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

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

87
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
87
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
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...
27

You might also read

Related Articles

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

Sort by
Same author

EXPRESS: Exaggerated Self-Referencing in Body Dysmorphic Disorder.

Quarterly journal of experimental psychology (2006)·2026
Same author

3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment.

Scientific data·2026
Same author

Would you give four stars to a restaurant entirely staffed by robots?

Science robotics·2026
Same author

Trial Frequency Outweighs Trial Duration in Associative Learning: Generality and Boundary Conditions.

Quarterly journal of experimental psychology (2006)·2025
Same author

Sad, Angry and Fearful Facial Expressions Interfere With Perception of Causal Outcomes.

Quarterly journal of experimental psychology (2006)·2025
Same author

LSD increases sleep duration the night after microdosing.

Translational psychiatry·2024

Related Experiment Video

Updated: Jul 7, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment.

Maryam Rahnemoonfar1,2, Tashnim Chowdhury3, Robin Murphy4

  • 1Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania, 18015, USA. maryam@lehigh.edu.

Scientific Data
|December 20, 2023
PubMed
Summary

RescueNet is a new high-resolution dataset for natural disaster damage assessment. It offers detailed pixel-level annotations to improve scene understanding for rescue teams using computer vision.

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

2.8K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Related Experiment Videos

Last Updated: Jul 7, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
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

2.8K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Area of Science:

  • Computer Vision
  • Deep Learning
  • Remote Sensing

Background:

  • Advancements in computer vision and deep learning aid disaster response.
  • Precise damage assessment is crucial for effective rescue operations.

Purpose of the Study:

  • Introduce RescueNet, a novel high-resolution post-disaster dataset.
  • Facilitate comprehensive scene understanding for natural disaster aftermaths.

Main Methods:

  • Collected high-resolution post-disaster images using Unmanned Aerial Vehicles (UAVs) after Hurricane Michael.
  • Provided detailed pixel-level annotations for all scene classes (buildings, roads, trees, etc.).

Main Results:

  • RescueNet offers comprehensive annotations, surpassing existing datasets limited to specific elements.
  • Demonstrated the dataset's utility by implementing state-of-the-art segmentation models.

Conclusions:

  • RescueNet enhances methodologies for natural disaster damage assessment.
  • The dataset's detailed annotations are valuable for improving scene understanding in disaster scenarios.