Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Selected Data About Geographic Locations

Selected Data About Geographic Locations

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...

Manipulation and Analysis

Manipulation and Analysis

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...

You might also read

Related Articles

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

Sort by

Same author

A model for classifying information objects using neural networks and fuzzy logic.

Scientific reports·2025

Same author

Features extraction from multi-spectral remote sensing images based on multi-threshold binarization.

Scientific reports·2023

Same author

Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis.

Journal of personalized medicine·2021

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 31, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical

Rostyslav Kosarevych¹, Oleksiy Lutsyk², Bohdan Rusyn²

¹Department of Remote Sensing Information Technologies, Karpenko Physico-Mechanical Institute, NAS of Ukraine, Lviv, Ukraine. kosar2311@gmail.com.

Scientific Reports

|August 22, 2022

Summary

This study introduces a novel method for generating spatial point patterns from remote sensing images using convolutional neural networks and data augmentation. The approach enhances image classification accuracy, improving environmental monitoring capabilities.

More Related Videos

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Related Experiment Videos

Last Updated: Aug 31, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

Area of Science:

Environmental Science
Computer Science
Geospatial Analysis

Background:

Technological advancements necessitate improved environmental monitoring.
Remote sensing offers a powerful tool for observing environmental changes.
Existing data augmentation methods for remote sensing image classification have limitations.

Purpose of the Study:

To develop a new method for spatial point pattern generation using classified remote sensing images.
To enhance the accuracy of remote sensing image classification through an advanced data augmentation scheme.
To analyze relationships between different spatial point patterns using marked point pattern analysis.

Main Methods:

Classification of remote sensing images using convolutional neural networks.
A novel data augmentation scheme based on image patch similarities within landscapes.
Generation of marked point patterns where class labels serve as marks.
Bivariate point pattern analysis to identify inter-point relationships.

Main Results:

The proposed data augmentation scheme improved image classification accuracy by 7% over current best practices.
Successfully generated spatial point patterns from classified remote sensing image patches.
Demonstrated the utility of marked point patterns for representing spatial data.
Identified relationships between different types of spatial points through bivariate analysis.

Conclusions:

The developed method offers a significant improvement in remote sensing image classification accuracy.
The approach provides a robust framework for spatial point pattern generation and analysis.
This work contributes to more effective environmental monitoring through advanced data analysis techniques.