Generating Named Road Vector Data from Raster Maps

Chiang, Yao‐Yi; Knoblock, Craig A.

doi:10.1007/978-3-642-33024-7_5

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…In addition, raster maps for monthly Global Aridity Index (GAI) were obtained from https://cgiarcsi.community/data/global-aridity-and-pet-database/ . Bioclimatic variables were then extracted using the R packages raster (v.3.5-2; Hijmans and van Etten 2012 ) and rgdal (v.1.5-27; Keitt et al . 2010 ) for each of the 332 accessions.…”

Section: Methodsmentioning

confidence: 99%

Polygenic architecture of flowering time and its relationship with local environments in the grass Brachypodium distachyon

Minadakis,

Kaderli,

Horvath

et al. 2024

Genetics

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Synchronizing the timing of reproduction with the environment is crucial in the wild. Among the multiple mechanisms annual plants evolved to sense their environment, the requirement of cold-mediated vernalization is a major process that prevents individuals from flowering during winter. In many annual plants including crops, both a long and short vernalization requirement can be observed within species, resulting in so-called early-(spring) and late (winter)-flowering genotypes. Here, using the grass model Brachypodium distachyon, we explored the link between flowering time-related traits (vernalization requirement and flowering time), environmental variation, and diversity at flowering-time genes by combining measurements under greenhouse and outdoor conditions. These experiments confirmed that B. distachyon natural accessions display large differences regarding vernalization requirements and ultimately flowering time. We underline significant, albeit quantitative effects of current environmental conditions on flowering time-related traits. While disentangling the confounding effects of population structure on flowering time-related traits remains challenging, population genomics analyses indicate that well-characterized flowering-time genes may contribute significantly to flowering time variation and display signs of polygenic selection. Flowering-time genes, however, do not colocalize with GWAs peaks obtained with outdoor measurements, suggesting that additional genetic factors contribute to flowering time variation in the wild. Altogether, our study fosters our understanding of the polygenic architecture of flowering time in a natural grass system and opens new avenues of research to investigate the gene-by-environment interaction at play for this trait.

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Section: Methodsmentioning

confidence: 99%

Polygenic architecture of flowering time and its relationship with local environments in the grass Brachypodium distachyon

Minadakis,

Kaderli,

Horvath

et al. 2024

Genetics

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“…Textual label is an important type of information that we often use to find the initial set of matching entities on two maps. Here, the textual labels are already extracted and assigned to their corresponding geographic entities, and there exist methods for doing so, such as the one from Chiang and Knoblock (2012) which is based on the orientations of textual labels and their distances to corresponding geographic entities. We consider nine possible methods for the step of textual label alignment.…”

Section: Textual Label Alignmentmentioning

confidence: 99%

Aligning geographic entities from historical maps for building knowledge graphs

Sun

Song

et al. 2020

International Journal of Geographical Information Science

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Historical maps contain rich geographic information about the past of a region. They are sometimes the only source of information before the availability of digital maps. Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images. It is even more timeconsuming and labor-intensive to conduct an analysis that requires a synthesis of the information from multiple historical maps. To facilitate the use of the geographic information contained in historical maps, one way is to build a geographic knowledge graph (GKG) from them. This paper proposes a general workflow for completing one important step of building such a GKG, namely aligning the same geographic entities from different maps. We present this workflow and the related methods for implementation, and systematically evaluate their performances using two different datasets of historical maps. The evaluation results show that machine learning and deep learning models for matching place names are sensitive to the thresholds learned from the training data, and a combination of measures based on string similarity, spatial distance, and approximate topological relation achieves the best performance with an average F-score of 0.89.

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“…Second, in feature-based georeferencing, the goal is to detect one or more thematic feature layers shown in the map (e.g., roads), and then compare that information with some reference data containing known coordinates [ 47 – 52 ]. The primary challenges identified in this literature include, first, how to accurately identify the thematic feature layers, and second, how to efficiently compare complex map feature s with a potentially much larger global reference set (i.e., the problem of point set conflation; [ 53 , 54 ]).…”

Section: Introductionmentioning

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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Generating Named Road Vector Data from Raster Maps

Cited by 4 publications

References 9 publications

Polygenic architecture of flowering time and its relationship with local environments in the grass Brachypodium distachyon

Polygenic architecture of flowering time and its relationship with local environments in the grass Brachypodium distachyon

Aligning geographic entities from historical maps for building knowledge graphs

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

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