Corgarff Castle and Elgin Cathedral

It’s been almost 2 weeks since my fieldwork trip to the Highlands – a 4-day survey whirlwind through several locations in the North of Scotland, including Inverness, Elgin, Fortrose and Corgarff. Most of my time was spent laser scanning Corgarff Castle – together with HES colleagues from the Fort George office and Li Sou from Historic England. Corgarff is a very special site: a striking white garrison tower surrounded by star-shaped walls, located at the edge of Cairngorms National Park. The location is very remote, but the views were stunning.

One of the highlights of the trip was certainly my return to Elgin Cathedral. Elgin was my first field project with the Digital Documentation team and provided the material for my very first post on this blog (see Elgin Cathedral QTVR). The objective was to photograph the interiors of the two towers of the Cathedral in order to produce a new set of QTVR panoramas – documenting the spaces after the installation of the new interpretation suite. We used the same set-up as before (Nikon D810 mounted on survey tripod using nodal ninja) with a few improvements (HDR photography using 5 brackets instead of 3, plus remote shutter release to eliminate movement). The system worked really well and we managed to finish the job in a few hours with great results. The only (slight) downside of this process: 5 brackets for every angle (3 rotations plus ceiling shots) resulted in approx. 150 images for each location (in RAW and JPG) = a lot of storage space. However, the quality of the final panoramas definitely justifies the file sizes!

I have already processed the raw photos in Photomatix to create the HDR images, which I’m now stitching together in PTGui to produce the panoramas. I’ve been using only RAW photographs for processing and exporting uncompressed TIFs, which doesn’t seem to add much to processing time, but really makes a difference to the quality of the final product.

My visit to Elgin was a great chance to finally see the stone collection properly displayed in the brand new exhibition, after a year of conservation work – which took place literally next door to our office in Edinburgh, in the HES Conservation Centre. The dark purple display stands were a perfect choice for showcasing the stones. The effigy of the bishop is one of the highlights of the collection – the light projection system used to display the colour scheme onto the stone is brilliant.


Colour scheme projected onto the stone effigy.

Once the QTVRs are completed, my next project will be to process all the laser scan data from Corgarff Castle and Fortrose Cathedral. I’ve already imported all the data into Cyclone using the Auto-Align function, with moderate success (it gets confused by repeating geometry such as staircases with sometimes “interesting” results). However, auto-align can be a great tool for quickly checking results at the end of each day on the field – when there is still a chance to go back and fill in any gaps.

Laser scanning Kinneil House

A few months ago, the Digital Documentation team spent 2 days in the Kinneil Estate near Bo’ness. There were several reasons for this visit; one of them was to collect data (laser scanning and GNSS) in the area of the Roman fortlet for a PhD research project on the Antonine Wall.

The other reason for the visit was to laser scan Kinneil House itself for the Rae Project, our programme of digitally documenting all of HES’s properties in care. I was sadly not involved in scanning the House, which has some incredible Renaissance wall paintings and really is worth a visit. I was, however, given the task of processing the data, registering the scans in Cyclone and producing the deliverables: TruViews and a set of orthoimages (plans, elevations and sections).

The project consisted of P40 scans with HDR imaging for the exterior, plus HDS6100 and Faro scans for the interiors (total around 60 scans). I registered the scans and then cleaned them in Cyclone to isolate the building, remove trees, people and occasional noise from the data. For the orthoimages, I experimented with different visual styles, to see which one would bring out more detail in the point cloud. In the end, for each view I exported the same ortho-TIFF with different visual styles (shaded and silhouette) and combined them in Photoshop. Here are some of the results:

The Hidden Landscape of a Roman Frontier is joint PhD programme between HES and Canterbury University. For more info see here or follow Nick Hannon @Hannon_Arch on Twitter.

The local charity group Friends of Kinneil have been very enthusiastic about our work on the site. You can follow their activities on Twitter @kinneil.

And, as always, follow Rae Project activity on Twitter #RaeProject.

2nd SEAHA Conference, Oxford

I have arrived back in Edinburgh after spending  2 fantastic days in Oxford at the 2nd International Conference on Science and Engineering in Arts, Heritage and Archaeology (SEAHA). I had the pleasure of listening to some great speakers during the 8 sessions on topics ranging from Imaging and the Environment to Digitisation. The conference also offered a great range of breakout sessions: my choices were a tour of the conservation department at the Ashmolean Museum led by Mark Norman and a heritage tour of Oxford by Professor Heather Viles.


Part of the conference was also a poster session, where I participated with a poster on 3D moisture mapping. Using Skelmorlie Aisle in Largs as an example, I combined 2D image data from microwave moisture meters with 3D point cloud data from laser scanning using the functionalities of Leica Cyclone software. This method of presenting image-based data enables much clearer communication of scientific data, such as moisture levels, and in some cases can aid interpretation of the results. The same workflow can be applied for thermal (IR) images and other image-based data. In fact, the application of both thermal and moisture data to the Skelmorlie Aisle point cloud (in different areas, of course) is the next step of the project.

My poster has been included in the book of abstracts, which can be found as a PDF on the SEAHA website.

3D moisture mapping: combining image-based data and 3D point clouds

Sofia Antonopoulou, Historic Environment Scotland

3D moisture mapping is a collaborative project between the Digital Documentation and Science teams at Historic Environment Scotland, which aims to develop a method for analysing and presenting the environmental behaviour of historic buildings by combining different datasets. In this case, environmental information in the form of 2D moisture maps is overlain on 3D geometric data from laser scanning. The resulting datasets retain the colour-coded environmental information (moisture levels) with the spatial accuracy of 3D laser scans, thus creating a metrically accurate 3D model of the environmental condition of a building. This can be used as a tool for analysis, interpretation, and presentation of image-based environmental data for the purpose of informing conservation decisions. The case study focuses on Skelmorlie Aisle, a 17th century chapel in Largs, Scotland. The Science Team at Historic Environment Scotland have been regularly monitoring and analysing the environmental conditions inside the chapel, collecting data on temperature and moisture levels. In the case study, the data from microwave moisture meters (2D moisture maps) are combined with laser scan data (3D point clouds) captured by the Digital Documentation Team. The methodology developed for 3D moisture mapping can also be applied for other image-based information, such as thermal imaging.

The SEAHA conference was an incredible opportunity for me to listen to some really interesting presentations and find out more about current research on heritage science. I was able to chat to some very knowledgeable people about all things heritage, be inspired by them, and get insight and ideas on a variety of issues – even potentially finding solutions to some practical (digital documentation) problems, but more on that to follow…

For a full account of what went on at the conference, follow the SEAHA blog here.

Saying goodbye to Oxford on Wednesday with a walk around the city centre (and a rather long visit to Blackwell’s on Broad Street) brought me back in front of the Sheldonian Theatre to have a last look at the Emperor Heads. Until next time Oxford.

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One of the Emporor Heads outside the Sheldonian Theatre in Oxford. Apparently, no one knows exactly who or what they are meant to be. My favourite theory is that they represent a history of beards. This guy at least looks pretty Greek to me…

3D and 4D Digital Monitoring of Sea Defences at Skara Brae

A while ago, I was asked by the Climate Change team at Historic Environment Scotland to write a short article on the work we did at Skara Brae this April, to be featured in the HES Climate Change blog. The blog is a great resource on various topics relating to the effects of climate change on the historic environment.

The article explained how laser scanning is being used to monitor the condition of the protective sea wall that has defended the Neolithic village for more than 80 years. In light of growing concern for the effects of climate change on coastal heritage, our work on the site is of particular importance in ensuring the long-term survival of Orcadian heritage.

The blog article was also a good opportunity for me to produce images from the database, which is still being processed. For more details on our work at Skara Brae, please read the article here.

Cleaning scans in Cyclone

Registering scans from the Great Hall in Edinburgh Castle has been a great opportunity for clarifying certain aspects of the registration process in Cyclone: mainly, the effect of cleaning scans for cloud alignment and registration.

Some laser scanning manuals and practice guides advise that cleaning scans prior to registration can have a positive effect when cloud alignment is used as a registration method. That means removing “bad data”, such as speckle noise or moving objects (cars, people, trees). In the Great Hall project, the effect of cleaning scans was apparent in the cloud-to-cloud alignment statistics  (mainly looking at RMS values). The improvement could also be verified visually: registered clean scans present “crisp” surfaces, in contrast with the noisy, “fuzzy” result from the original scans.

It must be noted that manually cleaning scans can be very time-consuming depending on the number of scans, amount of noise and geometry of spaces/objects. Using features like the limit box or various sections/slices through the point cloud can help to identify, isolate, and remove bad data. Cleaning scans is more important when the main registration method is cloud-to-cloud, as the results depend entirely on the effectiveness of the ICP algorithm and overlap between scans.

Deciding when to clean is also important: immediately after importing scans (before any kind of registration) or half-way through (e.g. after putting scans together with minimum number of cloud constraints, but before auto-adding extra constraints)? In some cases, it was very useful to first quickly register the scans without cleaning. This will not produce the best registration result, but was good enough for identifying data voids or other problems. This initial registration can be done before the data acquisition phase is over and even on site: additional scans can be done without having to return to the site later. After this initial registration phase, the scans can be properly cleaned and the registration updated to work with the clean point clouds.

This process has been followed for the Great Hall registration as well as other projects and has shown good results. The initial registration was in some cases performed using the auto-alignment option in Cyclone, for which the software calculates the relative positions of the scans automatically on import. The results are never perfect: usually, auto-alignment creates several distinct groups of aligned scans that then have to be registered together. That’s usually the best case scenario – although in some cases Cyclone has been able to successfully auto-align smaller projects (small number of scans, very good overlap). In other cases, however, the auto-alignment is just plain wrong: the software fails to match the correct surfaces together, resulting in a jumble of unrelated scans. For this reason, the constraints created by the auto-alignment process should be checked manually. Note that looking at the statistics of the cloud alignment algorithm is not always enough: sometimes constraints with RMS value <<10mm are actually completely wrong when checked visually. Still, the auto-alignment feature has proven extremely useful especially in larger projects, because it can save considerable amounts of processing time. In some cases, however, (perhaps when overlap between scans was rather low) it made more sense to completely disregard the results of the cloud-alignment and start fresh, as trying to fix the constraints would take more time than creating them from scratch.

The correct procedure for cleaning scans before registration in Cyclone is not as straightforward as one might imagine. This is mostly due to the complicated structure of Cyclone databases; one needs to understand the hierarchy and relationships among ControlSpace, ModelSpace, ScanWorld and Scans. After much experimentation and communication with Leica support, it has been determined that in the latest version of Cyclone (9.1.3), the cloud alignment algorithm works with the point cloud inside the ScanWorld’s ControlSpace, not with the ModelSpace or Default Clouds. However, in the registered ScanWorld, it is the Default Clouds that appear for each individual ScanWorld. Which can be useful in some cases, but also very confusing. Do you clean in the ModelSpace or ControlSpace? And if in the ControlSpace, which ControlSpace?*

*Cyclone creates a “child” ControlSpace for every time the ScanWorld appears in a registration within the database.

As it stands at the moment, the process for cleaning scans is as follows:

  1. In the tree view, go to each ScanWorld’s ModelSpace and clean unwanted objects.
  2. Select the clean point cloud and make it the ScanWorld’s Default Cloud.
  3. Delete point clouds from the ControlSpace(s).
  4. Go back to the clean ModelSpace, select clean cloud and Copy to ControlSpace(s).

This process will ensure that (a) the cloud alignment algorithm will use the clean clouds (from the ControlSpace), therefore improving registration results and (b) any new ModelSpaces created, as well as the registered ScanWorld ModelSpace, will contain only the clean clouds (Default Clouds).


Edinburgh Castle: Great Hall

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The 3D documentation programme for Edinburgh Castle included laser scanning the interior of the Great Hall and adjacent rooms and circulation areas. The data capture phase for this part of the Castle had already been finished, but the scans had not yet been registered. My job was to register 6 P20 and 36 Faro scans (mostly freescans) from the interior of the Great Hall and several adjacent rooms and circulations areas, using Leica Cyclone.

After importing all the scans to a Cyclone database (Faro scans exported as PTX files from Scene – although now Cyclone supports FLS files), it was decided that a certain level of cleaning the scans would improve the results of the cloud alignment algorithm. Only a few targets had been used, so the main registration method was cloud-to-cloud. Using clean clouds for registration was especially important for those scans taken inside the Great Hall, which was at the time open to the public and therefore full of people.

after cleaning
P20 scan inside the Great Hall before cleaning.
before cleaning
Same scan, after cleaning.

The process was time-consuming, but the results justified the effort: the improvement over the results of the cloud optimisation algorithm were significant (RMS error dropping from 0.014 to 0.007 or similar). The auto-add constraints options was used, but some of the constraints added automatically were disabled, as the overlap in some cases was very low (only a few thousand points) and they did not seem to improve the overall registration. Based on the registration diagnostics, the mean absolute error was 0.001 m, but based on visual inspection, measurement, and some control targets, the errors are in fact closer to 3-4mm, which is well within the expected tolerance of the survey.

Cyclone registration diagnostics.

One interesting area covered by the scanning was the “laird’s lug”, a device allowing the lord of the castle to spy on his guests or would-be conpsirators, which consists of an opening on the wall over the fireplace in the Great Hall which is linked through some convoluted tunnels to a room on the third floor, currently used as an office.

lairds lug.jpg
View of the point cloud showing the laird’s lug.

Creating a final registered point cloud for the entire Edinburgh Castle seems like a giant 3D puzzle. The next step for me will be to join the Great Hall interiors to a registered point cloud of the French Prison and underground vaults leading all the way up to the Crown Square, which has already been created in Cyclone).