environment-agency.gov.uk, a.hamilton@salford.ac.uk,

y.arayici@salford.ac.uk, john.counsell@uwe.ac.uk, Besik2.Tkhelidze@uwe.ac.uk}

AbstractThis paper discusses recent project work that has

been examining how to minimise the digital 3Dmodelling of urban and rural environments, whereremotely sensed data is available. The increasingavailability of highly accurate LIDAR data offers theseopportunities, but currently is not captured so often noris yet an extensive coverage that it can be relied on tokeep VR analogues of real places up to date. This placesincreasing importance on developing an ‘urban datafusion’ of different types including: LIDAR; DigitalElevation Models derived from radar altimetry andsimilar data (SAR interferometry); real-time videophotogrammetry; and thus on standards. 3D models ofproposed changes then need to comply with thesestandards as they emerge.

There has been research into the 3D modelling ofurban settings and landscapes for visual impactassessment since the early 1980’s. This started by usingcommercial CAD systems, but has since used GIS inorder to generate 3D VR models of urban areas andintegrate them with information from various sourcesinto an overall navigable interactive whole. There isfound to be an increasing need for tools for integratingdifferent forms of representation or media, andstandards for: scalability (levels of detail); movementthrough space and change over time; for integrating andoptimising imported models of proposed change, oftenemanating from architectural practice; two way links forinteractive exchange between a selected view and thesource data; and retention of multiple differentinterpretations or proposals for comparison.

Keywords- LIDAR; SAR; DEM; Visual ImpactAssessment; Spatial Information; GIS; VR; World WideWeb.

1. Introduction

This paper discusses recent and forthcoming projectwork that has been examining how to minimise 3Dmodelling where remotely sensed data is available. Theincreasing availability of highly accurate light detectionand ranging LIDAR data (the basis for the forthcomingVEPS project described later) offers other opportunities.

However it is currently not captured sufficiently often,nor is yet a sufficiently extensive coverage, that it canyet be relied on to keep Virtual reality (VR) analogues ofreal places fully up to date. This places increasingimportance on developing an ‘urban data fusion’ ofdifferent data and media types that can theninterchangeably support a common 3D urbaninformation space. Such data types include:Conventional mapping: Satellite and aerial imagery;LIght Detection And Ranging (LIDAR); DigitalElevation Models derived from radar altimetry andsimilar data (SAR interferometry); and Real-time videophotogrammetry.The very range and diversity and increasing detail andaccuracy of such datasets, together with increasingfrequency of capture, creates problems for managing andextracting requisite data on demand. Automated capturecan swiftly create massive datasets and thus createssignificant problems for data management, storage andretrieval. For Virtual Reality views there are additionalchallenges in culling and selecting appropriate data inreal-time. As these datasets become broadly availableand provide overlapping descriptions and views of thesame places, there is an increasing urgency for thedefinition of common standards and metadata. 3Dmodels of proposed changes then need to comply withthese standards as they emerge. The main issue beingthat while exceptional efforts in resources can producevery good 3D urban models, this effort is difficult tosustain, and needs to become commonplace andsubstantially automated before it can be generally useful.The INSPIRE [1] draft directive defines a need for ‘acoherent combination of spatial data sets or services thatrepresents added value, without requiring specific effortson the part of a human operator or a machine’, and thisneeds to be both broadly available and commonplace, inorder for 3D urban models to become the norm.

2. 3D Modelling Urban and Rural Settings

There has been research into the digital 3Dmodelling of urban settings and landscapes for visualimpact assessment since the early 1980’s. This started byusing commercial CAD systems, but has since developedwith the use of Geographic Information Systems (GIS).The GIS have been used both to generate 3D VR models

of urban areas, and to integrate them with informationfrom various sources into an overall navigableinteractive whole, in a manner still difficult to achievewith the latest commercial CAD software [2]. There isargued to be an increasing need for tools for integratingdifferent forms of representation or media, and anincreasing need for standards that address and assist inautomating: Scalability (levels of detail); Movementthrough space and change over time; Integrating andoptimising imported models of proposed change, oftenemanating from architectural practice; Two way links forinteractive exchange between a selected view and thesource data; And retention of multiple differentinterpretations or proposals for comparison.

3. Available 3D Data for Contextual Models

In the recent Interreg IIIB project proposal for avirtual environmental planning system (VEPS) it wasargued that there is growing pressure to use 3Dmodelling and VR at the planning proposal stage tocreate virtual models that enable all the stakeholders tounderstand the proposals [3]. The 3D digital terrain dataavailable from mapping organisations is still consideredto be too inaccurate to make critical judgements aboutthe extent to which a new structure masks or intrudes onan existing view. “Urban simulations; that is computergenerated simulations of the built environment, are aneffective means of improving the public ’s participationin the planning process ”.[i4] “it is well known thatclassified urban land cover does not bear a spectrallyidentifiable correspondence with urban land use. theinadequacies of self-organisation in cities necessitatessome kind of intervention, and, in order to intervene,some knowledge of city dynamics is required. new,relevant, and timely lifestyles data may be 'tied' to otherframework data such as those provide by remote sensingor ordnance survey's addresspoint. We believe that suchapproaches offer the prospect of creating vastlyenhanced models of the form and functioning of systemswhich can be implemented into the management of'sustainable cities'”[ii5]

3.1. Aerial LIDAR and Environmental Data

3D building proposal information is often nowavailable from design practices and is inherently moreinclusively understandable by all stakeholders thanconventional drawings. However the contexts in whichsuch proposals are demonstrated are often unreliable andeven misleadingly inaccurate. Yet 3D contextualinformation is now increasingly available (at an accuracythat far surpasses commercially available mapping data)from LIDAR and it is claimed is now accurate enough tosupport local analysis of the visual environmentaloutcomes of detailed proposals. The EnvironmentAgency for England and Wales began R&D Surveysusing airborne LIDAR in late 1996, leading tooperational surveys from March 1998, concentrating onsurveys of river and coastal floodplains. To date they

have surveyed in excess of 60,000 sq km using anOptech ALTM laser scanner. A continued R&Dprogramme has led to the development of operationalfiltering routines for the separate extraction of “bareearth” (terrain models), vegetation and building (surfacemodels) objects. These filtering routines areimplemented using ESRI ArcView Spatial and 3Danalyst and include: Elevation Variance analysis;Maximum vs. Minimum difference within varyingspatial windows; Aspect Variance analysis; Inversehydro-fill and edge detection; Minimum filters (varyingspatial windows); Edge detection (varying thresholds)and area segmentation.

Figure 1: Example classified LIDAR objects;buildings, vegetation, bare earth. Copyright

Environment Agency 2005Combinations of different filtering techniques are

applied to specific landscape types (e.g., urban, steepslopes, forested, rural, etc) along with a choice ofinterpolation methods, and also tools for addingbreaklines and additional ground points.

The automated vegetation and building classificationidentifies DEM cells based on particular heightdifferences and places a buffer around the identifiedobjects. These objects may then be stripped out of ascene, and the bare earth terrain gap is filled by simpleinterpolation. The work requires some manualintervention for the finished product, which has a 1 metregrid spacing and a height accuracy of +/- 15cm. thecurrent new and improved methods of vegetation andbuilding classification identify unique and separateobjects for all of the surface features (i.e. for eachbuilding). These individual surface objects identified cannow have a height assigned to them.

The 3d raster products may then be used with avariety of environmental point and vector datasets e.g.river quality samples or verlain onto the OS 1:10k digitalmaps The data is also used for input into modellingFlood risk Zone maps (http:/ /www.environment-agency.gov.uk/yourenv/)

3.2. Ground Based 3D Scan Data

In the field of surveying, ground based 3D scanningtechnologies are able to acquire accurate 3D data about

portions of land and objects of various shapes and sizes.These instruments are commonly known as terrestriallaser scanners. While laser scanner instruments based onthe triangulation principle and high degrees of precisionhave been widely used since the 1980’s, TOF (Time ofFlight) instruments have been developed for 3D surveyapplications only in the last 5 years (Bornaz & Rinaudo,2004)[6iii].

These types of laser scanners can be considered ashighly automated total stations. They are usually madeup of a laser, which has been optimised for high speedsurveying, and a set of mechanisms that allows the laserbeam to be directed in space in a range that variesaccording to the instrument that is being used. For eachacquired points, a distance is measured on a knowndirection: X, Y, and Z coordinates of a point can becomputed for each recorded distance direction. Laserscanners allow millions of points to be recorded in a fewminutes. Because of their practicality and versatility,these kinds of instruments are today widely used in thefield of architectural, archaeological and environmentalsurveying (Valanis & Tsakiri, 2004). [iv7]

Terrestrial laser scanning offers fast 3D terrain dataacquisition. It has advantages over current surveytechniques including EDM, GPS and photogrammetricapplications obtaining high density point data withoutthe need for a reflector system. Merged data clouds havesufficient points to negate the need for DEM (DigitalElevation Model) interpolation techniques potentiallyproviding the optimum representation of any scannedsurface. The advantages of speed and high data pointdensity must be viewed against the data point accuracywhich may reduce instrument performance below thatachievable using EDM (Electronic DistanceMeasurement) techniques (such techniques are, however,much more time consuming). Any Improvement tomeasurement range, resolution, field of view anderror/accuracy would further fill the research gap relatingto spatial and temporal measurement of space andchange in the built and natural environment and resolvethe accuracy issue with regard to EDM techniques.

The University of Salford purchased a Riegl LMSZ210 scanner in May 2002. The scanner is connected toa 12V battery and a ruggedised laptop. The high-speedscanner has the following specifications: Maximummeasurement range = 300 m (in typical conditions);Minimum measurement range = 2 m; Measurementaccuracy = typical +/- 25 mm; Measurement resolution =25 mm; Beam divergence = approx. 3 mrad (i.e. 30cmbeam width per 100m range); Field of view = 80°vertical angle, 333° horizontal angle; Scanning rate =6000 points per second; Class I eye-safe laser.

During data acquisition, the 3D-RiSCAN software isused. It allows the operator to perform a large number oftasks including sensor configuration, data acquisition,data visualization, data manipulation, and data archiving.Numerous export functions allow the scanned data topassed to post-processing data packages for, e.g., featureextraction or volume estimation. PolyWorks software(produced by Innovmetric Software Inc.) provides

comprehensive set of tools for quickly processing 3Dscanned data. This software has traditionally been usedin manufacturing industries with very short rangescanners, but with the advent of longer range laserscanners, it has seen widespread use in surveying andarchitecture, especially within North America. Thesoftware can handle many millions of data points whilestill retaining the ability to model very fine details veryaccurately.

The processing of laser scanning data can becomplex and since the Salford scanner was purchased,research has been conducted to investigate this complexenvironment for the use in the built environment. Theresearch on the laser scanner can provide reverseengineering in construction to aid the refurbishment ofbuildings. Producing building design and CAD modelsand VR (Virtual Reality) models from existing buildings,by means of the laser scanner, will facilitate an analysisof the current conditions of the buildings. This isparticularly important for historic buildings, which mayhave been altered and where plans no longer exist.

Besides, it even has the potential to accuratelyrecord inaccessible and potentially hazardous areas suchas pitched rooftops. Consequently, it facilitates “virtualrefurbishment” of the buildings and allows the existingstructure and proposed new services to be seen in aneffective manner. Figure 1,2 and 3 below shows theJactin House example, which is the case study buildingin East Manchester. VR models were produced from thebuilding data captured by the 3D Laser scanner. CADmodels were extracted from the VR models and plasticmodel produced from the CAD model. This work isfunded by the EU: the Intelcities FP6 IP 2004-2005.

Figure 2: Virtual Reality (VR) model producedfrom raw scan data

Figure 3: CAD model produced from the VRmodel in figure 1

Figure 4: Plastic model produced from the CADmodel

Regarding architecture and construction, architectsfrequently need to determine the “as built” plans ofbuildings or other structures such as industrial plants,bridges, and tunnels. A 3D model of a site can be used toverify that it was constructed according to specifications.When preparing for a renovation or a plant upgrade, theoriginal architectural plans might be inaccurate, if theyexist at all. A 3D model allows architects to plan arenovation and to test out various construction options.

3.3 GMES Urban Services

The European Space Agency (ESA) funds GMESUrban Services (GUS). This project aims to project fromthe European Space Agency to consolidate a productportfolio, based on the combination of satellite imagesand in-situ data, to facilitate cities and regionalauthorities in their implementation of Europeanenvironmental policies. GUS aims to provide them withcost effective, up-to-date and homogeneous, “GIS ready”spatial environmental information mainly based on highresolution satellite image data. GUS has developed anumber of products clustered into three thematic areas:Urban Land Use Mapping – with urban land use; urbanchange detection; urban development modeling tool andplan monitoring products; Urban Development Control -with change detection hot spots; Urban EnvironmentalQuality – with mapping of sealed areas; Urbanthermography; Road noise observatory; risk and securitymapping and Brownfield mapping products.

Figure 5: 3D Map of the Castle Hertogen vanBrabant in Turnhout, Belgium

These products, except for the urban thermographyproduct which is airborne, are based on IKONOS,Quickbird and SPOT-5 satellite images with scales1:5000 to 1:25000. During 2004 GUS also developedUrban 3D Mapping product based on airborne data,which was delivered to the Belgium city of Turnhout.This is a 3D model of the historic castle, with horizontalresolution < 15cm and vertical resolution < 30sm and the

product is integrated into the Turnhout City Hallgeoportal to be viewed by tourists. Unfortunately, 3Dmodeling is not going to be developed further withinGUS project during the 2005-2008 period, as the mainfocus is on developing generic satellite based productswith highest demand and potential to cover all Europe.

3.4 Other suitable data

“LIDAR data has a best accuracy of around 10cmin height (Z) and costs around . $500 per km2 and is bestsuited to applications over limited areas with highaccuracy requirements. IfSAR (interferometric syntheticaperture radar) has a best accuracy of around 0.5m in Zand costs $5 per km2 LIDAR And is best suited to largerareas with lower accuracy requirements. LIDAR LASstandards have been developed in the USA and futherspecification standards are being developed.(http://www.lasformat.org). IfSAR data analysis is stillvery much reliant upon the system operator and for bareearth filtering there will inevitably be a need for manualediting after the automatic processing. Filtering ofLIDAR is probably more effective that that of IfSAR.

Spaceborne IfSAR is more established as a source ofDEMs that its airborne counter part. The ESA ERS1/2tandem missions have acquired very wide coverageof interferometric SAR pairs which have beensuccessfully used for the generation of regional DEMs.For example the Radarmap of Germany produced byDLR.. and the Landmap project in UK... The ShuttleRadar Topography Mission (SRTM) has also producedDEMs and orthoimages between 60° North and 56°South. In addition RadarSat, JERS, and ENVISAT allproduce interferometric data and in the future RadarSat 2and ALOS PALSAR will join the ranks of IfSAR datagenerators. IfSAR has also had an important applicationin differential mode for monitoring tectonic movementand subsidence. Data fusion exploits the synergy of twoor more data sets to create a new data set which is greaterthat the sum of the parts. “ “Although LIDAR has thepotential for application in building extraction and 3Dcity modelling, automatic feature extraction is still notmature and therefore the output is unreliable, and manualediting is very expensive. LIDAR is also very expensivefor small areas. Wider use of LIDAR may therefore haveto wait until better feature extraction algorithms areavailable. New airborne technologies such as 3 lineoptical sensors could also compete with LIDAR whenthey become more mature and can acquire data withhigher resolution than at present. Three line data avoidsocclusions and adds redundancy to the data set. Multisensor data could also do this. The use of a digitalcamera with LIDAR is already commonplace, but a goodmodel for reconstruction and error analysis is needed. Inorder to inspire confidence in the data better theoreticalmodels are required, both for single sensors, and for datafusion, in order that the errors can be better understood.Potential errors such as multipath and transparencyeffects also need to be studied much more. Morecomparative tests, especially with different algorithms,

need to be carried out, User need to be educated moreand to aid the greater use of the data, standards need tobe defined for products and for data exchange.”[v8]

3.4 The difficulties in achieving automation

“Semi-automatic extraction of GIS and CAD (computeraided design) data is still mostly restricted to researchand development. Implemented algorithms combinecomputer vision approaches with rigorousphotogrammetric modelling. Some results indicate thatfuture systems will be equipped with more powerfultools…The human-computer interface is increasinglybeing seen as an important factor. Efforts have beendevoted to the development of systems and tools for theintegrated management of large-volume heterogeneousspatial data and for enabling users to access variousEarth Observation (EO) and other spatial data atregional, national and global scales. The Working groupon Information Systems and Services (WGISS) of theCommittee on Earth Observation Satellites (CEOS) isone of such spatial data custodian organisations. Systemsand data fusion becomes increasingly important andmust be addressed on different levels. The trend of usingseveral sensors on the same platform requiresestablishing a common reference system for the sensors(fusion on the physical (sensor) level). Similarly, dataobtained by different sensors, perhaps not on the sameplatform, must be merged (fusion on the data level). Notall multiple sensor data can be merged on that, however;it may be necessary to extract features independently andmerge them on the feature level”.[vi9]

“SARs have difficulties in urban settings whenbuildings are high or if the streets are narrow… Urbanareas can also be troublesome to SARs because ofshadowing and layover effects… However, anotherparticularly useful side benefit with InSAR maps is thatthey can detect change exquisitely, at the level of inchesof vertical deflection. Each sensor technology offersother features that are useful and unique. Laser radarprovides the most automated and rapid processing. EOprovides the best horizontal resolution, and a great dealof qualitative information about structures. InSARprocessing is reasonably well-automated, provides thebest vertical resolution, and is all-weather, but it issomewhat constrained in imaging steep slopes and highdepression angles. It is likely that high-quality urbanmapping will depend on combining data from all threetechniques in the future. Today, a number of agenciesroutinely produce maps combining pairs of sensor types,most frequently EO and SAR, or EO in several bands.Although this combining is usually still performed by ahuman operating a workstation, the operator is usingtools that are rapidly increasing in sophistication.”[vii10]

“A fundamental challenge for spatial analysis toolsis the need to resolve the "knowledge gap" in the processof deriving information from images and digital maps.This knowledge gap has arisen because our capacity tobuild sophisticated data collection instruments (such assatellite sensore, LIDAR, and GPS) is not matched by

our means of producing information from these datasources. Benefits to the geographical informationcommunity would accrue from the use of open-sourceGIS tools. E.g. … TerraLib, an open-source GIS librarythat enables quick development of custom-builtapp l i ca t ions fo r spa t i a l da ta ana lys i s(www.terralib.org).”[viii 11]

3.5. Continuing Need for Data Fusion

“The ISPRS Journal of Photogrammetry andRemote Sensing (Vol 58(1-2), 2003), published a themeissue on multi-source data fusion for urban areas whichclearly demonstrates the range and importance of datafusion. Data fusion can be used for many applications.Some of the established ones are: Assisting phaseunwrapping; Eliminating errors and blunders;Atmospheric correction; Providing orientation in areaswhere there is no control; Terrestrial images to LIDAR;Feature extraction, such as buildings and roads; Otheraspects of feature extraction and environmental analysis”

In the early digital urban models of the 1980’s and1990’s in the UK there was little consistency oragreement on scale, level of accuracy, or levels of detail.Some were of quite low levels of detail and geometricaccuracy, making use of great numbers of bitmapelevations, and often based on Ordnance Survey (OS)map and terrain data. Experience in several projectsshowed that the OS data, while kept up to date, was too'crude' to easily merge with data derived from totalstation street surveys, and the OS 3D terrain data stoppedat significant man made features such as railwayembankments, for which accurate 3D data was difficultor impossible to safely obtain. The OS data providedfootprints at ground level but heights of buildings andthe detail of roof-scapes had to be obtained from othersources. Even at that time there was therefore a need toamalgamate and meld data from different datasets atdiffering levels of geometric accuracy in order toconstruct or update an urban model [4].

Other city models were of significantly highergeometric accuracy, but perhaps consequently moredifficult to keep up to date as urban change occurred.Bourdakis [5] stated that coordination of differentmodels or part models is sufficiently difficult to managein practice to justify a common unifying geometricstructure or primary model, to which all relate or fromwhich all are initially generated. Bourdakis had workedon both Bath and London City Models, modelled at aconsistently high level of geometric detail with little orno use of bitmaps, from which lower level of detailmodels or part models of areas of interest were thengenerated on demand; (although the underlying terrainmodel was perhaps not so accurate, apparently beingbased largely on interpretation and interpolation fromOrdnance Survey spot-height data).

Counsell et al [1] further argued that modelling andupdating urban models takes place over a long periodand that software, bandwidth, standards and detail willchange over that time, rendering any fixed level of detail

potentially obsolete, and hence that for effective use ofresources an evolutionary approach is necessary, inwhich one may model the whole at a lower level of detailand then on demand introduce pockets of higher detail,as these become available from CAAD modelling or asdebate and interactive use define a demand for moredetail at that location.

This view of an increasing meld of different datasetsin urban modelling appears to be being borne out inpractice with the recent creation for example of theHeidelberg City Model, based on both LIDAR andconventional mapping data [6]. 'A range of different datasources were collected and constructed with differentmethods. These build a heterogeneous data source poolthat needs to be homogenized and integrated to beaccessible via a single 3D server. Examples of this datainclude several layers of digital 2D GIS data (ALK =Amtliches Liegenschaftskataster = German officialdigital data set for 2D-geometries) covering the wholecity of Heidelberg, mostly from the land surveying officeHeidelberg. Furthermore, another data source consists oflaser scan data of the old town of Heidelberg that hasbeen processed by the Institute for Photogrammetry (IfP)of the University of Stuttgart using an automated methodand textured VRML models of important building andsights that have been created manually using modelingtools' [6]. There is also the increasing availability ofSAR interferometry from the European Space Agencyand its emerging role in identifying and mappingauthorised and unauthorised development in urban areas.

A common 3D coordinate and reference system isnecessary to support the melding of such data over time,and needs to be sufficiently accurate to accommodate thehighest levels of detail likely to be required., Howeverthere still appears to be little consensus or agreement onlevels of accuracy or levels of detail today, either inarchitectural CAD practice generally, or in urbanmodelling, except in Germany where City Modellershave agreed to use City-GML as a common standard.(City-GML was recently proposed for part of ISOstandard, but rejected in case it slowed adoption of theremainder of the standard.) Thus elsewhere in Europeand in existing German modelling each digital urbanmodel tends to be developed in an ad-hoc manner and soit is likely to remain difficult to readily take a componentpart of one urban model and marry it into an urbanmodel created for a different place, until such commonstandards are defined and agreed.

4. European Standards for Spatial Datasets

There are moves to establish European standardsrelating to spatial datasets, which will eventually have animpact on common standards for urban modelling. Theirgoal is described as 'an open, cooperative infrastructurefor accessing and distributing information products andservices online’ (Inspire AST) [7]. The Inspire projectaims to establish ‘an infrastructure for spatialinformation in the Community’ and has published a

proposed directive in this respect that was adopted by theEuropean Commission (EC) on the 23rd July 2004 [1].

Figure 5: Diagrammatic view of the Inspirevision, (from Inspire AST 2002 p9)

It is stated that the proposal ‘focuses specifically oninformation needed in order to monitor and improve thestate of the environment’, and that ‘much of thisinformation needs to be underpinned by "multi-purpose"spatial data’. It is explicit that ‘in an infrastructure forspatial information, not all spatial data themes need to besubject to the same degree of harmonisation, nor canthey be brought within the infrastructure at the samepace’, and therefore gives different deadlines forharmonisation of different categories of spatialinformation. These are defined in the annexes to theproposed directive, but it makes clear that ‘they do notdetermine how spatial information should be organisedor harmonised’, although it states that ’implementingmeasures should be … designed to make the spatial datasets interoperable’ and that this should be achieved in‘such a way that the result is a coherent combination ofspatial data sets or services that represents added value,without requiring specific efforts on the part of a humanoperator or a machine.’ At the end of the proposal (pp 30and 31) it lists a comprehensive range of data types thatunderpin urban modelling, to which the directive willapply. Other directives and actions also indicate a moveto harmonisation and inter-operability. For example theEC have announced a GMES action plan from 2004 to2008 aimed at achieving integration and consistency ofsuch data [8]. While these initiatives do not specificallyexclude 3D data one can find nevertheless an implicitassumption that at the scales at which data is to becollated height data is less significant.

Height data standardisation was addressed in a 2004workshop on establishing ‘Vertical Reference Systemsfor Europe’. In the conclusions from this workshop itwas stated that significant work was still needed toachieve harmonised decimetre accuracy by 2006,although accuracy at the metre level was available now.The current problem was defined as 'Worldwide there aresome hundred physical height systems, in Europe about20, realized - by different reference tide gauges(inconsistencies within 2 m range) - by spirit levellingreduced by different theories (10-6) - at different epochs

- and as static systems' (EVRS) [9]. The Inspire ASTposition paper [7] made it clear that ‘from the Europeanpoint of view Coordinate Reference Systems (usingETRS89 and additional projections) will be used both inGIS and in geodesy. Both applications correspond todifferent accuracy classes (one or more meters in GIS;several decimetres or less in geodesy). At regional levelalso GIS information moves to smaller accuracy (likecadastre) with (adequate) regional geodetic referencesystems becoming more important.’

Figure 6: Diagram showing proposed ‘scales’ orgranularity of data -Inspire RDM position paper

2002The OpenGIS specification for locational geometry

[10] refers to the need for many spatial reference systemswith explicit mappings between them. In this light thecommon European Coordinate Reference Systemdatabase contains the descriptions of national CoordinateReference Systems of European countries and thedescriptions of Transformations to European TerrestrialReference System together with the descriptions ofEuropean Coordinate Reference Systems. (Inspire RDM)[11].

From this it may be argued that Europeanstandardisation that identifies local accuracy aspotentially adequate for the range under 2.5m in X, Y,and possibly Z, needs clarification and furtherdevelopment to become of use in urban modelling foraccurate visualisation. In practice these further standardsmay become defined in a de-facto manner by readilyavailable LIDAR data of significantly higher accuracy.De-facto standards that emerge from practice oftenprecede and establish international standards. Yet at thismoment there is still be a need for those engaged invisualisable (and thus 3D) urban models as facsimiles ofreality to define the most appropriate spatial referencesystem (that will serve all or most local requirements)while standardising their creation and maintenance, butalso to provide mappings from the localised referencesystem to national and international reference systems. Itis suggested that further research is needed in this area,and this is likely to be one focus of the VEPS project.

5. The VEPS Project

The Virtual Environmental Planning System (VEPS)project started in December 2004, with Interreg IIIBfunding for 3 years. Partners include: The EnvironmentAgency for England and Wales; the University of theWest of England, Bristol; Centre Scientifique etTechnique du Bâtiment; University of Salford;Manchester Digital Development Agency (a division ofManchester City Council); Clementine Media Limited;Stuttgart University of Applied Sciences; and theUniversity of Freiburg. The primary objective of the

VEPS project is to share technical competencies in thefield of: 3-dimensional visualisation; ICT applications topromote public consultation; environmental modelling;data collection and use for e-Planning in territorialdevelopment in North West Europe.

It is argued in justification of the project thatconventional planning systems are still substantiallybased on two-dimensional (2D) plans. Such 2D mapsand plans are readable using taught technicalinterpretative skills that are inherently exclusive to thosepeople without 'professional' qualifications and arecounter-intuitive to the layman. Despite the use oftaught skills 2D maps and plans also contain inherentambiguities that lead to and support misinterpretationeven by professional planners. At present there are onlytwo possibilities for planning consultations: either - thecomplex information in a planning consultation is“dumbed down” for the citizen i.e. the information isreduced to a level which can be understood by theaverage member of the public who does not hold aqualification or diploma in planning; or - the fullinformation is presented and the citizen must receivetraining in order to understand the highly accurate andhighly complex information. The VEPS project is a steptowards a third possibility which eliminates the need to“dumb down” the information and eliminates the needfor training. An interactive three-dimensional virtualreality visualisation will allow the viewer to experiencethe highly complex information without the need fortraining because they can see and experience what thevisual impacts of the planned development will be andcan see the environmental impact in the associatedmodel. [3].

The second objective of VEPS is to develop acommon architecture and methodology to enable citizensto view and respond to planned changes via home PCs.(The third objective of VEPS is to refine and implementa test-bed system and to evaluate and refine themethodology and the system architecture. In the finalsystem, we anticipate that users will be able to examinethe underlying raw data, and thus it will also promote e-Learning.) A key action of VEPs will be the analysis oflarge data sets, including high resolution 3 dimensionalheight data from LIDAR, for use within Virtual Reality(VR) visualisation software and the subsequent deliveryof the VR environment via the internet and world wideweb. The virtual environment used in VEPS can lookextremely realistic as the LIDAR images demonstrate.These images are compiled by overlaying different datasets, LIDAR data , and for example, Compact AirbourneSpectographic Imager (CASI) data, which records thecolour of the underlying ground [3].

Among the partners, researchers in the Departmentof Remote Sensing and Landscape Information Systemsat Freiburg University have developed methods andsoftware to calculate and to extract and visualizeinformation automatically from LIDAR and multi-spectral data. Work on two projects, Highscan and thestill running Natscan [12] has enabled them toautomatically extract and distinguish between vegetation,

streets and buildings. In the Highscan project theyshowed that, (with the Laser scanning technology then inuse,) the DTM accuracy obtained with the laser scannervaried from 15 cm to 1m depending on terrain slope[13].The focus of the Natscan project is a survey ofcomprehensive methods of laser scanning technologyand their adaptation to the specific requirements ofenvironmental monitoring. In the second phase aeriallaser scanner data and terrestrial laser scanner data willbe integrated into a closed system for multiple data use.

5.1. Alternative User Generated Proposals

The VEPS product will be based on interactive 3Dvisualisation. As such it represents a technology leap inthe modernisation of the planning process. Thevisualisation would also allow the user to explorealternatives such as before and after scenarios. Othertypes of data can also be overlaid according to the userrequirements. All the data would be underpinned bymapping data. The user will be able to explore thisvirtual world and click on features to see theenvironmental model. In some cases the environmentalmodel might be dynamic e.g. for properties in a floodplain the model might show the water graduallyinundating the planned development. In addition, theVEPS product challenges existing practice in that itwould allow a two-way consultation process. It wouldallow citizens to upload their own alternative planningscenarios and view the results in terms of visual andenvironmental impact as well as download and view thedetails of the planned development. The user could alsomanipulate the planned development and see the impactof their own suggestions [3]. This poses the question (asyet unanswered) of which media will acceptably andcredibly present user's own suggestions in this context. Itmight be argued that 2D images billboarded into the 3Dcontext might adequately convey a proposal from aspecific set viewpoint. There is certainly a truism thatsystems that exclude methods or approaches that arefound useful and relevant become bypassed andconsequently risk obsolescence. An effective versatileand lasting system should accommodate approaches andmethods not envisaged by its designers. In this respectthe VEPS project will examine, define and integratestandards for incorporating other media into the samespatial referencing system.

Conclusions

There is still a pressing need to develop better meansof integrating and fusing data from different datasets toautomate digital urban and landscape models that are atpresent highly resource intensive. There is also a need tore-examine and define in such standards the data to berecorded with media such as images and video. It shouldthen eventually become possible to limit the resourceintensive 'manual' modelling tasks to proposals forchange that have yet to exist, or to interpreting historicevents and contexts that no longer exist. While currently

emerging European Standards for 2D data and 'verticaldata' provide a context, there is still within this contextan apparently as yet un-addressed need to define digitalurban model standards that define data and urbanmodelling outcomes, at scales / resolutions that enablecritical tasks such as visualisation and construction to beadequately supported. The VEPS project will need toaddress these issues in attempting to define a usable pan-european standard approach to uploading proposals forchange and alternative scenarios into the context of suchincreasingly available highly accurate datasets as thosederived from LIDAR.

Acknowledgements

The VEPS project work presented in this paper isco-financed through the INTERREG IIIB North WestEurope programme.

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