Home Articles R2V: Automated Map Digitising

# R2V: Automated Map Digitising

Yecheng (Ted) Wu
Ph.D., President/CEO, Able Software Corp., 5, Appletree Lane, Lexington, MA 02420, USA
Email: [email protected]

Vector data is easier than raster data to handle on a computer because it has fewer data items and flexible to be adjusted for different scale more flexible to be adjusted for different scale.

Raster and vector are the two basic data structures for storing and manipulating images and graphics data on a computer. All of the major GIS (Geographic Information Systems) and CAD (Computer Aided Design) software packages available today are primarily based on one of the two structures, either raster based or vector based, while they have some extended functions to support other data structures.

Raster image comes in the form of individual pixels, and each spatial location or resolution element has a pixel associated where the pixel value indicates the attribute, such as colour, elevation, or an ID number. Raster image is normally acquired by optical scanner, digital CCD camera and other raster imaging devices. Its spatial resolution is determined by the resolution of the acquisition device and the quality of the original data source. Because a raster image has to have pixels for all spatial locations, it is strictly limited by how big a spatial area it can represent. When increasing the spatial resolution by 2 times, the total size of a two-dimensional raster image will increase by 4 times because the number of pixels is doubled in both X and Y dimensions. It is true when a larger area is to be covered when using same spatial resolution.

Vector data comes in the form of points and lines that are geometrically and mathematically associated. Points are stored using the coordinates, for example, a two-dimensional point is stored as (x, y). Lines are stored as a series of point pairs, where each pair represents a straight line segment, for example, (x1, y1) and (x2, y2) indicating a line from (x1, y1) to (x2, y2). In general, vector data structure produces smaller file size than raster image because a raster image needs space for all pixels while only point coordinates are stored in vector representation. This is even truer in the case when the graphics or images have large homogenous regions and the boundaries and shapes are the primary interest.

Flexible for Geographic Representation
When geometric shapes need to be represented precisely in a GIS or CAD system, vector data structure is always the option to use because it is not limited to spatial resolution or pixel size and mathematical formulae can be used for regular shapes and smooth curves. In addition, polygon topology is another important issue when implementing a GIS system. Vector data structure makes it easy to describe if a region is on the left side or the right side of a common boundary or if a point is in or out of a polygon area.

Another advantage vector data structure has over raster image is the flexibility of resising without losing resolution. For example, graphical features such as rivers and roads in a map viewed with a real-world projection system can be easily displayed at any scale without physically changing the data. By contrast, raster image has to be stretched and distorted when scaled beyond its native resolution. Besides the above issues, vector data is easier than raster data to handle on a computer because it has fewer data items and more flexible to be adjusted for different scale, for example, a specific projection system in a GIS database. This makes vector data structure the apparent choice for most mapping, GIS (Geographic Information System) and CAD (Computer Aided Design) software packages.

 Shows regions of inteShows an original scanned soil map with dark background in the left window. The image in the right window is processed with the background removed. The processed image will reduce the amount of editing needed after automatic vectorisation process

Why Is Raster to Vector Conversion Needed?
When vector data is not readily available for setting up a GIS database, the vector data is normally created from existing paper maps or natural source images, such as aerial photos or satellite imagery. Because of its abstract form, vector data has traditionally been acquired using manual tracing with a digitising tablet from paper maps or base images. The disadvantages of the manual method are slowness and lack of accuracy because human hand is capable of resolution only to the level of about 40 dots per inch (DPI). For a typical contour map, it can take one skilled operator several weeks to trace all the lines manually. The intensive labour requirement makes large mapping and GIS project difficult and expensive to implement.

With the development of scanning technology, image scanners have become cost-effective and capable of high resolution, in the range of 100 – 1,200 DPI. Moreover, similar developments in automated raster to vector conversion have made it possible to take a paper map, scan it and accurately convert it into vector format. This method uses computer to automatically extract vector data from scanned images and eliminates the manual tracing process. Using raster to vector conversion technology, large scale map digitising or GIS database creation project can now be accomplished in a much shorter time with less demand on human resources.

How Raster to Vector Conversion Is Done?
While vector data structure provides a simpler and more abstract data representation than raster image, an automatic conversion from raster to vector, or so called vectorisation process, is not a very easy task, although the opposite direction (from vector to raster) is quite trivial and straightforward. There have been extensive research efforts focused on the issues involved in raster to vector conversion during the past decades.

A complete raster to vector conversion process includes image acquisition, pre-processing, line tracing, text extraction (OCR), shape recognition, topology creation and attribute assignment.

 Shows regions of interest defined for the SPOT image (Washington DC, USA) and image cropped to indicate the regions to be processed

Setting Scanning Resolution
The image acquisition process generates the initial raster image at a certain spatial resolution. The quality and resolution of the raster image are key factors for the quality and accuracy of the vectorised data. It is always recommended to start with clean and sharps originals and scans at a reasonable resolution.

The scanning resolution should match the resolution at which the original image source was created. If scanning resolution is set too high than the original image source, it not only uses unnecessary amount of system resource to process, but also noise and artifact are scanned or generated. This is the same case as looking at a low resolution hardcopy map through using a large magnify glass, rough edges, dots and even paper texture are visible. If you scan a paper map using very high scanning resolution and see a lot of noise in the scanned image, especially when using a colour scanner, lowering the scanning resolution may be the solution to improve its quality. However, if lines are toughing each other in the scanned image, then it indicates the scanning resolution is too low and higher scanning resolution is certainly needed.

 Shows a classified and vectorised colour topo map. The generated vector data is displayed in the window on the right. 3D-elevation model is created from the vector contours and displayed in the upper right window.

Choose Image Type
Most good quality black and white maps and engineering drawings, including color map separates, can be scanned as 1-bit monochrome. If the background is clean and the scanned image does not show many dots and speckles, 1-bit monochrome is the perfect type to use because it takes less storage space and is faster in display and processing.

For single color maps with dirty and smearing background, such as old maps or blue prints, they can be scanned as 8-bit greyscale and cleaned using image-processing techniques, such as background removal. Noise and other artifacts can be easily smoothed out using a pair of grey level thresholds before automatic vectorisation. Greyscale image provide more information than 1-bit monochrome image for image processing tasks such as background and noise removal but is normally 8 times larger in size than 1-bit monochrome image. If smaller image size is preferred, one safe way is to start with greyscale image typed and uses software to clean up and convert to 1-bit monochrome for storage. On the other hand, image compression, such as JPEG and wavelet methods, can be applied to reduce the size of greyscale image while maintaining the same pixel bit depth.

Although color scanners have come a long way, large format and high resolution scanning is still quite expensive. If the source image is in color and a good quality color scanner is available, scanning using 24-bit color image type certainly gives the benefit of separating color layers and simplifying the vectorisation process. Color separation normally uses color classification or color ratio based methods to divide millions of colors into a limited number of color groups and each group is assigned a single color. Each color in the classified image can then be vectorised or extracted to create a single color image.

Other color images, such as satellite and aerial photos, have been used directly to create vector data, such as region boundaries, street and road lines. Because of more bits (normally 24-bit) are used, color image files are much bigger than 8-bit greyscale and 1-bit monochrome images and require more system resource to store and process. Of course, image compression techniques can help to reduce the size of color images. When using lossy compression, such as JPEG or wavelet-based methods, compression ratio must be carefully selected so image quality is not sacrificed since the success of vectorisation depends heavily on the quality of the image.

Preprocessing Steps
Preprocessing steps are different depending on the image type. For 1-bit monochrome image, de-speckle is often used to remove noise and smooth rough edges. For 8-bit greyscale image, thresholding and background removal are processing steps to improve image quality for vectorisation. For color images, they are often classified to separate the colors so each color can be vectorised into a separate vector layer.

Defining regions of interest (ROI) for vectorisation or image cropping is another often used preprocessing step to limit the processing only in the areas interested. It is important to allow the use of polygons and group of polygons to include cases such as islands, holes, rings and other shapes.

Image mosaic or stitching is normally done when a source map is larger than the scanner can handle. In this case, the map is scanned into sub-sections and then merged into a whole image for raster to vector conversion. This is often done as a post-processing step by merging the vector data sets after each section is vectorised. Merging vector data instead of raster image certain has its advantages, because vector data takes much less computer memory and can be processed faster while image stitching can create huge size images that are beyond the processing capability of a regular PC.

 Shows that closed polygons are created from vectorised line segments.

Vectorisation
The line tracing process extracts two types of lines: center line and boundary line. The center line method tracks the center pixel within a raster line and follow to the line until it reaches an intersection or the end of the line. The boundary line method tracks the boundary pixels of a color region to get closed polygons.

Although there have been many methods developed for line tracing, they can be divided into two groups: line thinning and line following. The line thinning method is more of a global approach, which iterates through the entire image in multiple passes and eliminates boundary pixels during each iteration until only the skeleton pixels are left. The line following method uses computer intelligence to analyze line shapes, thickness and intersections to follow the line centers. This method is frequently employed in semi-automatic interactive tracing while line thinning based methods is used for fully automatic conversion of complex images.

After lines are extracted, they are labeled with line attributes or elevations if contours. Closed polygons can be generated from line segments to create the topology. Control points are defined and applied to geo-reference the vector data to a projection system.

One common use of labeled contour lines is creation of 3D DEM (Digital Terrain Model) and other 3D data models. We will be seeing more and more use of 3D display in the next couple of years in GIS and computer mapping applications. The use of 3D visualisation gets us one step closer to the 3D world we live in but it puts more demand on computer software and hardware. Many people think today’s computer technology is far more powerful than we need, they are right if word processing is what they do everyday. They will be surprised how much more computing power is badly needed when 3D digital terrain model is used in real time and how much worse it can get when high resolution satellite imagery are draped onto the surface of the digital terrain model. We are quite sure that faster CPU, bigger memory and better quality display will not be wasted in GIS and computer mapping applications.

Choosing the Right Conversion Tool
Several raster to vector conversion software packages are commercially available for different applications, such as engineering drawing conversion, map digitising and GIS data capture. The R2V software developed by Able Software Corp. (www.ablesw.com) in 1993 has a focus on vectorisation of scanned maps and GIS data creation.

Below are few questions one should ask when selecting the right tool for the task:

1. Does it support different image types, such as 1-bit black/white, greyscale and 24-bit RGB color? This is quite important for people whose source images are in color. Treating color images as black and white or greyscale apparently loses all color information and a significant amount of editing may be needed to separate colors by hand.
2. Is it designed for maps or engineering drawings? In practice, the handling of map data and engineering data are quite different although they both are vectors based. If a package is designed for CAD drawings, the algorithms normally works well for straight lines and regular geometric shapes and will not be efficient for curving lines, polygons and topology between polygons. Geo-referencing is another crucial factor for maps and GIS database while it is normally not a concern for CAD applications.
3. Does it support the native format for your application? It’s unfortunate that most vector file formats used today are different and data exchange between two formats can easily result some data loss. One format may be excellent for CAD data transfer, but very limited if you need to get data into a GIS or mapping database. When creating vector data, it is always better to use the native format the target system supports.
4. Image processing functions The quality of raster to vector conversion depends largely on the quality of the source image that is affected by many factors, including scanner, cleanness and age of the source map, scanning resolution, color or black/white, and others. Without necessary image processing functions, such as remove background for old maps with blue background, color separation for color maps, define polygon-based region of interest (ROI), image rubber sheeting to correct distortion, the usefulness of the final vector product may be quite limited.
 Shows that boundary lines are traced directly from a classified SPOT image. Different color regions are traced and put into separate map layers.

Future Developments
Because of the complexity, automated raster to vector conversion has attracted a significant amount of research focus in the past decades among GIS and image processing communities. Although commercial products have been developed and used in production type applications for large scale map digitising and GIS data capture projects, there is still room for improvements and demand for new algorithms and technologies, for example, color image processing and color separation, text recognition (OCR), use of satellite imagery to create vector map layers and others.

When 24-bit true color is used to scan color maps or drawings, each pixel has 3 color components (red, green and blue). Each component is recorded as an 8-bit integer number with the value range of 0 – 255. Roughly, a 24-bit color image can have up to 16.7 million different colors. Classifying the millions of colors into a small number of color groups becomes a challenge, especially when some color groups have only small number of pixels and the source image quality is not perfect. Clustering based color classification and ration based methods have been developed to solve this problem but in many cases, more robust methods are needed to achieve more satisfactory color separation result.

Text recognition (OCR) is another challenge faced by developers and researchers. To reliably recognize text labels in maps, the first step is to separate them from lines, also known as the text segmentation step. Once separated, text recognition engines are applied to identify the text and convert them to computer readable ASCII code, or unicode for other languages, such as Chinese and Japanese. Conventional text recognition (OCR) technologies have not worked well for recognizing text in maps and drawings, largely due to the variety of fonts, sizes and orientations used. International languages add more difficulty to the problem.

When high resolution satellite imagery become more affordable and easily accessible, we will be seeing more use of them to create GIS data layers and update existing map data. To automate the process from raw satellite imagery to finished vector data layers, new methods and products will be developed to recognize and map natural objects, such as roads, building roof tops, trees, vegetation, water and so on. Not only lines and polygons will be generated from the images, but also important attribute and layer information associated with the graphical objects.

References

• Y. Wu, “Raster, Vector, and Automated Raster-to-Vector Conversion”, in “Moving Theory into Practice: Digital Imaging for Libraries and Archives”, book eds. by Anne R. Kinney and Oya Y. Rieger, 2000, RLG, Cornell Univ. Library
• Y. Wu, “R2V Conversion: Why and How?”, GeoInformatics, No. 6, Volume 3, Sept. 2000, pp. 28-31
• L.R. Poos and Y. Wu, “Digitizing History: GIS and Historical Research”, GIS World, July 1995, pp. 48-51
• J.R. Parker, “Algorithms for Image Processing and Computer Vision”, 1997, John Wiley & Sons.