Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tidf20 Download by: [89.74.31.90] Date: 19 September 2017, At: 11:19 International Journal of Image and Data Fusion ISSN: 1947-9832 (Print) 1947-9824 (Online) Journal homepage: http://www.tandfonline.com/loi/tidf20 Distance to neighbour calculations among OBIA primitives as an innovation to urban mapping techniques R. de Kok, P. Wężyk, B. Hejmanowska & J. Książek To cite this article: R. de Kok, P. Wężyk, B. Hejmanowska & J. Książek (2017): Distance to neighbour calculations among OBIA primitives as an innovation to urban mapping techniques, International Journal of Image and Data Fusion, DOI: 10.1080/19479832.2017.1375029 To link to this article: http://dx.doi.org/10.1080/19479832.2017.1375029 Published online: 13 Sep 2017. Submit your article to this journal Article views: 12 View related articles View Crossmark data
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Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=tidf20
Download by: [89.74.31.90] Date: 19 September 2017, At: 11:19
Distance to neighbour calculations among OBIAprimitives as an innovation to urban mappingtechniques
R. de Kok, P. Wężyk, B. Hejmanowska & J. Książek
To cite this article: R. de Kok, P. Wężyk, B. Hejmanowska & J. Książek (2017): Distance toneighbour calculations among OBIA primitives as an innovation to urban mapping techniques,International Journal of Image and Data Fusion, DOI: 10.1080/19479832.2017.1375029
To link to this article: http://dx.doi.org/10.1080/19479832.2017.1375029
Distance to neighbour calculations among OBIA primitives asan innovation to urban mapping techniquesR. de Koka, P. Wężykb, B. Hejmanowskac and J. Książekc
aProGea, Kraków, Poland; bLaboratory of Geomatics, Institute of Forest Resource Management, Faculty ofForestry, University of Agriculture, Kraków, Poland; cFaculty of Mine Surveying and EnvironmentalEngineering, Department of Geoinformation, Photogrammetry and Remote Sensing of Environment, AGHUniversity of Science and Technology, Kraków, Poland
ABSTRACTThe intuitive notion of urban fabric is related to the type ofbuildings and their proximity. Mapping the density of urban fabricby proxy, using the vegetation index, is considered standard. Theclassification of urban fabric can also be achieved using the infor-mation of all individual buildings. By using the volume and mea-suring their distance to all other image objects in the map, a newurban density map is made. This requires all building volumes inthe city and fusing such data with remote-sensing imagery. Thedistance calculation of over 1.3 million segments in 6 categories,results in a new map (of Cracow) with urban density and iscompared to the existing urban fabric map. The NormalizedDifference Vegetation Index and the commercial off the shelf(COTS) roadmap are traditionally recommended for urban map-ping. But COTS data can, at this stage, not be automated. Themass processing of the distance to categorical buildings, withobject-based image analysis (OBIA), can be automated instead.The OBIA classification requires image–object fusion for the trans-fer of attributes in various levels of aggregation. This procedurecan be applied in extending the manuals and it serves as aninnovation for the standard urban map procedure.
ARTICLE HISTORYReceived 8 March 2017Accepted 24 August 2017
1. The background of perception and general practices in urban mapping
1.1. Urban fabric by proxy
The urban fabric is described in terms of ‘dense’, ‘less dense’, ‘continuous’ and‘discontinuous’. These classifications are usually applied in projects such as Corineand Urban Atlas (UA), (European Union 2006). The driving factor for the urbanfabric classification is the layer of ‘soil sealing’ and the defined ‘texture’ (Bossardet al. 2000, European Union 2006). The degree of imperviousness can be estimatedwith proxy parameters that quantify the cover of green vegetation, which can beconsidered inversely correlated with the degree of surface imperviousness(Gangkofner et al. 2010). The surface imperviousness plays a role in classifyingdense and less dense urban fabric according to the manual (European Union2006). Here, dense urban fabric with strong imperviousness lacks large active
vegetation, and therefore shows low Normalized Difference Vegetation Index (NDVI)values.
1.2. Classifying building by type
This study describes a method to map urban density without relying on the availability ofthe red and the infrared spectral channels. Although the manual for UA explicitly states thatbuilding type should not play a role (European Union 2006), it is the intuitive and historicallydeveloped human perception on conventions in the topographic map that shows the roleof building type. The convention is more than a century old. Hachure and symbolicfootprints of characteristic buildings are used in historical urban maps (compare KDR±1850). The choice of a remote-sensing-based UA classification, according to existingmanuals (Bossard et al. 2000), breaks with this historical convention. The description ofusing texture in this manual only gives an indication for urban mapping but lacks furtherdetailed descriptions. An extension on urban mapping theory can be found in urban sprawlanalysis (Schneider and Woodcock 2008, Jaeger et al. 2010). Although urban metrics are adeveloped field of study (Jaeger et al. 2010), the most recent hardware and softwarebreakthroughs allow to work with massive amount of data that might influence a newapproach or allow the re-evaluation of the developed metrics. The innovation presentedhere lies in the optimal use ofmost recentmass calculations on image objects and less in thecomplexity of the chosen algorithms. In addition to the selected algorithm, an object-basedimage analysis (OBIA) strategy is developed for the process flow. This hierarchy allows tochange the attributes from fused and classified image objects to a lower hierarchical level ofobject primitives in a correct sequence, overruling conflicting classification conditions by asequence of priority classes.
For this study, we remain close to the UA categorisation (European Union 2006) and usethe EU guideline in the initial accuracy analysis steps. This reference set from UA is based onthe NDVI and commercial off the shelf (COTS) data. The classified urban areas, based onOBIA, are using Euclidian distance of all image objects to categorical buildings. The core dataset used in this study is based on a very large data set that allows the extraction of the sizeand height of all individual buildings within the extended city environment of Cracow. Thiscovers 400 km2 with LiDAR and WorldView II imagery (see Section 2.1).
There are however different data sets for retrieving the height of individual buildings;besides LiDAR data, that goes back till end of twentieth century, classic photogram-metric imagery can provide such information (Avery and Berlin 1992). This data has theadvantage that it goes back till the end of the nineteenth century. The latter implies are-evaluation/processing of the archive with robotic vision techniques. A full automaticnormalised digital surface model (nDSM) extraction, after digitalisation of the completearchive, is an option. Such trends of large-scale digitalisation of archives can be seen inother fields (Weller et al. 2007) and considered as an example. Further, the data sets fromearly remote sensing (Corona stereo data ±1963, Keyhole, ±1978) and more recent SPOT(since 1986) and Cartosat_P6 (2006) all provide urban stereo data for extracting catego-rical nDSM height. Most recent, UAV can be considered, but covering a complete cityarea is still a challenge for this type of data acquisition. The latter is also facing legalrestrains (keeping eye-of-sight on the flightpath).
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1.3. Extracting building clusters
The level of detail in cadastral maps deviates from satellite-based urban mapping.Details on individual buildings in cadastral maps (scales 1:5000 and 1:1000) appear inthe form of the building footprints. The visualisation of the cadastral map only hints atthe inherent grouping of neighbouring buildings with similarities in footprint. Betweenurban mapping for international use based on satellite data and the cadastral maps forlocal use based on surveying, there are huge differences in the budget, productiontimeline, users and mapping techniques. The aim is to move the detailed informationderived from grouped individual buildings up to the level of 1:25,000 (trans) nationalscale, making it comparable to the mapping scale of Corine and UA. This requires datafusion from different sources.
In the cadastral map as well as in the UA manual, the buildings are presented asfootprints and/or regarded as texture (Bossard et al. 2000). The correct clustering ofbuildings is essential for our classification strategy. An existing algorithm for listingbuildings as a cluster can be based upon Blondel’s algorithm (Rotundo 2016) but thisis not enough, as it lacks an area description. Within OBIA, the list with buildingclusters can be extended to an area description or closed polygon in hectares orsquare metres. This area description defines a neighbourhood with dense or lessdense buildings.
Therefore, we split this study in an example with synthetic data from the manual andtransfer the developed OBIA algorithm towards big data over the city of Cracow. For thebig data that uses a very small set of large buildings and a large amount of small imageobjects, the visualisation reveals that the OBIA analysis is organised as a cellular structurewith similarities to the Thiessen polygon algorithm. There is a larger theoretical back-ground on the grouping of image elements. The produced results of this study find theirtheoretical backgrounds related to the Thiessen polygons, Blondel’s algorithm (Rotundo2016) and Marr’s ‘primal sketch’ (Marr 1982) to mention a few. These are all mathema-tical approaches to the curious organisation of the human brain that allows a correctgrouping of elements from image information. The theory of both the human brainorganisation (Marr 1982) and the efforts to approach it with algorithms is still a matter ofongoing study. The EU manuals simply pose the symbolic city representation in the mapas texture, without any further discussion. In this study, we present a valid grouping ofcity density areas, based upon OBIA algorithms, realising that the theoretical back-ground is a debated field. In order to judge the validity of the presented results, inthe confrontation of OBIA with an UA reference, the similarities can be calculated. Thecalculated deviations can only be judged invalid if the position of the reference is clear.We consider this a matter of discussion regarding the incomplete theoretical back-ground. A selected theoretical background is used to clarify our strategy decisions inthe set-up of a synthetic OBIA example and a consequential big data analysis over thecity of Cracow.
After demonstrating the synthetic and real data analysis, the presented study canfunction in a discussion on the relevance of urban maps, derived directly from historicaldata. This implies a reprocessing of archives by computer vision techniques. The classi-fication results can also serve as an alternative to overcome the problems related toanalysis of urban change detection. The analysis could further be applied in the near
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future for a full automatic tagging on existing UA polygons that might require anupdate.
1.4. Perception
Urban fabric is classified by a proxy parameter. The measurement by proxy does notexplain the real physical density of urban area. There are few conceptual information inthe manuals on this topic and even no information on the ontology of urban fabric.Outside of the manuals, the scientific literature is extensive (Montenegro and Duarte2009).
Starting with a classical figure from the UA mapping guide (European Union 2006), itis Figure 1(a), ‘Input’. This figure can be related to the very early manual on Corinelandcover (Bossard et al. 2000). It is one of the rare illustrations related to the concept ofurban fabric. Now any explicit information on this image is just removed in the 2006 UAversion of the guide, leaving little theoretical basis for the concepts of the urban fabric.
Although Figure 1(a) might seem simple, the perception (Marr 1982) of this inputfigure is complicated. First of all, the classical layout of the historical topographic map at
Figure 1. (a)–(e). Illustrations on the various concepts of map interpretation. (a): computer vision,input value 40% (European Union 2006); (b): a sketch on Figure 1(a) to illustrate the humanperception; (c): historical convention on the topo map around 1850, from KDR 1850; (d): OBIAfeature visualisation on Figure 1(a,e).
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large scale depicts urban areas with individual footprints of selected large buildings. Thesimple Figure 1(a) triggers three elements of human vision. First, we see Figure 1(a) asrepresenting a set of housing footprints. Second, the footprints display different buildingtypes. And third, the human observer interprets the different city districts with small andlarge neighbourhood housing not randomly mixed but organised together in similarclusters. A computer algorithm must take all three conclusions into account. The threehuman observations are possible, due to a century-old convention in historical maps.Figure 1(c) is a snapshot of a nineteenth-century map of Alexanderplaz, Berlin (compareKDR ±1850). Figure 1(c) is related to the convention on how to display the symbol of alarge building with the printing technique of that time. On the contrary, the ‘40%’annotation in Figure 1(a), from the 90th, is a break with that convention. No personwould ever ‘see’ ‘40%’. This ‘40%’ annotation is a computer vision fossil. It only hints atthe value at which the image can be automatically processed, using the most basicimage-processing techniques of counting black and white pixels. With a revolution incomputer vision, the 40% must be diminished to only one of the multitude of statisticalvalues that makes Figure 1(a) accessible to computer processing. A small GedankenExperiment would further clarify the perception on urban areas. If all building footprintsin Figure 1(a) would be randomly shuffled into a new image, the 40% value, the amountof buildings, the building size and a lot of other statistical values would not change forthe computer algorithm’s input. Figure 1(a) visualises an organised structure among thesymbols for building footprints. A computer vision algorithm explicitly needs to expressthis organisation and the non-randomness of the data set. This organisation is expressedin this study by an area classification of the small neighbourhood in the syntheticexperiment and in the six categorical urban areas on the big data of Cracow.
1.5. The primal sketch
A first attempt on perception is achieved using Figure 1(b), which is derived fromFigure 1(a) in limited accordance with the theory on perception used by Marr (Marr1982). In Figure 1(b), an attempt is made to create a ‘primal sketch’ using manuallyadded colours. The primal sketch should make clear which image elements should begrouped together. It is required to make clear how a person ‘sees’ the city and mostimportantly to show where borderlines between various density areas exist. It is thehuman vision that guides the description of what is considered dense as well as thesparse urban fabric.
Computer vision uses algorithms and explicit coding in order to approach the humaninterpretation of the input image. The input Figure 1(a) does not automatically lead to aconclusion that the image can be segmented in urban areas with different densities. Theonly way to judge the correctness of the computer vision result (the left lower area inFigure 1(e), representing small neighbourhood) is the confrontation with the visualinterpretation (the same lower left area, a cluster of small of houses in Figure 1(b)).The visual interpretation relies on the convention applied in Figure 1(c) that usesindividual buildings to highlight the city blocks with more important functions in animplicit way. This is done classically by selecting which footprints are depicted andwhich parts are generalised using hachure/hatching. We are now in need of an
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extension of the manuals with examples of computer protocols that illustrate thecomputer vision processing of the ‘Input’ imagery (see also Appendix 1).
The feature ‘distance to neighbour object of a selected class’ for OBIA is visualised inFigure 1(d), only for the small buildings. Figure 1(d) visualises the result of the selectedalgorithm. It might be a simple algorithm, but the mass application of the calculationsallows for its success.
The distance to neighbours can be assessed with various algorithms, as was testedduring the development of this study. The experiments were compared with theBlondel’s algorithm in MatLAb (Blondel et al. 2008, Rotundo 2016) only for the syntheticdata from the manual. But for the real data, over the city of Cracow, we did not useBlondel, but applied the distance feature inside an OBIA approach using eCognitionV9.0.1. Inside this OBIA software, we calculated all distances among buildings in variousvolumes as well as all other image objects. The OBIA analysis groups all pixels in animage into meaningful segments with their unique identity code. This can then behandled in different layers of hierarchical aggregation (De Kok and Wezyk 2008).
There is an important difference between OBIA and Blondel’s methods in MatLab.Blondel’s algorithm delivers a table of classified buildings according to their distance butonly as a listing of candidates. This gives the number of buildings in each class. OBIAclassifies the whole area data. This area info can be exported as a closed polygoncontaining hectares or square metres that include buildings and their non-buildingneighbourhood. Blondel’s algorithm shows which buildings can be grouped but cannotshow the exact position of the polygon border that should be created in between thebuilding groups. For creating the area definition in OBIA, it requires extended hardwareand software resources, especially when the experiment is applied over a complete city,not with simulated experiments but using data fusion from different sources and real,large data sets, from LiDAR, Satellite imagery and additional GIS info. In addition, theimage objects themselves are subject to various fusion procedures in order to transferattributes of higher aggregation levels to lower level object primitives. After these fusionsteps, the fused image layers are deleted (see Section 2.5). This procedure faces amassive amount of data and can only produce a result within an acceptable timeframe(<24 h), making use of the latest hardware and software options (64 bit environment,Memory of >32 GB RAM, i7 multicore processors).
2. From simulation to real data
2.1. Essential features
The experimental process design that created Figure 1(e) can be applied to real data aswell as the synthetic data. The complete nDSM of Cracow covers 581 km2 with a base of50 cm pixels from WorldView II (October 2012) of which 400 km2 is used in this study(Figure 2). The emphasis on mass data production with LiDAR, applying this distance toneighbour as an OBIA feature, is also mentioned by O’Neil-Dunne (O’Neil-Dunne et al.2016, De Kok et al. 2016, Geobia, proceedings pending). OBIA is not only about creatingobjects from neighbouring pixels (segmentation), it is also necessary to make a formaldescription of how close- and far-related objects exceed their immediate spatial neigh-bours (the spatial arrangement, after Marr 1982). OBIA can define both. The
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neighbouring objects (these are objects of interest) as a result of the segmentation ofpixels in their adjacent neighbour list. This requires a shared border to the next segment.In addition, it defines the neighbourhood. A neighbourhood extends upon the adja-cency of segments, further than only sharing a border. This is achieved by a spatialfusion of image objects at higher levels of hierarchical aggregation. A selected objectwith a higher category can define a neighbourhood of many segments within a distance,where lower category object might only influence the attribute list of segments thatshare a direct border. This results in the urban map in an `area of interest' or `spatialarrangement' of all segments, using the distance of all object primitives to variousbuilding volume categories. This spatial arrangement is a classification of existingsegments. In this study, it is best exemplified by an area definition of ‘Allotments’(Category 20 buildings, see Section 2.4) or ‘Dense Urban Fabric’ (among buildingCategories 40–70), where all objects of interest are buildings and all other objects areartificial squares from a chessboard segmentation using a leaf 25 on 50-cm WorldView IIimagery.
Figure 2 Shows the study area over Cracow, an WVII image of 20 × 10 km, with, forbetter orientation, the castle Wawel along the river Wisla. Figure 3 visualises the samefeature ‘distance to building’ in the exact area of Figure 2, with the same OBIA feature asused in Figure 1(d). This is elaborated in the protocol of Appendix 2. The buildingcategories are derived from a high-quality Fugro_LiDAR campaign 2006 with 12 pointsper metre in the ISOK project (Wezyk et al. 2015), which makes their classificationaccording to volume a trivial matter in OBIA. The total building set of Cracow fromthe nDSM was edited and prepared for the ‘Monit-Air Atlas’ of the city (Bajorek-Zydronand Wezyk 2016) and used as input layer in this study.
There are 114,525 buildings in the 20 × 10 km block of the city of Cracow. They arethe first image objects in the segmentation of the LiDAR data. All other image objectsare considered as non-buildings and are segmented using a chessboard on a 2012WorldView II image (Bajorek-Zydron and Wezyk 2016) with a leaf of 25. The segmenta-tion takes place in a fusion of data sets of different sources and resolutions (LiDAR,
Figure 2. An image of WVII, with the study area of Cracow and the Wawel castle along the riverWisla, the box left lower part, as subset in Figure 8.
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Multispectral WV II 1 m and 50 cm Pan imagery) and are forced by the Pan layer of theWV II image, to maintain a basic minimal resolution of 50 cm per pixel. This results in12.5 × 12.5-m chessboard of object primitives. The centroid of these object primitives isused in all distance calculations. The total OBIA database contains over 1.3 millionobjects. From each image object, six Euclidian distance attributes are written in theobject table. The feature ‘distance to neighbour’ can be visualised for each of the sixbuilding categories. Figure 3 shows the distance of all 1.3 million image objects towardstheir nearest object in the Category 60. If the distance is very small, the value is low andthe display is dark. There are 541 large buildings in Category 60 and 541 ‘Bubbles’. Thesebubbles approach the characteristics of Thiessen polygons if the amount of buildings isvery small compared to all image objects (see Figure 5). The core building volumesderived from LiDAR and the reference data from UA (2006) are from the same period.The new buildings, visible in the WV II image from October 2012 receive a zero volumeand do not affect the overall classification. The time gap between the acquisition of theLiDAR and the satellite imagery does not affect the proof of concept but should beconsidered if the proof of concept is transferred to a production environment. Both theLiDAR-derived volumes and the reference UA map are from the 2006 editions. In orderto prove that building-type matters, contrary to the statement in the manual, thebuilding-type and their volumes from LiDAR should match the reference data set fromUA by covering the same period of time. It is possible to use WV II imagery to map urbansprawl between 2006 and 2012, but for the purpose of this study, that option is hereignored.
2.2. Repetitive patterns
Intuitive and empirical findings on urban mapping support the assumption that similarbuildings are encountered as grouped in city blocks. At least for European conditionsthis is often the case. This hints at the fractal nature of European cities (Tannier andPumain 2005), where repetitive patterns can be observed within city blocks. Buildings
Figure 3. Visualisation of the feature ‘distance to large buildings’ (for Category 60 buildings).
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within a certain category of ‘Building Volume’ can then be expected to have severalneighbours with similar values for ‘Building Volume’. Although this seems to be logic formodern and planned city blocks, the characteristic of similarity for neighbouring build-ings still can be observed among organic/historical growth patterns in European cities.This assumption allows to move from category of buildings, a trivial classification inOBIA, towards an area definition containing similar building categories as well as theirsurrounded area’s (the spatial arrangement). It is now assumed that neighbourhoodswith very large values for ‘Building Volume’ cover the area of dense urban fabric. Viceversa, the buildings with small values for ‘Building Volume’ are associated with sparseurban fabric. These assumptions are necessary when moving from the trivial categoricalbuilding classification (Number of buildings, m3, Volume) towards the OBIA neighbour-hood definitions (in m2, hectares or km2, area definitions). A visualisation of all sixdifferent distances to respective six building categories can be combined in a colourcomposite that is expressed as brightness/darkness in Figure 4. This enhances the areaswith similarities in distance to the same building category, sharing the same colourcombinations. Now these features must be translated to the protocol of the OBIAprocess tree (in Appendix 2).
By singling out certain categories of buildings, essential classification steps can behighlighted and confronted with normal UA data, to highlight the approaches to theaccuracy analysis.
2.3. Thiessen area by proxy
The drape of Thiessen polygons over the Category 60 buildings reveals their conceptualproximity to the Thiessen origin (Figure 5). However, in a real OBIA production environ-ment, the non-building object primitives will not be approaching the here-applied pointdata or small squared objects. The experimental data here is classified in building withvolume and non-building primitives. In an OBIA production environment, the non-
Figure 4. Colour composite of six ‘distance to buildings building classes’. Each of the 1.3 millionobjects has six values for distance to building in six categories.
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building segments will be further differentiated and fused after classification. Thisdifferentiation and object fusion of non-building primitives will then further obscurethe underlying conceptual relation to Thiessen and guarantee that the borderlinebetween two ‘Thiessen cells’ will never be a straight line in any OBIA production.Moreover, Thiessen is normally not a symmetric area, where, in this particular use-case, the basic area is more or less symmetric (circular), due to the choice of segmenta-tion (chessboard with a small parameter or small leaf of 25) for all non-building objects.
Figures 5, 4 and 3 are closely related. Figure 4 visualises the six attributes required for theclassification. Figure 3 visualises the cellular structure created by a single attribute, distance tobuilding, becoming more evident with a few objects and large amount of non-buildingprimitives. This cellular area division is not uncommon as it is related to the Thiessen polygonof Figure 5. By further differentiationof imageobjects in a furtherOBIAproductionprocess, therelationship between a Thiessen area and the distance to neighbour cell structure will be lost.
2.4. From building category to area definition
The next step after categorisation of buildings is the definition of neighbourhoodaround each cluster of building category. Due to the sequence of classifying, with thelargest building category being classified first, the classification of neighbourhood isdominated by the majority of larger buildings in a range. Each building receives acategorical label. Only 6 categories namely Categories 20–70 are used in this study(Category 10, with very small elements, becomes unclassified).
List of volume-based building categories in OBIA:
● Category 70: buildings >125,000 m cubic● Category 60: buildings >50,000 m cubic● Category 50: buildings >12,500 m cubic
Figure 5. Visualisation of Thiessen polygons on top of Category 60 buildings.
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● Category 40: buildings >5000 m cubic● Category 30: buildings >1250 m cubic● Category 20: below 1250 m cubic
The process starts with a rasterised version of edited and cleaned LiDAR point dataderived from the Monit_Air project. The following OBIA processing steps are performed(see Figure 7) as well as described in detail in Appendix 2.
Phase 1: Segmenting building volumes from LiDAR;Phase 2: Segmenting non-building object primitives from WV_II inside a fused layer
with LiDAR building-volumes, Leaf 25 Chessboard.Sequential Phase 3: Classifying Categories 70, 60, 50, 40, 30 and 20.Figure 6 shows a condensed process flow from Appendix 2.
Figure 6. Visualisation of the OBIA classification.
Figure 7. Flowchart of the OBIA process.
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The final classification is dominated by the most relevant amount of large buildingswithin a certain distance. This is secured by following a sequence of area definitionsusing the larger category first and then classifying the smaller building clusters and theirnon-building neighbourhood objects.
After each classification, an aggregation layer fuses the total classified area in a sopra-object level that transfers only the attributes to the lower object primitives. After thistransfer, the aggregation layer is deleted.
Each step adds an additional attribute to an object primitive. If the original Category70 attribution is not overruled by a sequential classification, the final output will giveCategory 70 segments. Attributes can be overruled. In practice, any object that is closeto a Category 70 object can equally be close to a small volume building. There are manykiosks and small shops around high apartment buildings. To assign the correct category,overruling of previous classification can only be done after an additional evaluation. Theproblem of fused image segments is that the attributes of their separate individualmembers cannot be individually changed anymore. To avoid this problem, it is necessaryto allow the overruling of the smallest primitive’s final classification, until all categoriesare evaluated. This is achieved by using and destroying object fusion layers in the sopra-level and use the sopra-level to change attributes in the sub-level. A strict separation ofthe consequences of segmentation, classification and object-fusion should be regardedin designing the sequential strategy of Figure 6. The eCognition OBIA software allowssuch separate handling of image segments in a well-organised process flow. Still, it ispossible to export the total object area that requires an area fusion, after all phases ofclassification have been finalised. Finally, some object cleaning is applied to larger waterbodies and vegetation surfaces. For this cleaning, the infrared and red channel informa-tion is applied from WV II. The multispectral channels are thus not applied to classify thedensity of the urban area, but only to remove large fused image object containing awater body or large vegetated areas over 5 ha.
The output polygon is an area definition with a single final class, created from objectprimitives from a variety of classes.
3. Data confrontation and selected excerpts of the evaluation procedure
3.1. The UA selection
The classification result is now confronted with selected parts of UA (European Union2006). The various classes must be split, because not all urban areas are within the urbanfabric of the reference UA map. Most notably, the class of Allotment gardens shows atypical deviation from the other urban classes and can be used to highlight thecharacteristics of using ‘distance to neighbour’ to define an area in OBIA. The followingconfrontation of the OBIA result with the existing UA data set is based upon selectedparts only. This does not return a full evaluation of the results. Therefore, it also limitsthe amount of conclusions from this incomplete accuracy assessment. As long as a validarea definition can be presented and a valid way to highlight the discrepancies betweenthe two data sets, this limitation is considered with care. It still delivers enough argu-ments and statistics to reconsider the fundamental pillars of urban mapping (both NDVIand COTS data). The selected evaluation and statistics can be used to highlight a strong
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supporting role of OBIA in urban mapping. The chosen strategy and the evaluation areused to highlight the possibilities of an alternative to COTS data that cannot (as yet) befully automated and support arguments to reduce necessity to apply the NDVI, which isonly a proxy parameter and not a direct measurement on urban density.
3.2. The buildings from Category 20
A special category for urban areas is the ‘Allotments’. Extracting this category in OBIA isfavoured by the general absence of larger buildings inside these complexes. The‘Allotments’ are a typical element of the urban landscape and are under influence ofpolitical decisions and their typical historical developments in the larger European citiesduring the nineteenth and twentieth century. ‘Allotments’ are classified here by usingthe Category 20 buildings and their neighbourhoods. The layout of the ‘Allotments’contains only buildings smaller than 1250 cubic metres in close proximity. They are asubset of UA class 14200 ‘Sports and leisure facilities’. To explain the method for theevaluation, first a single polygon and its overlap values with the UA are discussed beforemore details are treated.
3.3. Evaluating statistics for Category 20
In the Category 20 buildings plus the surrounding area, only for Figure 8, the polygon isfalling 94% inside the UA Class 14200. When we take all classified polygons fromCategory 20, we encounter 89% coverage of this classification inside the UA reference
Figure 8. A selected OBIA subset of the Category 20 building and neighbourhood with a polygonover class 14200 UA and WVII panchromatic background. A small subset of Figure 3, the box alongthe Wisla river.
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class 14200. The 89% is an important value in this study. It confirms that it is possible tocreate a coherent urban area class, based on the simplest distance calculation, applied ina massive way. In this study, there are millions of image objects and their attributes areused to assign six categories. This result can be compared and evaluated with a standardUA map. Also, it explicitly does not require any COTS data.
After the first confrontation in the overlap between OBIA results and UA reference, itbecomes possible to develop further on these overlap values, when applying the methodMarinho (Marinho et al. 2012). It is further important to notice that the reference class UA14200 is 68% not covering the ‘Allotments’ but covers sport places and other leisure area.
3.4. Adaptations to the Marinho approach
In accordance to the method Marinho, two points on the trajectory are developed with a10- and 20-metre buffer. By expanding the input/producer results over the referencedata, a buffering trajectory would reveal the spatial relationship between producer andreference data. The buffering can be applied to the total area of Category 20.
There is 68% of UA class 14200 that covers sport and leisure areas outside the overlapwith Category 20 Allotments. This means that only 32% of the UA class 14200 containsAllotments. When the Allotments receive a bufferzone of 10 respectively 20 m inaccordance with the Marinho’s method, the overlap between Allotments and UA14200 increases from the initial 32% to 46% and then to 51%. Although this seems tobe of low values, it is because an increment is achieved, this can only be explained dueto the spatial neighbourhood of the producer’s data with the reference. This allows aconclusion that the OBIA result is not randomly distributed over the image, but main-tains at spatial proximity with UA class 14200.
The overlap and buffering of Category 20 with UA shows the possibility of arriving ata coherent area result for a subset of UA 14200, without NDVI and COTS information andthe results maintain a spatial relationship with the reference UA data.
3.5. Evaluating the larger buildings in Category 30
The building Category 30 is considered to contain single houses, shown in Table 1. Thelarger Categories 40–70 contain large and very large buildings, normally associated withdense urban fabric and industry by a lay person. For simplicity, they are grouped togetherin Table 2. Although the assessment of the OBIA result might be considered incomplete, itis because a large amount of buildings are used (all 114,525 buildings of Cracow), thestatistics highlight a valid result on the existing discrepancies and can be used to functionas a basis to discuss where and how the classical approach of using NDVI and COTS data inan UA map causes problems. Table 1 shows that only 5.2% of medium buildings Category30 (2150–5000 m3) occupy the reference class UA 11210 (Table 1, Cell E:5). The discrepancybetween this value and the visualisation of the OBIA classification (Figure 9) casts doubt onthe accuracy of UA_11210 outside the city centre. It would be expected that Category 30houses occupy a very large part of UA_11210, much larger than 5.2%. Doubt can also begiven to the conflict highlighted by the value in Cell E:3 in Table 1. It is to be expected that31% of Category 30 on top of 11100 might have an undisputed >80% soil sealing (or lessthan 20% NDVI), but it does not mean that inhabitants perceive such areas with Category
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30 buildings as continuous urban fabric. In general terms, a low NDVI value in a city blockcontaining the small volume houses are not perceived by the lay person as a dense urbanfabric. As well as the opposite, of very large buildings with a high NDVI value inside the cityblock are not perceived as sparse urban fabric. It is further remarkable that for Cracow, theCategory 30 Area+Buildings follows the traditional village layouts (Figure 9). This class canalso be used to track historical village relicts within the city and open up the link tohistorical urban developments.
3.6. The statistics on very large buildings in Categories 40–70
Table 2 shows an amount of large buildings of 18.4% (Cell E:3) on UA class 20000. Thismight trigger an update on the UA. Large buildings are perceived as part of 11100 or
Table 1. The results of the confrontation of the Urban Atlas data with the classification for Category+Area 30.
A B C D E1 Code Item Area [m2] Area_all [m2] Share [%]
2 12100 Industrial, commercial, public, military and private units 13,319,760 29,290,105 45.53 11100 Continuous Urban Fabric (S.L. > 80%) 9,089,675 314 12220 Other roads and associated land 2,741,802 9.45 11210 Discontinuous dense urban fabric (S.L.: 50–80%) 1,519,647 5.26 14100 Green urban areas 766,933 2.67 20000 Agricultural + semi-natural areas + wetlands 592,035 28 14200 Sports and leisure facilities 394,419 1.39 13300 Construction sites 380,094 1.310 12230 Railways and associated land 247,262 0.811 13400 Land without current use 140,497 0.512 50000 Water bodies 66,027 0.213 11300 Isolated structures 11,670 014 11220 Discontinuous medium density urban Fabric (S.L.: 30–50%) 9856 015 30000 Forests 7906 016 13100 Mineral extraction and dump sites 2226 0
Table 2. Statistics on the Categories 40–70 buildings and their overlap with UA.A B C D E
1 Area_all [m2] Code Item Area [m2] Share [%]
2 112,934,718 11210 Discontinuous dense urban fabric (S.L.: 50–80%) 49,841,466 44.13 20000 Agricultural + semi-natural areas + wetlands 20,774,337 18.44 11220 Discontinuous medium density urban fabric (S.L.: 30–50%) 11,136,969 9.95 11100 Continuous urban fabric (S.L. >80%) 9,742,833 8.66 12100 Industrial, commercial, public, military and private units 7,571,506 6.77 12220 Other roads and associated land 6,763,288 68 11300 Isolated structures 1,634,102 1.49 14100 Green urban areas 1,566,682 1.410 30000 Forests 947,932 0.811 14200 Sports and leisure facilities 711,470 0.612 13400 Land without current use 693,367 0.613 13300 Construction sites 579,698 0.514 12230 Railways and associated land 393,933 0.315 11230 Discontinuous low-density urban fabric (S.L.: 10–30%) 334,363 0.316 12400 Airports 98,935 0.117 13100 Mineral extraction and dump sites 64,044 0.118 50000 Water bodies 44,458 019 12210 Fast transit roads and associated land 32,380 020 11240 Discontinuous very low-density urban fabric (S.L. <10%) 2955 0
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11210. The 11220 might contain a lot of vegetation (Table 2; Cell E:4, 9.9%), but ifinhabitancy witnesses a lot of Categories 40–70 buildings inside 11220, they still willperceive this as UA 11210 or even as UA 11100 areas.
4. Discussion
Current manuals on urban mapping recommend not to use building type. This is strangebecause building type is an attribute that is much easier to be extracted from data andimage fusion in most recent sensors and processing methodology. Modern develop-ments in sensor technology not only make it easier to deliver building type for acomplete city, also the latest hardware and software technology allows to extractbuilding type from archive data.
It is time to reconsider the self-imposed limitations in the manuals that might havebeen based upon economic factors. It was very expensive, some decades ago, toextract building type for a city. Classifying building type in itself is not an innovation.Mass measurement of a selection of important attributes is considered here aninnovative use of the most recent calculation potentials, made available throughhardware and software developments in the last 3 years. The presented study couldnot have been done in the pre-64 bit software environment with less than 12 GB Rammemory.
The main aim of the study then delivers arguments to consider a valid alternative forusing NDVI and COTS data in urban mapping. Moreover, it is clear that COTS datacannot be presently automated but building-type classification can be.
For over 1.3 million image objects, the distance to six building categories arecalculated. These six Euclidian distances as OBIA attributes are then used in a sequentialclassification to design areas of urban density, based upon the dominant building type.
Figure 9. In dark; the aggregated building Categories 40–70 and in bright area+buildings ofCategory 30. In polygon overlay, Urban Atlas 11 210.
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A single distance of an image object towards its nearest building category is a simpleEuclidian distance measurement. The presented OBIA approach even exceeds theBlondel’s algorithm that measures only the building objects but not the non-buildingimage objects inside the neighbourhood.
By measuring not only the building objects but all image objects, it is now possible toconstruct an area in hectares or square metres. This is more than a categorical classifica-tion of only the buildings. It is an urban area classification, based on density derivedfrom the huge amount of simple calculated statistics that show their effect in their sheermass of the calculations. With the ability to fuse LiDAR volume information with satelliteimagery, the extraction of the complete building volume of the city as well as all non-building image objects allows to use the potential of these data sets. It can be turnedinto a classified urban density map.
The confrontation of the OBIA mapping results with the UA reference data high-lighting potential conflicts that underlie the assumptions of sealed area mapping andthe relationship between dense and sparse urban fabric. It is not immediately neces-sary to make a complete assessment on user and producer accuracy or even com-plete the Marinho approach to spatial data evaluation, in order to come up withsome essential discrepancies between the experimental data and the standard UAmaps. Even this partial evaluation already reveals a conflict potential in the existingUA mapping results. Category 20 mapping shows that the OBIA approach leads to avalid area descriptions of a build-up area category and can be achieved automatically.It shows that OBIA is a valid tool for creating functional mapping units for UA areadefinition, without using the recommended NDVI or COTS data. Innovation in themanuals of urban mapping should consider strategies, which allow for further auto-matic processes.
The OBIA classification of Category 30 buildings highlights the lack of single-housebuildings in the UA 11210 class and demonstrates the discrepancy between UA map-ping techniques and the public perception. The large amount of Categories 40–70 in UAclass 20000 reveals a necessity for an update of the UA maps. It also highlights the roleof an automatic alarm function of the OBIA results, to use as a flag for outdated areaswithin the UA.
All the produced statistics deliver enough points for arguing the consideration of analternative approach to traditional UA mapping techniques. The existing manuals are inneed of innovations and a more profound theoretical backing (like the ontology ofurban mapping classes). The argument that the existing manuals are constructed on asound and successful basis in the past and now, it is the time for consolidation,continuation and stability, might be valid, but this risks that the innovative techniquesof the last few years are kept at bay, especially, innovation in the sense of making use ofthe full capacity for number crunching at desktop computers encountered in the last3 years.
This study allows a basic set of arguments to discuss further the budget allocationsfor UA mapping updates and the necessity of a more extensive pilot. This should exceedthe presented experimental results. Both Lidar-based OBIA classification and a complete(Marinho based) assessment require a much larger budget and timeframe than could beallocated to the presented experimental assessment of OBIA-based UA classification.
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An interesting concept is the study of Weller et al. (2007), which could lead to a re-evaluation of the archives. When full automatic procedures are developed, the old mapitself becomes less important than the core data that are used for producing thesemaps. Instead of the ±1930 Cadastre map, a direct nDSM and a building extraction cantake place on the stereo photogrammetric archive, for a reliable statistic on urbanchange. This removes the problems of post-classification comparison with historicalUA layers and focus directly on the data comparison itself.
5. Conclusions
The classification of areas, grouped by similar distance to building-type, offers at theleast a signal, or acts as a flagging procedure, to highlight polygons in UA that requirean update. The present availability of high-quality LiDAR on European cities challengesthe role of the NDVI and COTS data on European UA-mapping techniques. Automaticclassification on geodata will be a must-have situation in the face of big data thatbecomes now available using modern sensor techniques. The existing manuals on UrbanMapping require an urgent update and should be open to the introduction of recentinnovations.
Disclosure statement
No potential conflict of interest was reported by the authors.
References
Avery, T.E., and Berlin, G.L., 1992. Fundamentals of remote sensing and airphoto interpretation. 5thed. New Jersey: Prentice-Hall.
Bajorek-Zydron, K. and Wezyk, P., ed., 2016. Atlas pokrycia terenu i przewietrzania Krakowa, MonitAir, EEA grants and Progea consulting. Available from: http://umk-gd.maps.arcgis.com/apps/MapSeries/index.html?appid=81480b0233ab4163a376936410be9064.
Blondel, V.D., et al., 2008. Fast unfolding of communities in large networks. Journal of StatisticalMechanics: Theory and Experiment, 2008, P10008. Article ID: P10008. doi:10.1088/1742-5468/2008/10/P10008
Bossard, M., Feranec, J., and Otahel, J., 2000. CORINE land cover technical guide addendum 2000.European Environment Agency Technical Report 40. Available from: http://www.eea.europa.eu/publications/tech40add.
De Kok, R. and Wezyk, P., 2008. Principles of full autonomy in image interpretation. The basicarchitectural design for a sequential process with image objects. In: T. Blaschke, S. Lang, and G.J.Hay, eds. Object-Based Image Analysis – Spatial Concepts for Knowledge-Driven RemoteSensing Applications. Lecture Notes in Geoinformation and Cartography, 18. Springer Berlin/Heidelberg, Germany.
De Kok, R., et al., 2016. Replacing the use of texture and sealed area in urban fabric classifications byintegrating volume and object based distance calculations, extended abstract,GEOBIA 2016: 14September - 16 September 2016, University of Twente Faculty of Geo-Information and EarthObservation. Available from: https://www.geobia2016.com/wp-content/uploads/2015/04/GeobiaProgramme.pdf.
European Union, 2006. Mapping guide for a European urban atlas. Available from: http://www.eea.europa.eu/data-and-maps/data/urban-atlas.
Gangkofner, U., Weichselbaum, J., Kuntz, S., Brodsky, L., Larsson, K., and De Pasquale, V. 2010.Update of the European high-resolution layer of built-up areas and soil sealing 2006 withimage2009 Data. 30th EARSeL Symposium 2010, Paris, France, 31 May-3 June, 185–192.
Jaeger, J.A.G., et al., 2010. Urban permiation of landscapes and sprawl per capita: new measures ofurban sprawl. Ecological Indicators, 10, 427–441. doi:10.1016/j.ecolind.2009.07.010
KDR, ±1850. Karte des Deutschen Reiches, see also; 1893 (map of the German empire) detail of sheet269 Berlin Karte des Deutschen Reiches 1893 Available from: http://www.davidrumsey.com/blog/2011/4/10/karte-des-deutschen-reiches-1893.
Marinho, E., Fasbender, D., and De Kok, R., 2012. Spatial assessment of categorical maps: aproposed framework. In: Proceedings of the 4th GEOBIA. Rio de Janeiro, Brazil: São José dosCampos: INPE, 602–607.
Marr, D., 1982. Vision: a computational investigation into the human representation and processing ofvisual information. San Francisco, CA: W. H. Freeman.
Montenegro, N. and Duarte, J.P., 2009. CUMINCAD computational ontology of urban design: towardsa city information model, computation: the new realm of architectural design. Turkey: IstanbulTechnical University.
O’Neil-Dunne, J., MacFaden, S., and Ahles, N., GEOBIA, 2016. Massive dataset processing, in; solu-tions and synergies., 14 September - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation. Available from: https://www.geobia2016.com/wp-content/uploads/2015/04/GeobiaProgramme.pdf.
Rotundo, G., 2016. Organization of knowledge in large datasets: clustering algorithms and strategies.Available from: http://knowescape.org/wp-content/uploads/2016/07/STSM-report-v25lug.pdf.
Schneider, A. and Woodcock, C.E., 2008. Compact, dispersed, fragmented, extensive? A Comparisonof Urban Growth in Twenty-Five Global Cities Using Remotely Sensed Data, Pattern Metrics andCensus Information Urban Studies, 45 (3), 659–692.
Tannier, C. and Pumain, D., 2005. Fractals in urban geography: a theoretical outline and an empiricalexample. Cybergeo: European Journal of Geography, document 307. doi:10.4000/cybergeo.3275
Weller, L., Becker, T., Archinal, B., Bennett, A., Cook, D., Gaddis, L., Galuszka, D., Kirk, R., Redding, B.,and Soltesz, D. 2007. USGS lunar orbiter digitization project: updates and status, LPS #2092abstract for lunar and planetary science XXXVIII. Astrogeology Team, U.S. Geological Survey,Flagstaff, AZ. Available from: https://astrogeology.usgs.gov/Projects/LunarOrbiterDigitization/.
Wezyk, P., et al., 2015. Podręcznik dla uczestników szkoleń z wykorzystania produktów LiDAR.Warschau: ISOK.
Appendices
OBIA process tree extracts.
Appendix 1
Protocol for OBIA on synthetic BMP map (input, UA mapping guide, Figure page 8, EuropeanUnion 2006);
LowerLeveldoassign class: with Brightness <1 at BaseA: Buildingimage object fusion: loop: Building at BaseA:: Buildingassign class: with Brightness <1 at BaseA: Buildingassign class: Building with Area ≤300 Pxl at BaseA: Smallassign class: Small with Area ≤10 Pxl at BaseA: unclassifiedassign class: Building with Area ≤850 Pxl at BaseA: Mediumassign class: unclassified with Distance to Medium ≤50 Pxl at BaseA: Area_Mediumassign class: unclassified with Distance to Small ≤35 Pxl at BaseA: Area_Smallassign class: Area_Medium, unclassified with Distance to Small ≤15 Pxl at BaseA: Area_Small
HigherLeveldocopy image object level: at BaseA: copy creating ‘LevelB’ aboveimage object fusion: loop: Area_Small, Small at LevelB:: Area_Smallassign class: Area_Small with Number of sub objects Small (1) ≤20 at LevelB: unclassifiedassign class: Area_Small at LevelB: Area_small_Bassign class: Area_Small with Existence of super objects Area_small_B (1) = 1 at BaseA: Goal1assign class: Area_Medium, Area_Small with Existence of super objects Area_small_B (1) <
1 at BaseA: unclassified
Appendix 2
Protocol for WV II imagery and Lidar-derived nDSM (data; Bajorek-Zydron and Wezyk 2016).Classes: Area_20, Area_30, Area_40, Area_50, Area_60, Area_70, B_20, B_30, B_40, B_50, B_60,
do multi-threshold segmentation: creating ‘Sub_A’: unclassified ≤ 0.1 < Build on LidarBuildingschessboard segmentation: unclassified at Sub_A: chess board: 25assign class: unclassified with ZabudFull ≤ 100 at Sub_A: Out
B70assign class: Build with Volume 2 ≥ 50 at Sub_A: B_70copy image object level: at Sub_A: copy creating ‘Base_A’ aboveassign class: unclassified with Distance to B_70 ≤ 750 Pxl at Base_A: Area_70image object fusion: loop: Area_70, B_70 at Base_A:: Container_2image object fusion: loop: Container_2 at Base_A:: Container_2assign class: Container_2 with Number of sub objects B_70 (1) ≤ 2 at Base_A: unclassifiedassign class: unclassified with Existence of super objects Container_2 (1) ≥ 0.9 at Sub_A: Area_70delete image object level: delete ‘Base_A’Urb_1802Ddomulti-threshold segmentation: creating ‘Sub_A’: unclassified ≤ 0.1 < Build on LidarBuildingschessboard segmentation: unclassified at Sub_A: chess board: 25
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doassign class: unclassified with ZabudFull ≤ 100 at Sub_A: Out
B70assign class: Build with Volume 2 ≥ 50 at Sub_A: B_70copy image object level: at Sub_A: copy creating ‘Base_A’ aboveassign class: unclassified with Distance to B_70 ≤ 750 Pxl at Base_A: Area_70image object fusion: loop: Area_70, B_70 at Base_A:: Container_2image object fusion: loop: Container_2 at Base_A:: Container_2assign class: Container_2 with Number of sub objects B_70 (1) ≤ 2 at Base_A: unclassifiedassign class: unclassified with Existence of super objects Container_2 (1) ≥ 0.9 at Sub_A: Area_70delete image object level: delete ‘Base_A’
B60doassign class: Build with Volume2 ≥ 20 at Sub_A: B_60copy image object level: at Sub_A: copy creating ‘Base_A’ aboveassign class: Area_70, unclassified with Distance to B_60 ≤ 250 Pxl at Base_A: Area_60image object fusion: loop: Area_60, B_60 at Base_A:: Container_2image object fusion: loop: Container_2 at Base_A:: Container_2assign class: Container_2 with Number of sub objects B_60 (1) ≤ 2 at Base_A: unclassifiedassign class: Area_70, unclassified with Existence of super objects Container_2 (1) ≥ 0.9 at
Sub_A: Area_60assign class: loop: Area_60 with Rel. border to Area_70 ≥ 0.2 at Sub_A: Area_70delete image object level: delete ‘Base_A’
B50doassign class: Build with Volume2 ≥ 5 at Sub_A: B_50copy image object level: at Sub_A: copy creating ‘Base_A’ aboveassign class: Area_60, Area_70, unclassified with Distance to B_50 ≤ 100 Pxl at Base_A:
Area_50image object fusion: loop: Area_50, B_50 at Base_A:: Container_2image object fusion: loop: Container_2 at Base_A:: Container_2assign class: Container_2 with Number of sub objects B_50 (1) ≤ 2 at Base_A: unclassifiedassign class: Area_60, unclassified with Existence of super objects Container_2 (1) ≥ 0.9 at
Sub_A: Area_50assign class: Container_2 with Number of sub objects B_50 (1) ≤ 8 at Base_A: unclassifiedassign class: Area_70 with Existence of super objects Container_2 (1) ≥ 0.9 at Sub_A: Area_50delete image object level: delete ‘Base_A’
dodo
B40doassign class: Build with Volume2 ≥ 2 at Sub_A: B_40copy image object level: at Sub_A: copy creating ‘Base_A’ aboveassign class: Area_60, Area_70, unclassified with Distance to B_40 ≤ 80 Pxl at Base_A: Area_40image object fusion: loop: Area_40, B_40 at Base_A:: Container_2image object fusion: loop: Container_2 at Base_A:: Container_2assign class: Container_2 with Number of sub objects B_40 (1) ≤ 1 at Base_A: unclassifiedassign class: Area_60, Area_70, unclassified with Existence of super objects Container_2 (1) ≥ 0.9
at Sub_A: Area_40delete image object level: delete ‘Base_A’
B30doassign class: Build with Volume2 ≥ 0.5 at Sub_A: B_30copy image object level: at Sub_A: copy creating ‘Base_A’ above
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assign class: unclassified with Distance to B_30 ≤ 80 Pxl at Base_A: Area_30image object fusion: loop: Area_30, B_30 at Base_A:: Container_2image object fusion: loop: Container_2 at Base_A:: Container_2assign class: unclassified with Existence of super objects Container_2 (1) ≥ 0.9 at Sub_A: Area_30delete image object level: delete ‘Base_A’
B20doassign class: Build with Volume2 ≥ 0.01 at Sub_A: B_20copy image object level: at Sub_A: copy creating ‘Base_A’ aboveassign class: loop: unclassified with Distance to B_20 ≤ 30 Pxl at Base_A: Area_20image object fusion: loop: Area_20, B_20 at Base_A:: Container_2image object fusion: loop: Container_2 at Base_A:: Container_2assign class: Container_2 with Number of sub objects B_20 (1) ≤ 10 at Base_A: unclassifiedassign class: unclassified with Existence of super objects Container_2 (1) ≥ 0.9 at Sub_A: Area_20delete image object level: delete ‘Base_A’
dodocopy image object level: at Sub_A: copy creating ‘Base_A’ aboveassign class: Area_40, Area_50, Area_60, Area_70 with Max. diff. ≥ 0.8 at Base_A: Diff8merge region: loop: Diff8 at Base_A: merge regionassign class: Diff8 with Area ≤ 20,000 Pxl at Base_A: unclassifiedfind enclosed by class: loop: Area_60, Area_70 at Base_A: enclosed by Diff8: Diff8 +assign class: Area_20, Area_30, Area_40, Area_50, Area_60, Area_70 with Existence of super
objects Diff8 (1) ≥ 0.9 at Sub_A: unclassifiedassign class: Area_40, Area_50, Area_60, Area_70 with Ratio to scene Red ≤ 0.85 at Base_A: Diff8assign class: Area_20, Area_30, Area_40, Area_50, Area_60, Area_70 with Existence of super
