Deformable Part Models for Automatically Georeferencing Historical Map Images

Howe, Nicholas R.; Weinman, J.; Gouwar, John; Shamji, Aabid

doi:10.1145/3347146.3359367

Cited by 10 publications

(13 citation statements)

References 13 publications

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“…RANSAC not only outputs a score, but also a transform model, which can be used for alignment of the geolocalised maps. However, these transformations did not consistently produce acceptable registration accuracy on their own and should be supplemented with one of the proposed methods of alignment in order to exploit more available spatial information (Howe et al, 2019, Luft and Schiewe, 2021.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, georeferencing can significantly increase the accessibility of the important cultural heritage that are historical maps (Crom, 2016, Buckley, 2019. Some work has been done on the constrained task of aligning two maps which are roughly covering the same area (e. g. Howe et al, 2019. However, to solve the general task of geolocating a map with little or no prior information on location, maps have to be compared against large volumes of comparable reference geodata, possibly across the globe.…”

Section: Introductionmentioning

confidence: 99%

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Content-based Image Retrieval for Map Georeferencing

Luft

Schiewe

2021

Proc. Int. Cartogr. Assoc.

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Abstract. In recent years, libraries have made great progress in digitising troves of historical maps with high-resolution scanners. Providing user-friendly information access for cultural heritage through spatial search and webGIS requires georeferencing of the hundreds of thousands of digitised maps.Georeferencing is usually done manually by finding “ground control points”, locations in the digital map image, whose identity is unambiguous and can easily be found in modern-day reference geodata/mapping data. To decide whether two symbols from different maps describe the same object, their semantic and spatial relations need to be matched. Automating this process is the only feasible way to georeference the immense quantities of maps in conceivable time. However, automated solutions for spatial matching quickly fail when faced with incomplete data – which is the greatest challenge when comparing maps of different ages or scales.These problems can be overcome by computing map similarity in the image domain. Treating maps as a special case of image processing allows efficient and robust matching and thus identification of geographical regions without the need to explicitly model semantics. We propose a method to encode worldwide reference VGI mapping data as image features, allowing the construction of an efficient lookup index. With this index, content-based image retrieval can be used for both geolocating a given map for georeferencing with high accuracy. We demonstrate our approach on hundreds of map sheets of different historical topographical survey map series, successfully georeferencing most of them within mere seconds.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Content-based Image Retrieval for Map Georeferencing

Luft

Schiewe

2021

Proc. Int. Cartogr. Assoc.

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“…Possible avenues for improved matching include repeated step-wise georeferencing to incrementally refine and limit the candidates to be searched [55,56,58] or the use of more sophisticated point set registration methods [56,[87][88][89]. Assuming a set of matched points, there are also a number of alternative non-polynomial transform estimation methods that have been suggested [99,102,103] that may or may not result in more accurate map transformations, particularly for larger-extent maps.…”

Section: Future Workmentioning

confidence: 99%

Toponym-assisted map georeferencing: Evaluating the use of toponyms for the digitization of map collections

Bahgat

Runfola

2021

PLoS ONE

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A great deal of information is contained within archival maps—ranging from historic political boundaries, to mineral resources, to the locations of cultural landmarks. There are many ongoing efforts to preserve and digitize historic maps so that the information contained within them can be stored and analyzed efficiently. A major barrier to such map digitizing efforts is that the geographic location of each map is typically unknown and must be determined through an often slow and manual process known as georeferencing. To mitigate the time costs associated with the georeferencing process, this paper introduces a fully automated method based on map toponym (place name) labels. It is the first study to demonstrate these methods across a wide range of both simulated and real-world maps. We find that toponym-based georeferencing is sufficiently accurate to be used for data extraction purposes in nearly half of all cases. We make our implementation available to the wider research community through fully open-source replication code, as well as an online georeferencing tool, and highlight areas of improvement for future research. It is hoped that the practical implications of this research will allow for larger and more efficient processing and digitizing of map information for researchers, institutions, and the general public.

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“…Howe, Weinman, Gouwar, and Shamji, (2019) implemented an approach to content‐based georeferencing on small‐scale political and thematic maps. They detect and geocode toponyms using convolutional neural networks trained on synthetic training data.…”

Section: Introductionmentioning

confidence: 99%

Automatic content‐based georeferencing of historical topographic maps

Luft

Schiewe

2021

Transactions in GIS

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Old maps are more than a cultural artefact: they are data. Data about the past still hold value for science and decision‐making today. Libraries and archives have come a long way in digitising their inventories of thousands, sometimes millions, of historical maps using high‐resolution scanning. Unfortunately, even with digital images the rich spatial and semantic information is inaccessible for people without a strong background in history and cartography. Only with georeferencing can historical maps be used in GIS and thus processed and compared with modern geospatial data. We introduce content‐based image retrieval to automatically localise and georeference map images from topographic map series. We align the maps by extracting a subset of their symbols and cross‐referencing them with online reference data from OpenStreetMap. We demonstrate our method with the Karte des Deutschen Reiches at 1: 100,000 scale, obtaining 96% correct location predictions and a median georeferencing error of 101 m.

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Deformable Part Models for Automatically Georeferencing Historical Map Images

Cited by 10 publications

References 13 publications

Content-based Image Retrieval for Map Georeferencing

Content-based Image Retrieval for Map Georeferencing

Toponym-assisted map georeferencing: Evaluating the use of toponyms for the digitization of map collections

Automatic content‐based georeferencing of historical topographic maps

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