Automatic delineation of built-up area at urban block level from topographic maps

https://doi.org/10.1016/j.compenvurbsys.2016.04.001Get rights and content

Highlights

  • We developed a process to automatically segment built-up area from topographic maps.

  • This process is designed modular.

  • Due to the binary map form, only geometric and topological information is used.

  • The map image is processed by a two-stage, top-down approach.

  • We achieve accuracy values of 0.93 to 0.97 for three study sites.

Abstract

To comprehensively study and better understand urban dynamic processes — such as densification, growth and sprawl, or shrinkage — spatio-temporal databases that allow to track changes of geographic objects like buildings and urban blocks are essential. While comprehensive databases exist for contemporary data, they usually lack a historic dimension. The manual constitution of historic geographic data, be it based on historic maps or aerial images, is a time consuming and laborious process, however. Therefore, we present an approach to semi-automatically extract this data from binary topographic maps with regard to built-up areas at urban block level. The suitability of topographic maps for historic urban analysis has been proven in previous research. To overcome the challenges that are inherent in scanned topographic maps in regard to digital image interpretation we designed a modular process. Among others, these challenges include fused and (multi-)fragmented map objects caused by the overlap of competing content layers in one single binary map. After a preliminary separation of individual map object layers from the map content, the process follows a two-stage top-down approach. At first, the map is organized into street blocks, which after that are re-delineated in regard to built-up area. In doing so, we achieve correctness values ranging from 0.97 to 0.93 for three study sites in Germany. With an increasing number of projects that provide historic topographic maps as georeferenced digital data, our process represents a promising approach to efficiently prepare these historic data for integration into a spatio-temporal database with minimal user intervention.

Introduction

The analysis of urban form and urban morphology has a long history (Bianchin & Bravin, 2008: 300; Hofmeister, 2004: 5) and is of high importance for stakeholders of urban governance such as authorities of local and interurban spatial planning (Foltête and Piombini, 2007, Krumdieck et al., 2010), climate researchers (Heldens et al., 2010, Peeters and Etzion, 2012), or decision makers in economics (Webster, 2010). Ex-post analyses of built-up areas1 based on time series are most relevant for planning and evaluation processes in terms of achieving settlement policy goals (e.g. to reduce urban sprawl) (Herold et al., 2005, Jaeger et al., 2010). Further, the analysis results can be used to feed ex-ante models of urban dynamics with necessary auxiliary input data (Herold et al., 2005, Jantz et al., 2010). Both forms of urban analysis require data about the morphology of the built-up area ranging from (digital) topographic data to remote sensing data. The latter include aerial/satellite imagery (e.g. Taubenböck et al., 2010, Thiel et al., 2008, Wurm et al., 2010) and airborne laser scanning data (Yoshida & Omae, 2005). While recent research focuses on information retrieval from very high resolution remote sensing systems, data of earlier generations (e.g. Landsat TM/ETM +) lack the spatial resolution to allow small-scale analysis and are only suited for regional-scale delineation of built-up area (Taubenböck, Esch, & Racette, 2011).

To derive extensive historic information about urban morphology for ex-post analyses potential surveying methods vary. While most case studies are of local character and, therefore, are based on visual interpretation to demarcate built-up areas from open space (Herold et al., 2005, Jaeger et al., 2010), methodologies have been suggested to automatically extract this information (cf. in Stengele, 1995). Scanned topographic maps have the advantage of implicitly holding semantically structured information about the urban fabric that is spatially accurate within given restrictions due to generalization. This information, in order to be analyzed, needs to be converted to explicit spatial objects by means of pattern recognition and image processing (Graeff & Carosio, 2002). According to Walter and Luo (2011), this process can be referred to as map interpretation in general or inverse cartographic engineering in particular. Behind the background of a global trend to digitize knowledge that can also be observed with topographic maps (e.g. USGS, 2013), methods for an automated map interpretation form the necessary foundation to analyze the resulting geographic knowledge database. Previous research has shown the suitability of topographic maps for (ex-post) analyses of urban morphology (e.g. Meinel, Hecht, & Herold, 2009), despite their inherent level of uncertainty and generalization (Leyk et al., 2005, Tucci and Giordano, 2011).

Recently, high-resolution analysis of urban morphology focused on the development and application of building-based methods, e.g. to describe stock changes or to derive indicators of urban morphology relevant for planning (Carneiro et al., 2010, Meinel et al., 2009). However, the next superior spatial entity of the urban fabric – the urban block representing a group of developed parcels with buildings – remains an important reference for spatial planning and official areal statistics. The spatial segmentation of built-up space into urban blocks is a common concept of urban morphology. The urban block represents an important morphological unit that not only helps to globally differentiate forms of urban morphology based on quantitative characterization, but also allows for a comparative monitoring of urban metamorphism at city or urban district scale (Wurm et al., 2010, Yoshida and Omae, 2005).

In this paper, we propose an approach using methods of image processing and geometrical constraints to automatically extract urban information from historic topographic maps placing our emphasis on the primary objects of urban fabric: urban block and street network, both sharing a mutual relationship (Edwardes & Mackaness, 2000). While databases already exist for contemporary data (e.g. DLM), our approach is important to constitute historical data. We demonstrate the applicability of our methodology using binary German topographic maps at a scale of 1:25.000. Section 2 will give a short summary of related work in urban object extraction in general and map interpretation in context of urban analysis in particular. Section 3 covers the data input and the chosen study sites for testing the methodology that is presented in Section 4. The results for our study sites are given in Section 5 and discussed in an international context in Section 6. Section 7 will give a summary and present an outlook on implications to future work.

Section snippets

Object extraction from raster maps

In reference to urban analysis, the delineation of urban objects used to be mainly a task of visual interpretation of maps and aerial images. With emerging new technologies, the focus moved to an automated analysis of images showing urban phenomena (Bianchin & Bravin, 2008). Accompanying the paradigm shift from analog to digital cartography and the consequent building up of digital (historic) spatial databases, the automated image analysis of maps had its first peak some 20 years ago (e.g.

Topographic data

Derived from the scope of our research to allow for comprehensive monitoring of built-up area in time series, the approach for delineating urban blocks from historic topographic maps proposed here is based on the binary black layer of the DTK25(-V) — an abbreviation for the scan of a hard copy German topographic map at scale 1:25.000. It has a scan resolution of 508 dpi. Being at large comparable to other European maps, the black layer contains the elementary objects of urban morphology:

Methodology

Most of the widely accepted methods developed for delineating objects with linear demarcation from images or digitized maps solely use a raster-based approach to separate the object of interest from the ‘background’, including morphological filtering and thresholding for color images. Many of them appear to create good results depending on the quality of the input or the object of interest. The hereby presented concept, however, appends such methods by an object-based approach (raster-vector

Evaluation

The methodology has been trained based on three tiles of the size 1250  1250 m for the map sheets and all map object models have been tuned to achieve perfect delineation results for those tiles. We have added a table of parameters and the assigned values used in each module as Supplementary online material. Due to the amount of parameters and depending on the map layout complexity, the adaption can be time consuming and requires some knowledge of image analysis, however. To evaluate the results

Discussions of results

The overall delineation result is very promising. However, for multi-fragmented or incomplete street block boundaries the approach shows only medium performance. This can be observed especially in industry-dominated map regions, areas with complex, superimposing transportation infrastructure, and inner city areas with an especially high competition of different map contents. Further, one particular problem that can be observed in peripheral settlement areas is the use of dashed rather than

Conclusion and outlook

We have presented an approach to automatically interpret topographic maps with regard to street blocks and built-up areas (urban blocks) that significantly reduces manual effort to prepare scanned historic maps for integration into a spatio-temporal database and urban analysis. Using a modular process that applies methods of image analysis and spatial analysis, we are able to overcome the challenges that are inherent in topographic maps concerning the automated delineation of built-up area.

Acknowledgments

This research is funded by the German Research Foundation (ME 1592/3-1, ME 1592/3-2). The authors greatly appreciate this funding. The data used to proof our concept are kindly provided by the Federal Office for Cartography and Geodesy (BKG).

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