Creating a dataset of historic roads in Sydney from scanned maps

Turner, Hamish; Lahoorpoor, Bahman; Levinson, David M.

doi:10.1038/s41597-023-02574-5

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…Their production was often coordinated by various researchers and government entities, although on many occasions they were produced by a large number of different people (Perret et al ., 2015). Finally, and only very recently, there is a tendency towards the digitisation and use of historical maps through the application of artificial intelligence and various automated processes, although this method is still in its early stages (Turner et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

The Spanish railway network, 1848–2023

Esteban-Oliver,

Martí-Henneberg

2024

RHE / JILAEH

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GIS data on the evolution of railway networks facilitate the study of the role played by the expansion of transport infrastructure since the industrial revolution. The arrival of the railway transformed economic and social activity and the distribution of population within the territory. Given their importance, we have reconstructed and digitised the layout of the railway lines and the location of the stations and halts that existed from the opening of Spain's first railway line, in 1848, until 2023. We have also added indicators of the quality of the network, more specifically, the dates of its electrification and when the track was doubled to allow two-way traffic. The potential of this database lies in its capacity to analyse the interrelationship between the railway infrastructure and a wide range of elements located in the territory, amongst which it is necessary to highlight other modes of transport, urban expansion and socio-economic development.

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Section: Introductionmentioning

confidence: 99%

The Spanish railway network, 1848–2023

Esteban-Oliver,

Martí-Henneberg

2024

RHE / JILAEH

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GIS-based relationship between pathway names and landscape. A multilingual case study: Euskadi, Spain

Mitxelena-Hoyos,

Amaro-Mellado

2024

GeoJournal

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Place names determine geographic units in space, encapsulate the description of places where inhabitants conduct their activities, and preserve the perception of the territory in past times. The very definition of landscape impacts two interrelated concepts: human action and perception. On another note, traditional pathways are structuring elements of the landscape, bearing witness to the dynamic relationship between the territory and its inhabitants by connecting residences and workplaces. This work aims to assess the relationship between the landscape mosaic and the toponyms of pathways and roads gathered in the current cartography of Euskadi (Spain), a territory influenced by the existence of two official languages. Given the spatial component of the data, this analysis is conducted through geographic information systems. Firstly, a corpus of 3072 pathway names selected from current official toponymic databases is compiled, as well as the content of the first edition of the National Topographic Map. Subsequently, the semantic content of the corpus elements is examined, as well as the nature of their referential content concerning the landscape units obtained from the Atlas of Spanish Landscapes. The results show common factors in characterizing landscapes and the etymology of names. Thus, it is noted that traditional agricultural, forestry, livestock, and traditional industry activities shape the toponymy in the most populated landscape units. Meanwhile, references to the orography typify the more mountainous landscapes. Therefore, the geographical study demonstrates the existence of common factors that link landscape and toponymy, validating one as a study source for the other.

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Automatic Road Extraction from Historical Maps Using Transformer-Based SegFormers

Sertel,

Hucko,

Kabadayı

2024

IJGI

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Historical maps are valuable sources of geospatial data for various geography-related applications, providing insightful information about historical land use, transportation infrastructure, and settlements. While transformer-based segmentation methods have been widely applied to image segmentation tasks, they have mostly focused on satellite images. There is a growing need to explore transformer-based approaches for geospatial object extraction from historical maps, given their superior performance over traditional convolutional neural network (CNN)-based architectures. In this research, we aim to automatically extract five different road types from historical maps, using a road dataset digitized from the scanned Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps. We applied the variants of the transformer-based SegFormer model and evaluated the effects of different encoders, batch sizes, loss functions, optimizers, and augmentation techniques on road extraction performance. Our best results, with an intersection over union (IoU) of 0.5411 and an F1 score of 0.7017, were achieved using the SegFormer-B2 model, the Adam optimizer, and the focal loss function. All SegFormer-based experiments outperformed previously reported CNN-based segmentation models on the same dataset. In general, increasing the batch size and using larger SegFormer variants (from B0 to B2) resulted in improved accuracy metrics. Additionally, the choice of augmentation techniques significantly influenced the outcomes. Our results demonstrate that SegFormer models substantially enhance true positive predictions and resulted in higher precision metric values. These findings suggest that the output weights could be directly applied to transfer learning for similar historical maps and the inference of additional DHK maps, while offering a promising architecture for future road extraction studies.

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Creating a dataset of historic roads in Sydney from scanned maps

Cited by 3 publications

References 52 publications

The Spanish railway network, 1848–2023

The Spanish railway network, 1848–2023

GIS-based relationship between pathway names and landscape. A multilingual case study: Euskadi, Spain

Automatic Road Extraction from Historical Maps Using Transformer-Based SegFormers

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