Jove
Visualize
Contact Us

Related Concept Videos

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

95
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
95
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

106
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
106
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

210
Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
210
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

87
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
87
Profile Leveling and Cross Sections01:26

Profile Leveling and Cross Sections

467
Profile leveling and cross-sections are surveying methods used to determine and document terrain elevations for infrastructure projects such as highways, railroads, canals, and pipelines. These methods provide data for earthwork planning and alignment of proposed routes.  Profile leveling involves measuring elevations along a fixed line to create a vertical terrain profile. A surveyor sets up a leveling instrument at the benchmark (BM) and records a backsight (BS) to determine the...
467
Manipulation and Analysis01:21

Manipulation and Analysis

53
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
53

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records.

NPJ digital medicine·2025
Same author

Enhancing biomedical relation extraction through data-centric and preprocessing-robust ensemble learning approach.

Database : the journal of biological databases and curation·2025
Same author

Scale-Aware Crowd Counting Network With Annotation Error Modeling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Integrating manual preprocessing with automated feature extraction for improved rodent seizure classification.

Epilepsy & behavior : E&B·2025
Same author

Scattering-based structural inversion of soft materials via Kolmogorov-Arnold networks.

The Journal of chemical physics·2025
Same author

The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.

Database : the journal of biological databases and curation·2024
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 22, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

Semi-supervised learning for topographic map analysis over time: a study of bridge segmentation.

Cheng-Shih Wong1, Hsiung-Ming Liao1, Richard Tzong-Han Tsai2

  • 1Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, 115201, Taiwan.

Scientific Reports
|November 8, 2022
PubMed
Summary

This study introduces a semi-supervised learning method for analyzing historical maps, reducing manual annotation needs. The framework enables effective feature detection on digitized maps across different years using style transfer and AI.

More Related Videos

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.9K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

Related Experiment Videos

Last Updated: Aug 22, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.9K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

Area of Science:

  • Geographical Information Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Digitalization of historical maps and AI advancements offer new research avenues.
  • Supervised learning for historical map analysis is hindered by extensive manual annotation requirements.

Purpose of the Study:

  • To develop a semi-supervised learning method for historical map analysis.
  • To enable cross-year map comparison and anthropogenic studies by transferring annotations.
  • To reduce the labor of manual map annotations.

Main Methods:

  • A novel two-stage framework involving style transfer of topographic maps across years and versions.
  • Application of supervised learning on synthesized maps with transferred annotations.
  • Investigation using four deep neural networks (DNNs): U-Net, FCN, DeepLabV3, and MobileNetV3.

Main Results:

  • The U-Net model achieved high performance metrics ([Formula: see text] and [Formula: see text]) on style-transfer synthesized maps for detecting features like bridges.
  • The proposed framework successfully detected target features on historical maps without manual annotations.
  • U-Net trained on Contrastive Unpaired Translation (CUT) generated data outperformed other configurations by 57.3%.

Conclusions:

  • The proposed semi-supervised learning framework effectively enables analysis of historical maps across different time periods.
  • The method significantly reduces the need for manual annotations, making historical map analysis more efficient.
  • Future research should address remaining challenges and explore further applications of this technique.