Automatic Georeferencing of Historical Maps by Geocoding

Luft, Jonas

doi:10.21862/avhm2020.10

Cited by 3 publications

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“…Toponym recognition promises satisfactory results on small‐scale maps with low density of information. However, optical character recognition performs badly on topographic maps because of the large amount of clutter, overlapping features and irregular typesetting (Luft, 2020). Our method, on the other hand, is not greatly affected by clutter or unusual map designs.…”

Section: Discussionmentioning

confidence: 99%

“…We will compare their georeferencing results with ours in Section 4.2. Luft (2020) used a similar method of toponym detection and open geodata for geocoding. The text detection and recognition algorithms proved to be too error‐prone on cluttered, highly detailed topographic maps and led to unsatisfactory georeferencing results, demonstrating the fragility of toponym‐based methods and the sensitivity of georeferencing transform methods to noise and incorrect control point matches.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic content‐based georeferencing of historical topographic maps

Luft

Schiewe

2021

Transactions in GIS

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Coordinate georeferencing of geoimages using computer vision

Mukhametshin,

Samsonov,

Lurie

2024

G&C

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This paper presents the process of creating a model for automated georeferencing of geo- images using image matching technology, an increasingly popular computer vision concept that has not received proper attention in cartography to date. LoFTR (Local Feature Matching with Transformers), a new approach to find and match key points in pairs of images was used as a basis for the developed model. At the first stage, a model architecture and successive stages of input data processing were defined. At the second stage, the model was implemented as a program in accordance with the previously outlined steps. At the third stage, the resulting model was tested on several pairs of geoimages to further evaluate its effectiveness and applicability in various scientific tasks. Results show that the developed model provides a universal algorithm for automated georeferencing of geo-images, demonstrating high-quality results

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Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Uhl

Leyk

et al. 2021

Remote Sensing

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Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

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Automatic Georeferencing of Historical Maps by Geocoding

Cited by 3 publications

References 0 publications

Automatic content‐based georeferencing of historical topographic maps

Automatic content‐based georeferencing of historical topographic maps

Coordinate georeferencing of geoimages using computer vision

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

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