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

Methods of Obtaining Topography

Methods of Obtaining Topography

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,...

You might also read

Related Articles

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

Sort by

Same author

Correction: Zeng et al. High-Performance Silicon Nanowire Array Biosensor for Combined Detection of Colorectal Cancer Biomarkers. <i>Micromachines</i> 2025, <i>16</i>, 1089.

Micromachines·2025

Same author

High-Performance Silicon Nanowire Array Biosensor for Combined Detection of Colorectal Cancer Biomarkers.

Micromachines·2025

Same author

Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art.

Journal of imaging·2025

Same author

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation.

Journal of imaging·2024

Same author

VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction.

Journal of imaging·2023

Same author

Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review.

Journal of imaging·2023

Search research articles

Related Experiment Video

Updated: Jul 23, 2025

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

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

Published on: December 15, 2023

State-of-the-Art Deep Learning Methods for Objects Detection in Remote Sensing Satellite Images.

Adekanmi Adeyinka Adegun¹, Jean Vincent Fonou Dombeu², Serestina Viriri¹

¹School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4041, South Africa.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This summary is machine-generated.

Deep learning object detection in satellite images faces challenges. This study evaluated YOLO and R-CNN models on diverse landscapes, achieving over 90% accuracy for vegetation and swimming pools with YOLOv8 showing the fastest detection speed.

Keywords:

R-CNN YOLO object detection remote sensing satellite images

More Related Videos

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

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

Published on: November 30, 2022

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Jul 23, 2025

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

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

Published on: December 15, 2023

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

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

Published on: November 30, 2022

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Remote sensing and geospatial analysis.
Computer vision and deep learning applications.
Environmental and urban monitoring.

Background:

Object detection in satellite imagery is vital for disaster prevention, resource management, and urban planning.
Deep learning models struggle with heterogeneous landscapes, similar object classes, and diverse object appearances in remote sensing data.
Acquiring representative training data for complex remote sensing scenarios remains a significant challenge.

Purpose of the Study:

To evaluate the performance of deep learning-based object detection algorithms on remotely sensed satellite images.
To address challenges in object detection posed by landscape heterogeneity and data limitations.
To compare various object detection models, including R-CNN and YOLO architectures, using a newly created dataset.

Main Methods:

Utilized multi-object detection deep learning algorithms combined with a transfer learning approach.
Developed and employed a new dataset of diverse features from Google Earth Engine in southern KwaZulu-Natal, South Africa.
Experimentally evaluated five object detection methods based on R-CNN and YOLO architectures on the new dataset and two public datasets (Visdron, PASCAL VOC2007).

Main Results:

Comprehensive performance evaluation and analysis of recent deep learning object detection methods were conducted.
Highest detection accuracy exceeding 90% was achieved for vegetation and swimming pool instances.
The YOLOv8 model demonstrated the fastest detection speed, recorded at 0.2 milliseconds.

Conclusions:

Deep learning object detection models show promise for analyzing high-resolution remote sensing imagery.
The study highlights the effectiveness of YOLOv8 in terms of both accuracy and speed for specific object classes.
Transfer learning and tailored datasets are crucial for improving object detection performance in complex remote sensing applications.