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

Rapidly Varying Flow01:24

Rapidly Varying Flow

121
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
121
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

90
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
90
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

139
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...
139
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

124
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
124

You might also read

Related Articles

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

Sort by
Same author

A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction.

Sensors (Basel, Switzerland)·2021
Same author

RAVA: Region-Based Average Video Quality Assessment.

Sensors (Basel, Switzerland)·2021
Same author

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring.

Sensors (Basel, Switzerland)·2021
Same author

Marker-Less 3d Object Recognition and 6d Pose Estimation for Homogeneous Textureless Objects: An RGB-D Approach.

Sensors (Basel, Switzerland)·2020
Same author

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson's Disease.

Sensors (Basel, Switzerland)·2019
Same author

Allele-specific real-time PCR testing for minor HIV-1 drug resistance mutations: assay preparation and application to reveal dynamic of mutations in vivo.

Chinese medical journal·2011
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 22, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

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

Published on: February 25, 2021

3.9K

FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring.

M D Samiul Islam1, Xinyao Sun1, Zheng Wang2

  • 1Multimedia Research Centre, University of Alberta, Edmonton, AB T6G 2E8, Canada.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

A new Fusion Adaptive Patch Network (FAPNET) improves satellite waterbody segmentation using advanced data fusion and adaptive augmentation. This method achieves higher accuracy and faster training than existing models, enhancing flood-water mapping.

Keywords:
SAR imageryflood-water mappingimage segmentationsatellite image analysiswaterbody detection

More Related Videos

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.3K
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.3K

Related Experiment Videos

Last Updated: Aug 22, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

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

Published on: February 25, 2021

3.9K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.3K
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.3K

Area of Science:

  • Remote Sensing and Earth Observation
  • Artificial Intelligence for Geospatial Analysis
  • Computer Vision for Environmental Monitoring

Background:

  • Waterbody segmentation is crucial for mapping and monitoring surface water dynamics using satellite imagery.
  • Challenges in satellite image segmentation include cloud cover, limited labeled data, low lighting, and terrain interference.
  • Convolutional Neural Networks (CNNs) show promise but face issues with resolution, class balance, and computational overhead.

Purpose of the Study:

  • To develop an improved waterbody segmentation method for satellite remote sensing applications.
  • To address limitations of existing CNNs, including resolution, class balance, and computational cost.
  • To enhance the accuracy and robustness of surface water mapping, particularly for flood-water detection.

Main Methods:

  • Proposed a novel Fusion Adaptive Patch Network (FAPNET) incorporating a multi-channel Data-Fusion Module (DFM) and Neural Adaptive Patch (NAP) augmentation.
  • Implemented a re-weight class balancing technique (PHR-CB) to ensure prediction quality.
  • Utilized Sentinel-1 SAR microwave signals for segmentation and compared FAPNET against UNET, VNET, and other models across four experimental setups.

Main Results:

  • FAPNET, particularly the PHR-CB setup, demonstrated superior performance across all tested models.
  • FAPNET achieved a Mean Intersection over Union (MeanIoU) score of 87.06%, outperforming the state-of-the-art UNET (79.54%).
  • FAPNET exhibited a shorter training time (6.77 min for 5 epochs) comparable to UNET, with improved robustness to lighting and weather conditions.

Conclusions:

  • FAPNET effectively detects salient features and distinguishes micro waterbodies with high accuracy.
  • The proposed method is lightweight, computationally inexpensive, and robust, suitable for industrial applications.
  • SAR signals provide more accurate flood-water mapping compared to RGB images, validated by the FAPNET architecture.