Content

Building and Vegetation Heights 2012

Figures / Tables

Figures

Fig. 1: Segment from the Automated Properties Map (ALK; number of storeys in Roman numerals)

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Fig. 2: Extent of the project area: Area of the city of Berlin and surrounding areas

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Fig. 3: Section of the geo-base and technical data used: ALK building layer (red), ISU5 block boundaries (yellow), street trees (green), with a TrueOrtho-photo background

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Fig. 4: Work procedure for the ascertainment of buildings and vegetation items in the Berlin urban area

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Fig. 5: The principle for the generation of a Normalized Digital Surface Model (NDSM)

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Fig. 6: A comparison of the DOP and the TOM: a) DSP 20RGB 2011, with tilted building images, b) TOM RGB, with ALK building contours and building edges superimposed and corrected true to the position, c) NDSM with ALK building contours superimposed

JPG-Document (99.4 kB) Image: Source: a) SenStadtUm – FIS-Broker, c) DLR 2013

Fig. 7: Quality of the TrueOrthoMosaic (TOM) data produced: Use of the near-infrared (NIR) channel (below); the bright areas indicate high spectral values and good ascertainability of the vegetation shares

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Fig. 8: Abstract depiction of the object hierarchy and several scale levels (left), and with image examples (right)

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Fig. 9: Process tree developed for object-based classification (depiction of a segment)

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Fig. 10: Two classification examples for buildings of the item class “Buildings planned or under construction” (as of June 2012); left and middle: certain classified segments, and, right, as compiled items; in the top row is an example of the construction project in progress; below it is a building already completed at the time of the aerial photography flight in September 2010

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Fig. 11: Classification steps for the determination of the item classes “Garden cottages” outside the ALK system: a) The classification of the vegetation mask attained prior to this step, with real colour RGB aerial photography data superimposed; b) Situation-precise section of the surface model NDSM – no heights of garden cottages can be directly derived; c) Initial classification attempt yields errors in the area of streets; d) Ensuing classification step, with correction for the street areas

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Fig. 12: Aggregation of heights of building segments to the ALK building portions; left: building segments; right: ALK building portions

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Fig. 13: Results of Multi-Threshold Segmentation (MTS) – Subdivision of the vegetation into nine height levels

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Fig. 14: Results of the subdivision into vegetation structures with the aid of multi-resolution segmentation (b & c) within particular height levels, from the preceding multi-threshold segmentation (a, cf. Figure 13)

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Fig. 15: Greened roof surfaces, as a result of the correlation of the vegetation mask with the ALK building layer

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Fig. 16: Classification results of the class “Roof segments covered by vegetation” (conclusive representation below right)

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Fig. 17: Building height ascertainment, using the example of the Victory Column: Here, the calculated average height of approx. 20 m deviates considerably from the maximum height of the item, which is 67 m

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Fig. 18: Segmentation gaps in Project Phase 2, due to reduced quality of the image data material (using the example of Grunewald Forest)

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Fig. 19: Classification results in the Berlin inner city: Highly impervious inner-city buildings on both sides of Friedrichstraße (left), and Imperial era block construction with a high share of green space in the area of “Görlitz Park (right) (scale 1: 7500)

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Fig. 20: Classification results in the area of loose, intensively green construction at the edge of the inner city (inside the Circle Line): Row-houses and duplexes with gardens in Neu-Tempelhof (left) and row-houses with landscaped residential greenery south of An der Wuhlheide in Treptow-Köpenick (right) (scale 1: 7500)

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Tables

Tab. 1: Number of greened roofs/ portions of roofs per borough, and their respective uses (area ascertained shown in red, as of: July 2013); the ascertainment of green roofs in the rest of the city did not provide sufficient data quality to permit an assessment.

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