Jove
Visualize
Contact Us
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 Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

281
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
281
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

342
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,...
342
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

841
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
841
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

570
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,...
570
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

417
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
417
Introduction to GIS01:28

Introduction to GIS

605
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
605

You might also read

Related Articles

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

Sort by
Same author

Mechanochemistry-driven acid-free strategy for synergistic recycling of mixed spent lithium-ion batteries.

Waste management (New York, N.Y.)·2026
Same author

Comparative transcriptome analysis of Qinchuan and Wagyu cattle reveals lnc11599 as a negative regulator of intramuscular fat deposition.

BMC genomics·2026
Same author

Deep learning for predicting lumbar segmental instability using neutral lateral lumbar radiographs: a retrospective study.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026
Same author

Single-nucleus transcriptomics dissects beef quality variation: coordinated reprogramming of myofiber metabolism, FAPs Fate, and ECM-vascular signaling.

BMC genomics·2026
Same author

Aberrant hypothalamic-cortical functional connectivity in spinal cord injury: a neuroimaging-transcriptomic study for motor recovery prognostication.

Journal of neurosurgery. Spine·2026
Same author

Precision medicine in bladder cancer: Redefining treatment paradigms through ADCs, immunotherapy, and molecular subtyping.

Critical reviews in oncology/hematology·2026

Related Experiment Video

Updated: Feb 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

A method for compiling satellite image map geographic objects based on vector map data via deep learning.

Jiawei Du1, Dexian Zeng2, Kaijun Cai2

  • 1Space Engineering University, Beijing, 100000, China. whdxdjw@126.com.

Scientific Reports
|February 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated deep learning method for satellite image map compilation, guided by vector data. The approach enables precise modification of geographic objects for enhanced map accuracy and clarity.

Keywords:
Deepfake cartographyGenerative adversarial networkRemote sensing cartographyTransfer learningVector map

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K
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

10.8K

Related Experiment Videos

Last Updated: Feb 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K
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

10.8K

Area of Science:

  • Cartography
  • Geographic Information Systems (GIS)
  • Computer Vision

Background:

  • Map compilation is crucial for cartography, extending to satellite imagery for clarity and data security.
  • Existing methods often lack automation and precision in modifying satellite map features.

Purpose of the Study:

  • To develop an automated deep learning framework for satellite image map compilation.
  • To enable selective and diverse compilation operations guided by vector data.

Main Methods:

  • Aligned and partitioned vector and satellite map data were used to train an encoder-decoder deep learning model.
  • Transfer learning fine-tuned the model for object-specific modifications.
  • Defined compilation operations (deletion, insertion, distortion, displacement) were applied to vector features before generating updated satellite images.

Main Results:

  • The deep learning model successfully learned the vector-to-satellite image mapping.
  • Transfer learning enhanced the model's sensitivity to targeted compilation areas.
  • The method demonstrated capability in compiling linear and polygonal objects through various operations on real-world datasets.

Conclusions:

  • The proposed automated method effectively performs selective and operation-diverse compilation of geographic objects in satellite image maps.
  • This approach offers a significant advancement in the automation and precision of satellite map updating and editing.