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Virtual Surveying Using Drones and Mouse Clicks instead of Trucks and Tripods Tom Op 't Eyndt & Walter Volkmann

Drones are ever more often used as complementary survey instruments. These platforms acquire digital aerial images which are processed into elevation and orthophoto coverages. With resolutions of as high as 1cm, these datasets show amazing details. This detail however comes with massive amounts of pixels and point clouds which pose serious challenges to efficient data management and analysis. Virtual surveying provides a bridge between overwhelming point redundancy typical of UAV-based photogrammetric products on the one hand and sparseness of data points which have been intelligently selected during a conventional terrestrial survey on the other hand.

1. Introduction Since 2011 the use of drones for surveying purposes has mushroomed. However a good number of drone operators are discovering that in spite of the tremendous gains in efficiency and economy during the data acquisition phase, the level of effort in processing of the high resolution imagery typically acquired by drones into metric products such as ortho photos and 3D-models is all too often seriously underestimated. An optimum approach to overcoming the impediments of high volume data processing and the subsequent extraction of meaningful geospatial information is thus critical for the economic feasibility of drone based photogrammetric operations.

2. Conventional Surveying When deploying drones for survey applications it is important to know the work processes a conventional surveyor applies to get his job done. Field surveyors intelligently analyse the terrain and select optimal lines and points to correctly and as economically as possible describe the topography in three dimensions. Since this process involves the physical occupation of each such selected point on the terrain, the experienced and skilled surveyor has to find a balance between level of detail (i.e. number of points measured) and quality of final result. Choosing too many points renders a project

uneconomical; choosing too few points leads to lack of completeness and accuracy. This critical selection process relies very much on the tremendous power of the human brain to analyse terrain from eye-level perspectives. One can view the process of intelligently selecting, surveying and coding terrain details in the field as real time or direct vectorization of features that were detected by the eyes of the surveyor. The point is that conventional terrestrial surveys directly produce vectorized features (points such as lamp posts or property corners; lines such as top of ditch, center of road, polygons such as parcels and lakes) which require only minimal post-processing in the office. The post-processing typically involves the more or less automated interpretation of field data codes and the subsequent allocation of vectorized features to layers in a computer aided drafting (CAD) system. In a well-organized conventional terrestrial survey project the field work represents the major part of the effort while the packaging of the geospatial data into a specified deliverable requires only a small proportion of the overall effort. Figure 1 is a typical example of a CAD deliverable produced from conventional terrestrial survey measurements.

3. Virtual Surveying In contrast to conventional surveying as described above, drone-based photogrammetric surveys do


Figure 1 - An example of CAD file resulting from a field survey

not directly produce vectorized features. Instead, the process begins with planned and automated acquisition of aerial images in raster format. These images, which are the equivalent of raw survey measurements in conventional surveying, cover the terrain at more or less homogeneous density (irrespective of the nature of the terrain). The total volume of raw data per unit of terrain surface area thus depends on the resolution of the imagery and

the percentage of image overlap to obtain stereo coverage. Given typical ground sampling distances (GSDs) of a few centimeters, it follows that the volume of raw raster data (the aerial imagery) accumulates rapidly even in small projects of a few hectares. For example, an image with a GSD of 2cm contains 50x50=2500 pixels per square meter of terrain surface area. Photogrammetric processing of the images results in digital ortho photos and elevation models which in turn are substantive collections of primarily raster data of uniform density across the project area. The challenge in drone-based photogrammetric surveying lies in the optimum utilization of the photogrammetric products (i.e. digital ortho photo and terrain model) to efficiently and accurately extract and vectorize features of interest from them. This process not only requires a fluid and dynamic perspective rendering of the photogrammetrically modelled terrain but also a structured and reliable user interface that allows for intelligent and efficient capturing and vectorization of features of interest from the large data volume model. It is this interface which we understand as the door to the world of virtual surveying. Virtual surveying presents the surveyor with an extremely realistic picture, such as shown in Figure 2, of the terrain he would conventionally have to walk over in his effort to capture features. The

Figure 2 - Extract from a detailed virtual environment of a quarry created from aerial images acquired with the use of an Aibot X6 hexacopter, and an Olympus PEN E-P2 camera.


virtual surveyor program used for the projects described in this article allows the surveyor to use a computer mouse for totally effortless navigation across the terrain. Similarly, the mouse is used to “pick up” terrain points in very much the same way as would be done by placing a survey rod over a point in the real world. The embedded dialogue in the virtual survey program allows for all the conventional functionalities normally encountered in CAD and GIS software packages. This includes the creation of layers and allocation of symbols and annotation to individual features. The dialogue also provides for user-friendly editing of captured data. Once features have been captured they can be exported in popular CAD and GIS formats such as dxf or shape files.

better vantage points and to to measure around artifacts and surface objects such as houses, trees, hedges and so on, which are present in UAV data sets (Figure 3) significantly improves the confidence and ease with which the surveyor can identify critical elements for inclusion in the captured feature data set. Equally importantly this dynamic visibility of space in three dimensions provides for easy detection of those areas and details which the photogrammetric process failed to model correctly or accurately. Virtual surveying thus leads to a better and more efficient identification and delineation of the objects that best describe the terrain.

Working in a virtual environment makes it possible to see the image and elevation data at the same time, as in the real world. It has distinct advantages over the typical “heads-up” approach in which features are identified and traced on a (twodimensional) digital ortho photo. Furthermore the incredible “mobility” with which the surveyor can place himself in any location in (and above!) the model space for a better view of a specific detail of interest generally leads to more reliable interpretation of terrain details. In particular, the ability to move around or over obstructions to

For a good understanding of accuracy concepts in small UAV-based surveying it is useful to be aware of the total workflow of a typical UAV mapping project. Generally the workflow consists of the following steps:

Figure 3 - Measuring around surface objects

4. Notes on Accuracy

 Definition of Product Specification – This step includes the coverage area and the desired accuracy to be achieved.  Project Design – The coverage area and nature of terrain will determine which UAV type, fixed or rotary wing, should be used. While fixed wing UAVs are more efficient in covering larger areas, they require the necessary take-off and landing spaces. Vertical take-off and landing (VTOL) vehicles, while less efficient in terms of area coverage, on the other hand are much more deployable in congested and forested terrains. The specified mapping accuracy will determine the corresponding resolution of the aerial imagery from which the terrain is to be modelled photogrammetrically. Generally speaking the accuracy achievable in small UAVbased photogrammetric surveying is expressed in units of pixels in the aerial imagery. For example, if it is assumed that the horizontal and vertical accuracy of a photogrammetric survey is 1 and 2 pixels respectively then imagery with a GSD of 4cm can be expected to yield horizontal positioning accuracy of 4cm and height accuracy of 8cm. The project design also includes the provision of some ground control points (GCPs) which will be used to


reference the survey to a spatial reference frame. The GCP plan includes the approximate number and location of GCPs as well as the size and shape of the targets which will be placed on the mapping terrain prior to capturing the aerial imagery. The size and shape of the GCPs depends on the GSD of the imagery. GCP targets should be of a size and shape that allows for positive identification and accurate centering of the mouse curser over the imaged 1 target .  Flight Planning - The GSD of the aerial imagery will dictate the flying height for any given camera. The flying height also determines the size of the area covered by each photograph. Flight planning thus includes the choice of camera best suited for a given project. Factors such as quality and focal length of the lens, size and resolution of camera sensor and weight of the camera are considered when making the choice for the camera. Once a camera has been chosen the flying height for the project can be determined and corresponding image footprint dimensions will determine flight line and image spacing at given overlap percentages. Flight line spacing, image spacing and the shape of the area to be mapped will then be used to design flight and camera management plans for the UAV.  Obtaining the necessary flight authorizations – This aspect includes the prescribed applications to the relevant authorities as well as obtaining the permission of land owners over whose properties the UAV will be flying in the automatic image acquisition process.  Field Work – Prior to commencing with the image acquisition the GCP targets are placed in their planned positions on the terrain. Flight plans are uploaded to the UAV memory and the automated flights are executed under supervision of the surveyor. Depending on available resources, the survey of the GCPs can be conducted at the same occasion during which the aerial images are acquired. Typically this work is done with geodetic quality RTK GNSS methods and can theoretically be performed while the aerial missions are underway. It is advisable to screen the images for adequate quality and coverage prior to

departing from the site. It often happens that the camera was not switched on prior to takeoff or that the camera battery ran out of energy during a flight. To ascertain whether adequate coverage was indeed achieved it is very helpful to employ a UAV-system that produces georeferences for each image. Such georeferencing can be used on site to determine whether the planned spatial distribution of the captured images was in fact achieved.  Downloading of Data – Image as well as survey data files can either be dispatched by internet from remote sites or physically transported to the computer on which the photogrammetric processing is done.  Photogrammetric Processing – The available software for this step is highly automated. Processing can be done either in-house or in the cloud. For both cases it is necessary to import the imagery and GCP coordinates according to some prescribed procedures. Some processes require georeferenced images, others don’t. The processing requires a massive number of computations and is generally run on high powered computers and in batches. Typical products come in the form of an image file containing an ortho photo and a corresponding digital elevation model.  Virtual Survey – This process starts with the conversion of the photogrammetric products into a specially structured terrain model. The terrain model is then used to present the surveyor with a virtual world of the terrain as well as all the tools to efficiently capture and export geo-spatial data in vectorized form. It is obvious that the above steps contain many variables that contribute to the total error budget of the final product. These include GSD, camera quality, image overlap, GCP number and distribution, GCP coordinate accuracy, terrain texture, terrain profile, operator specific curser centring inaccuracies, radiometric image quality, algorithmic peculiarities in the photogrammetric processing and the effects of re-sampling in the conversion of the photogrammetric products to the virtual model. A detailed treatment of the propagation of each of the many types of errors is beyond the scope of this article. The point we are trying to make is that it is not straight forward to


make a priori statements about the spatial accuracy of a UAV-based virtual survey. For this reason it is advisable to perform some form of quality assurance prior to delivery of virtually surveyed geo-spatial information. The following approach is one example of how a responsible surveyor could provide an objective quality stamp

to his virtual survey product. The model shown in Figure 4 below was photogrammetrically generated from some 330 aerial images with GSDs varying from 2cm for the highest areas and 4cm for the areas at the bottom of the pit.

Figure 4 – Distribution of Ground Control Points (red) and Check Points (yellow) over a terrain model

The red points represent RTK GNSS surveyed GCPs used in the photogrammetric process to spatially reference the model to WGS 84 UTM. The yellow points represent so called check points that were also surveyed with the use of RTK GNSS. The accuracy of the RTK GNSS determined coordinates of both the GCPs as well as the check points is estimated to be better than 2cm (GNSS rover mounted on 2m survey rod) and is equivalent to what would have been achieved had the survey been done by conventional methods. Using Virtual Surveyor from GeoID the virtual world coordinates of the check points were determined by centring the mouse curser over the respective targets and exporting the coordinates to an ASCII file. The GNSS results could thus be compared to the Virtual Surveyor results as shown in Table 1. A concise and

transparent statement of the positional quality of the virtual survey product could be given by calculating the so-called Approximate Circular Error 2 at 95% confidence level as follows : ACE(95%) = 2.4477*0.5*(RMSE E + RMSE N) = 2.4477*0.5*(0.048+0.027) = 0.092 The vertical accuracy at 95% confidence level VA(95%) = 1.96 * RMSE H = 0.108 Note that the above equations hold for check point samples of a minimum of 20 points. The above results show that the accuracy estimates are close to 2 pixel (8cm) and 3 pixels (12cm) for horizontal and vertical positions which we use as a rule of thumb.


POINT 2 3 4 5 7 8 9 10 26 27 28 29 30 42 43 44 45 70 71 72

KINEMATIC GPS E N 543377,64 5672984,76 543318,94 5673055,94 543221,08 5673024,70 543190,65 5673088,67 543241,88 5673166,37 543207,05 5673134,58 543202,72 5673135,25 543187,21 5673106,37 543012,02 5673300,41 543002,56 5673307,98 542990,10 5673251,34 542955,99 5673250,78 543000,25 5673206,09 543095,06 5673009,80 543111,31 5673098,48 543099,72 5673165,06 543194,74 5673211,19 542981,24 5673214,81 542970,76 5673150,83 542997,25 5673132,60

H 454,54 457,14 455,90 457,22 455,80 450,15 449,91 450,89 478,04 477,51 479,03 480,85 478,86 447,43 435,88 425,70 429,50 486,86 486,44 485,33

VIRTUAL SURVEY E N 543377,66 5672984,78 543318,97 5673055,91 543221,09 5673024,71 543190,70 5673088,65 543241,98 5673166,36 543207,06 5673134,54 543202,75 5673135,26 543187,25 5673106,36 543012,03 5673300,34 543002,56 5673307,96 542990,06 5673251,33 542955,94 5673250,82 543000,17 5673206,09 543095,06 5673009,81 543111,28 5673098,50 543099,71 5673165,11 543194,81 5673211,16 542981,16 5673214,82 542970,73 5673150,87 542997,17 5673132,60

H 454,52 457,20 455,95 457,25 455,87 450,22 449,92 450,91 478,02 477,53 478,99 480,80 478,78 447,41 435,83 425,71 429,62 486,78 486,39 485,26

dE 0,02 0,03 0,02 0,04 0,10 0,01 0,03 0,04 0,01 0,00 -0,04 -0,05 -0,08 0,00 -0,03 -0,01 0,07 -0,08 -0,03 -0,07

DIFFERENCES dN 0,02 -0,03 0,01 -0,02 0,00 -0,03 0,01 -0,01 -0,06 -0,02 -0,01 0,04 0,00 0,01 0,02 0,05 -0,03 0,00 0,04 0,00

dH -0,02 0,05 0,05 0,03 0,07 0,07 0,00 0,02 -0,01 0,03 -0,04 -0,04 -0,08 -0,03 -0,04 0,01 0,13 -0,08 -0,06 -0,06

RMSE:

0,048

0,027

0,055

Table 1 – Errors calculated at Check Points to determine Position Quality of Model

To further describe the position quality of the virtual survey, the displacements between GNSSderived and Virtual Surveyor derived coordinates could be shown as suitably scaled error vectors on an ortho photo as illustrated in the Figures 5 and 6. The yellow lines represent the horizontal errors while the green and red lines (green for positive, red for negative) represent the vertical errors scaled up by a factor of 1000. Such a graphic display helps significantly in the detection of systematic patterns that could possibly indicate weaknesses in the overall adjustment process. In this particular case the error pattern suggests regional correlation with the nearest GCP – perhaps indicating that more GCPs would improve the result.

Figure 5 – Horizontal Errors at Check Points scaled by a factor of 1000.

Figure 6 – Vertical Errors at Check Points scaled by a factor of 1000.

5. Survey quality The method of assessing accuracy as described above is based on a point by point analysis. This method is most familiar to traditional field surveyors whose position accuracy expectations are typically very high. It must be pointed out however that such distinct point to point analyses disregard the benefits of aggregating a large number of points with lesser accuracy to describe a surface. A comparison of the green and red lines in Figure 7 below illustrates this concept. The red line represents a typical profile as would be generated by surveying in the real world a distinct number of terrain points. The green line represents a profile


Figure 7 – Profiling discrepancies resulting from terrain point sparseness in conventional methods.

generated in Virtual Surveyor which fully exploits the terrain point density in the virtual model. You will notice that the green line much more closely represents the shape of the actual profile than the red line. This ability in virtual surveying to force profile lines (or areas, for that matter) to “hug” the terrain at specified terrain point density outweighs the disadvantages caused by the random errors in the terrain point positions defining a photogrammetrically generated terrain model.

Sirius drone from MAVinci was used to cover three sections of a motorway interchange in Belgium with vertical aerial stereo imagery of a resolution of 4cm GSD. Together with 10 accurately surveyed GCPs this imagery was used to construct a virtual terrain model with 10cm accuracy as shown in Figure 8 below. The complete design of the sound barriers could be accomplished in this virtual model.

6. Case stories Obviously UAV-based photogrammetric surveys are limited to “as built” or “as is” mapping projects. For UAVs to be used in demarcation tasks, such as the setting of property corners, their payload capacities and navigational accuracies would have to be improved. Hanece, as a rule of thumb any project where data density prevails over data accuracy is a viable UAV-application candidate. Virtual surveys building on drone data were executed for plantation design, cement factory design, hydro-electricity plant design, quarry surveys, quarry remediation planning, road design, environmental impact studies, communication assignments, and many more. We will describe 2 successful case studies on which a Virtual Survey is applied. In a first project a design of a sound screen was realized using drone acquired aerial imagery. A

Figure 8 - Virtual survey to support design of sound screens


In a second project a stretch of 250m of urban street was surveyed for rehabilitation purposes. Because the environment was urban a VTOL (Aibot X6 from Aibotix) was used to acquire stereo imagery as shown in Figure 9 below. The process of planning and executing the aerial image acquisition phase of this project took one person less than two hours. Using some 120 of the acquired images (GSD 1cm) the photogrammetric processing was completed overnight. Virtual surveying could thus commence within less than 24 hours after the image acquisition. Figure 9 shows an extract of virtual surveyed vector features which typically get captured in the field by a crew of three.

weather can be controlled at will by adjusting the climate controls in the office. It is also the safest way to acquire data in dangerous environments such as mine pits, along unstable slopes or in vehicle traffic. And in case an item was overlooked, the virtual world can always be called up again to complete or correct the survey. But as with each new technique some initial investments are required. Not only in material but also to climb the learning curve. However, we believe this is quite limited with regard to virtual surveying. To become a virtual surveyor only requires a small investment in software and training. You remain - as before - a surveyor and will continue to apply your surveying expertise in the virtual environment. As in the field, you will still search for breaklines and measure points. Getting acquainted with the Virtual Surveyor software is as easy as learning how to use a new version of a GNSS data collector. Add to that some software costs and you are off in the virtual world for less than ten percent of an average priced UAS system. Virtual surveying is a productivity improvement technology which will ensure that the investment in your UAS will yield positive returns.

8. Biography of the Authors

Figure 9 - Virtual survey of road infrastructure

Tom Op 't Eyndt is the owner and manager of GeoID in Belgium. Graduated as a bio-engineer in land and forest management from Leuven University, he executed a series of consultancy jobs in the earth observation market. He established GeoID in 2005 and focuses on geographic visualization technology to support a better management of our environment.

7. Benefits and costs Virtual surveying is not applicable to the entire spectrum of survey jobs but has a clear number of benefits when applied to the appropriate project. It implies significant cost savings as one can measure considerable more points and lines in the same time window. Moreover, in the virtual world transportation logistics are no hindrance and the

Walter Volkmann is a land surveyor with roots in Southern Africa. He is the founder and CEO of Micro Aerial Projects in the US. He focuses on geo-spatial applications of UAV-based mapping operations.


Contact information  

Contact Tom at opteyndt@GeoID.eu or http://www.GeoID.eu. Address: Interleuvenlaan 62, B-3001, Leuven, Belgium Contact Walter at walter@unirove.com or http://www.microaerialprojects.com/. Address: 4509 NW 23rd Avenue Suite 8, 32606, Gainesville, Florida, USA

Additional material Interesting material to be explored are the following movies on YouTube:  

Virtual Surveying for quarry management: http://www.youtube.com/watch?v=I1ZvTdHa8RY Virtual Surveying for road management: http://www.youtube.com/watch?v=7CBgZAGYyBA

Foot notes 1

An alternative method of accurately referencing the survey to a given spatial reference network is to accurately fix the camera positions by means GNSS methods. This approach requires a multi frequency geodetic GNSS receiver that is mounted on the UAV and connected to the camera. The receiver has to be able to record raw phase GNSS measurements as well the camera exposure events. Camera positions can then be computed during after flight post processing and used as control points to reference the photogrammetric model to a given spatial reference frame. The precise positioning of the camera exposures in this approach requires the use of GNSS reference station observations for differential corrections. Hence either a refrence station has to be set up and run during the course of the aerial mission, or,if a high accuracy positioning service is available in the area of the survey, reference data from a virtual reference station (VRS) can be downloaded from the internet after the aerial mission is completed. 2

See pp 991-992 Manual of Photogrammetry Fifth Edition ASPRS


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