Topographic Mapping Evolution: From Field and Photographically Collected Data to GIS Production and Linked Open Data

Usery, E. Lynn; Varanka, Dalia E.; Davis, Larry R.

doi:10.1080/00087041.2018.1539555

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…In general, nowadays map creation is heavily influenced by the availability of open data and free software. For the derivation of topographic maps, Usery et al (2018) describes how map making has changed in the past decades, starting with field data and photogrammetrically collected data to arrive at digital databases and geographic information systems.…”

Section: Map Production Todaymentioning

confidence: 99%

Mapping Mt. Ushba – How to Create a High-Quality Map Product From Open Data With Free Software

Gröbe

Prechtel

Burghardt

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Mt. Ushba is a famous mountain in the Great Caucasus characterized by its iconical double peak. In addition, the surrounding Georgian region of Svaneti is known for its medieval defense towers, what makes the area a rising tourism destination. Climbers, hikers and visitors alike need maps for planning their tours, so there is a demand for a reliable map. The paper aims to present the production of an Alpine Club map to demonstrate how high-quality map production is possible by combining open data and, wherever possible, open-source software. An important result is a widely transferable workflow for mapmakers. It offers lowcost solutions in the production of a digital elevation model, which combines multi-stereo satellite images from Planet Labs and GNSS measurements, and an organized, dynamic mapping with OpenStreetMap. Moreover, free satellite images have been used to derive land cover information. While the final map will be sold as a commercial product, most geo-data are freely available. The OpenStreetMap data is improved and completed through fieldwork, and toponyms are collected in a combination of existing maps, signposts and information by locals. Our contribution may strengthen the spread of this sustainable database to many other digital services, resp. maps, which are using OpenStreetMap. Due to the complexity of the overall project, this paper describes the current status, some aspects are still under development.

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Section: Map Production Todaymentioning

confidence: 99%

Mapping Mt. Ushba – How to Create a High-Quality Map Product From Open Data With Free Software

Gröbe

Prechtel

Burghardt

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…by Strobl and Nazarkulova (2014), Hanečák et al (2017), Lazorenko-Hevel (2017) and Corti et al (2018). The quality of these data was pondered by Devillers et al (2010), Usery et al (2018) and Hamad (2020). The tendency to follow requirements is defined to geospatial data according to a set of standards ISO 19100, even for volunteer geoinformation systems.…”

Section: Analysis Of Recent Researchmentioning

confidence: 99%

The method for the quality evaluation of open geospatial data for creation and updating of datasets for National Spatial Data Infrastructure in Ukraine

Kin

Lazorenko-Hevel

2021

Polish Cartographical Review

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The purpose of the article is to present the research on method of the quality evaluation of published open geospatial data and its implementation in Ukraine. The method of the quality evaluation of open geospatial data considers the international standard ISO 19157 “Geographic information. Data quality”. This method is to determine the number of points or levels (maximum – 5). The research was carried out for the evaluation of open geoinformation resources for production of geospatial datasets, as defined in the Ukrainian Law on NSDI. The authors evaluated the quality of 142 open geoinformation resources and other information resources (materials) for the production and updating of 34 geospatial datasets for the development of NSDI in Ukraine. The authors present the example of the quality evaluation of geospatial data for datasets: “State Geodetic Reference Coordinate System UCS-2000”, “State Geodetic Network”, “Geographical Names” and “Administrative Units” because they are the components of the Core Reference Dataset of NSDI. Limitations of the research were determined by the adopted the Law of Ukraine “On National Spatial Data Infrastructure” and the Order for NSDI functioning in Ukraine and the requirements of the international standard ISO 19157 “Geographic information. Data quality”. The results of the research will be employed to evaluate the quality of NSDI implementation in Ukraine. The proposed method allows evaluating the quality of open geospatial dataset before using them for analysis and modeling of terrain, phenomena. This method takes into account the quality of geospatial data, and its related requirements for their production, updating and publication.

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“…First, a color-space segmentation of the candidate patches is carried out using k-means clustering with k = 5 in the RGB space. While historical USGS topographic maps were initially printed in three colors and later using five colors [66], the texture of the paper and the chosen bit depth during the scanning process artificially increase color complexity in the scanned document. Thus, color reduction decreases the complexity of the scanned image without losing important information.…”

Section: ) Collecting Building and Non-settlement Training Datamentioning

confidence: 99%

Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks

et al. 2020

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Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and the large data volume of digital map archives, which can hold thousands of digitized map sheets. Traditional map processing techniques typically rely on manually collected templates of the symbol of interest, and thus are not suitable for large-scale information extraction. In order to digitally preserve such large amounts of valuable retrospective geographic information, high levels of automation are required. Herein, we propose an automated machine-learning based framework to extract human settlement symbols, such as buildings and urban areas from historical topographic maps in the absence of training data, employing contemporary geospatial data as ancillary data to guide the collection of training samples. These samples are then used to train a convolutional neural network for semantic image segmentation, allowing for the extraction of human settlement patterns in an analysis-ready geospatial vector data format. We test our method on United States Geological Survey historical topographic maps published between 1893 and 1954. The results are promising, indicating high degrees of completeness in the extracted settlement features (i.e., recall of up to 0.96, F-measure of up to 0.79) and will guide the next steps to provide a fully automated operational approach for large-scale geographic feature extraction from a variety of historical map series. Moreover, the proposed framework provides a robust approach for the recognition of objects which are small in size, generalizable to many kinds of visual documents.INDEX TERMS Convolutional neural networks, digital humanities, digital preservation, document analysis, geospatial analysis, geospatial artificial intelligence, human settlement patterns, image analysis, weakly supervised learning.

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Topographic Mapping Evolution: From Field and Photographically Collected Data to GIS Production and Linked Open Data

Cited by 7 publications

References 8 publications

Mapping Mt. Ushba – How to Create a High-Quality Map Product From Open Data With Free Software

Mapping Mt. Ushba – How to Create a High-Quality Map Product From Open Data With Free Software

The method for the quality evaluation of open geospatial data for creation and updating of datasets for National Spatial Data Infrastructure in Ukraine

Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks

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