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Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

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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...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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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...
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Responses to Drought and Flooding02:41

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Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
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Related Experiment Video

Updated: May 27, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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DeepFlood for Inundated Vegetation High-Resolution Dataset for Accurate Flood Mapping and Segmentation.

Mulham Fawakherji1, Jeffrey Blay1, Matilda Anokye1

  • 1Department of Built Environment, College of Science and Technology, North Carolina A&T State University, Greensboro, NC, USA.

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|February 15, 2025
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Summary

DeepFlood, a new dataset of aerial and SAR imagery, enhances deep learning for accurate flood mapping, especially for challenging inundated vegetation. This aids effective disaster response and mitigation planning.

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Area of Science:

  • Earth and Environmental Sciences
  • Computer Science
  • Remote Sensing

Background:

  • Accurate flood extent mapping is crucial for disaster management but faces scalability and transferability issues with traditional methods.
  • Deep learning, especially Convolutional Neural Networks (CNNs), offers a promising approach for automated flood mapping by learning spatial patterns.
  • Existing datasets often lack the comprehensive annotations and diverse coverage needed for robust deep learning models.

Purpose of the Study:

  • Introduce DeepFlood, a novel, high-resolution dataset for training deep learning models for flood mapping.
  • Enable multi-modal flood mapping approaches using diverse imagery sources.
  • Address the limitations of current datasets by providing detailed labels, including inundated vegetation.

Main Methods:

  • Developed DeepFlood dataset with high-resolution manned/unmanned aerial and Synthetic Aperture Radar (SAR) imagery.
  • Annotated imagery with detailed labels, focusing on challenging features like inundated vegetation.
  • Evaluated various semantic segmentation architectures on the DeepFlood dataset.

Main Results:

  • Demonstrated the usability and efficacy of the DeepFlood dataset in post-disaster flood mapping scenarios.
  • Showcased the potential of deep learning models trained on DeepFlood for accurate flood extent assessment.
  • Highlighted the dataset's capability to support multi-modal approaches.

Conclusions:

  • DeepFlood provides a valuable resource for advancing deep learning-based flood mapping.
  • The dataset facilitates improved accuracy and efficiency in disaster response and mitigation planning.
  • Future research can leverage DeepFlood for developing more sophisticated flood prediction and management tools.