Digitize Old Maps with Raster-to-Vector Conversion and Use Them in GIS

If you digitize old paper maps (historical maps) and import them into a GIS (Geographic Information System), you can freely compare and analyze geographic information from the past and present. Historical maps come in many forms—land registry maps, older topographic maps, Edo-period pictorial maps, and so on—but while they remain analog it’s difficult to use them and they are at risk of deterioration. By digitizing historical maps using raster-to-vector conversion (raster-vector conversion), you can efficiently convert lines and shapes drawn on paper into vector data and overlay them with modern map data. This article explains in detail the digitization process for historical maps, the role of raster-to-vector conversion, and how to use the resulting data in a GIS.

Digitizing historical maps offers major benefits across various fields. For example, it can be useful in situations like the following:

• Geographers and historians: You can analyze past terrain and land-use changes. By overlaying historical maps on current maps, you can track historical geographic changes in detail—such as river channel shifts or urban expansion.

• Municipal disaster-management staff: If you digitize old flood extent maps or disaster-history maps and overlay them on current maps, you can identify areas at high disaster risk. Comparing past disaster records with current maps helps improve disaster planning and the accuracy of hazard maps.

• Surveying and design engineers: If cadastral maps or old design drawings kept on paper are converted into vector data, they can be handled directly in CAD or GIS. You can integrate existing boundary lines and reference points into the present coordinate system and use them as reference material for new designs.

Now let’s look at the concrete steps to digitize historical maps and the key points of raster-to-vector conversion.

What is raster-to-vector conversion?

First, what is “raster-to-vector conversion”? Raster-to-vector conversion is a technique that automatically converts a map captured as raster data (image data) into vector data (lines and point data). When you scan a paper map you get an image (a raster), but the lines and text in that image are just collections of pixels. Using raster-to-vector conversion software, the software analyzes line segments such as roads and boundaries in the image, links continuous pixels, and extracts them as digital lines (vectors). In short, it’s like automatically tracing the lines in an image to create drawing data usable in CAD or GIS.

The advantage of raster-to-vector conversion is that it is overwhelmingly more efficient than manually tracing from scratch. Digitizing vast areas of historical maps or complex drawings by hand requires enormous time and effort, but dedicated software can produce vector data with a certain level of accuracy in a short time. Raster-to-vector conversion software is particularly useful for materials printed with clear lines, such as cadastral maps and old topographic maps.

However, there are caveats. The accuracy and quality of automatically converted data depend on the source image. If the scanned image is unclear, or folds and distortions make the scale uneven, the positional accuracy of the conversion will suffer. Also, text and symbols are different from lines and may not be recognized correctly automatically, resulting in distortion. Although raster-to-vector conversion tools have improved year by year, manual correction and verification after conversion remain essential. For example, you may need to supplement details that couldn’t be fully converted or re-enter collapsed place names and numbers.

Even so, raster-to-vector conversion plays an important role in the digitization workflow for historical maps. Especially when handling large numbers of paper maps, combining appropriate preprocessing with raster-to-vector conversion makes it possible to efficiently produce vector data. Next, let’s go through the specific steps for digitizing historical maps.

Steps to digitize historical maps

To digitize historical maps, you need to follow several steps in order. Here is a typical workflow.

1. Scanning historical maps (rasterizing)

First, capture the paper map with a scanner or a high-resolution camera to create a digital image (raster) file. It’s important to scan at as high a resolution as possible so that fine lines and text are legible (generally 300–600 dpi is recommended). For large-format maps, use a large-format scanner or scan in multiple sections and stitch the images together afterward. Before scanning, it’s also important to flatten folds and wrinkles as much as possible. Old maps folded for storage may have distortions in the paper, so pressing them flat with a transparent weight (such as a glass plate) during scanning can yield better results. Save scanned images in non-lossy or losslessly compressed formats such as TIFF or PNG to preserve clarity without degradation.

2. Georeferencing the scanned image

The scanned map image doesn’t yet belong to any coordinate system. In other words, to compare or overlay it with modern maps, you need to perform georeferencing (aligning to geographic coordinates). Georeferencing assigns latitude/longitude or plane coordinate values to specific points on the image and aligns the image to real geographic space. Specifically, select multiple known points on the historical map (for example, intersections, building corners, triangulation points on mountain peaks that are still identifiable today), obtain their coordinates from a current map, and link them. By entering several control points in GIS software, the software will apply rotation, translation, scaling, and, if necessary, non-linear distortion correction so the historical map image can be overlaid on a modern coordinate system.

This alignment step ties the scanned historical map to real-world geographic space so it can be correctly overlaid with other GIS data (modern maps, aerial photos, etc.). To improve georeferencing accuracy, it’s desirable to place control points evenly across the map extent. If the historical map contains latitude/longitude grids or coordinate markings, use them to achieve more accurate alignment. For pictorial maps from before the Meiji period, which may be more schematic, precise georeferencing can be difficult; however, aligning major landmarks to their approximate positions can still be useful for comparative purposes.

3. Preprocessing the raster data (image adjustment)

Once the image is aligned, perform preprocessing to improve the quality of the scanned image before running raster-to-vector conversion. If the historical map is in color or grayscale, consider converting it to a black-and-white binary image. Raster-to-vector conversion software typically works best when lines (black) and the background (white) are clearly separated. Photographic tonal variations or paper yellowing and stains may be detected as noise if left uncorrected. Use image-editing software to adjust contrast and remove unnecessary colors so that only the map’s linework stands out.

Also, remove any extra border lines or margins captured in the scan and correct skew from scanning by trimming and rotating the image at this stage. Use filters for noise reduction if needed, but be cautious: overly strong filtering can erase fine roads or boundary lines. The goal of preprocessing is to provide as clear and low-noise a binarized image as possible so the raster-to-vector algorithm can accurately detect map lines.

If lines are thick and blurred, apply a line-thinning process to make lines thinner so the converter can more easily capture the line center and reduce the occurrence of duplicated lines.

4. Running raster-to-vector conversion (automatic vectorization)

When preparations are complete, load the image into raster-to-vector conversion software and run the automatic vectorization. Operations vary by tool, but generally you open the image file you want to convert, set conversion parameters, and start the process. Parameters typically include threshold values for what to consider as line (how dark a pixel must be to be treated as black), size thresholds to filter out areas regarded as noise, and the precision for approximating curves with straight or curved segments. Proper parameter settings yield better conversion results.

When conversion starts, the software analyzes lines in the image and generates vector objects (points, lines, polygons, etc.). Roads and boundaries will be output as continuous polyline data, building outlines as polygons, and so on. Choose an output format compatible with CAD/GIS such as DXF or SHP (shapefile) depending on your needs. Conversion time depends on image size and content, but it is much faster than manual work—processing a large map may take from a few minutes to tens of minutes.

Check the automatic conversion results and remove any clearly spurious vector elements (garbage or misrecognized lines). If building names or place names have been extracted as unnatural lines, remove those line segments and, if necessary, record the textual information separately as text data. Some raster-to-vector tools allow you to exclude specific colors or areas during conversion, so you can mask text regions before running the conversion to avoid extracting text as lines.

5. Editing, integrating, and saving vector data

Edit and clean up the vector data produced by raster-to-vector conversion as needed. When joining multiple maps, confirm there are no misalignments at the seams between adjacent sheets and make fine adjustments if necessary. Even vector data from a single historical map may contain broken lines or extra vertices. Use GIS or CAD software to edit these—reconnect lines that should be continuous, remove duplicate segments, and so forth. It’s especially important that boundary lines and road networks be continuously connected.

After editing, save the data in appropriate formats. For GIS use, export to shapefile (SHP), GeoJSON, or a database format to facilitate integration with other geographic data. For CAD use, save in DXF format so the data can be combined with other drawings or reused in designs. When saving, don’t forget to verify the coordinate system and unit settings. This prevents mismatches when overlaying with other data.

Following these steps, paper historical maps are reborn as digital vector data. Next, let’s look at concrete examples of how such historical map data can be used in GIS.

Using historical map data in GIS

Vectorized historical maps produced via raster-to-vector conversion can be used in many ways within a modern GIS environment. Here are representative use cases for historical map data.

Use in geographic and historical research: comparative analysis of past and present

Digitized historical maps are useful for comparing and analyzing past and present geography by overlaying them with current map data. For geographers and historians, comparing historical maps with contemporary maps in a GIS reveals many insights. For example, overlaying river courses or lake shapes derived from Meiji-era topographic maps onto modern satellite imagery can clearly show how former riverbeds are now residential areas. Similarly, comparing old city maps with current road networks can reveal how former highways or railway lines that were closed or relocated have influenced the present urban structure.

Visualizing historical maps in a GIS can also reveal geographic patterns that are not obvious from paper sources. For instance, aligning village layouts shown on Edo-period pictorial maps with modern topographic maps can show what environmental factors (distance from rivers, whether high ground or low ground, etc.) influenced settlement locations. Stacking maps from multiple periods allows quantitative evaluation of long-term environmental changes such as urban expansion speed or forest decline/increase. Such comparative analyses are valuable as materials for papers and reports and can convey convincing narratives when presented as visual figures.

Use in disaster prevention and hazard mapping: visualizing risk areas

Historical map data are also valuable for municipal disaster planning. Reflecting past disaster histories and terrain information on current maps can help identify potential risk areas. For example, if you extract old river channels (former river courses) from early Showa-era topographic maps as vector data and overlay them on modern residential maps, you can determine whether current housing sits on areas that used to be rivers. Areas developed on reclaimed land or former riverbeds may have higher risk of liquefaction or flooding, so they warrant attention in disaster planning. Similarly, plotting place names like “○○ embankment” or “pond” found in Edo-period maps can provide clues to terrain that was historically prone to flooding.

Digitizing old hazard maps and disaster-record maps and comparing them with current maps helps in disaster countermeasure planning. For instance, overlaying historical landslide extents on present residential maps reveals which modern districts fall within those ranges and should be prioritized for hazard measures. By adjusting the transparency of historical map layers in GIS, latent terrain features—past cliff lines or wetlands—that are no longer visible can be intuitively recognized. Using historical maps in disaster prevention can therefore improve hazard-map accuracy and help produce persuasive disaster-education materials for residents.

Use in surveying and design: integrating existing assets

In surveying and civil design, converting old drawings and cadastral maps into vectors via raster-to-vector conversion allows them to be used in modern planning. For example, digitizing paper cadastral maps (public land registry maps) and importing them into GIS lets you overlay current cadastral survey data or aerial photos to check changes in land boundaries. You can compare boundary lines visible on old cadastral maps with current registry information and, if discrepancies exist, consider re-surveying in the field as a decision-making aid.

Design work often refers to past drawings. If old floor plans or facility layout drawings are vectorized via raster-to-vector conversion, you can use existing structure positions and shapes as a base for new designs. Managing old piping diagrams or road drawings in a GIS makes it easier to identify discrepancies during renovation planning and prevents design errors. When existing drawings are digitized, you can easily extract just the needed parts for reuse in new drawings—especially useful in remodeling or expansion projects where reusing existing facility vector data improves efficiency.

Moreover, combining digitized historical-map data with field survey results (GPS survey points, coordinates from electronic reference stations, etc.) enables smooth integration of historical survey drawings with current survey coordinate systems. This allows you to reproduce benchmarks or leveling point information from old drawings on current base maps and reflect them in design and construction.

Accuracy verification and correction using on-site surveying with LRTK

When using historical map data in GIS, it’s ideal—if possible—to compare and correct the data against on-site survey measurements. One useful tool for this is the increasingly popular simple surveying system known as LRTK. LRTK is a system that links a compact high-precision GNSS receiver with a smartphone, enabling real-time centimeter-level positioning. Compared to conventional surveying instruments, it offers superior portability and intuitive operation, making it easier to use even for non-specialist survey personnel.

With LRTK you can readily obtain accurate current coordinates for points depicted on historical maps. For example, measure the current coordinates of control points or important intersections shown on a historical map using LRTK, then compare those coordinates with the corresponding points on the georeferenced historical map to verify positional accuracy of the digitized data. If offsets are discovered, you can refine the GIS data based on the survey results (for example, by shifting the whole dataset by a few meters or using rubber-sheet correction to locally align it) to improve conformity between the historical map data and the field coordinate system.

Because LRTK surveying is fast, you can also collect additional data on the spot as needed. Measure current information not present on the historical map (locations of new buildings, terrain changes, etc.) and import these measurements into the same GIS project to create a comprehensive map resource integrating past and present data. Combining raster-to-vector converted historical map data with LRTK field survey data enhances the reliability and usefulness of digital maps. Going forward, the adoption of affordable, easy-to-use LRTK in various projects is likely to advance precise use of historical materials.

Conclusion

This article on “Digitize Old Maps with Raster-to-Vector Conversion and Use Them in GIS” explained the significance, workflow, and applications of digitizing historical maps. By scanning paper maps, georeferencing and preprocessing them, and performing raster-to-vector conversion, you can revive vast amounts of historical information in modern digital mapping environments. The resulting vector data can be used across a wide range of fields—from geographic research to disaster planning to surveying and design—serving as valuable datasets that connect past and present.

Digitizing historical maps may seem laborious at first, but with the right procedures it can be done efficiently. In particular, using the automation of raster-to-vector conversion significantly shortens working time and enables digitizing more materials. Combining this with new surveying technologies such as LRTK further enhances the accuracy and value of digitized historical map data.

Historical maps contain many insights applicable to the present. By digitizing them and analyzing them in a GIS, we can learn from the past and apply those lessons to today’s society. Take advantage of raster-to-vector conversion to explore the world of historical maps in the digital realm, and put accumulated local records and wisdom to use in future disaster prevention, regional planning, and academic research.