Disaster Management Using Satellite Imagery and Deep Learning

Project Overview

The "Disaster Management" project focuses on leveraging satellite imagery and advanced deep learning techniques to extract actionable insights about environmental health and predict natural disasters such as wildfires and floods. The project also includes predicting disaster burdens and creating user-friendly web-based applications to visualize and disseminate critical information for decision-making.

 

Key Objectives

1. Canopy Health Assessment : Convert raw satellite imagery into meaningful data about the health of vegetation canopies.

2. Natural Disaster Prediction : Develop predictive models using deep learning to identify potential wildfire and flood risks.

3. Disaster Burden Estimation : Estimate the potential impact (e.g., economic, environmental, human) of predicted disasters.

4. Interactive Visualization : Create intuitive maps and integrate them into a web-based application for easy access and interpretation by stakeholders.

 

Technical Approach

 

1. Data Acquisition and Preprocessing

► Collected high-resolution satellite imagery from sources such as Sentinel-2, Landsat, or MODIS.

► Processed raw imagery to remove noise, correct atmospheric distortions, and enhance features relevant to vegetation health and disaster prediction.

► Extracted key indices like NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) to assess canopy health and water presence.

 

2. Deep Learning Model Development

► Designed and trained convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze spatial and temporal patterns in satellite data.

► Used labeled datasets of historical wildfire and flood events to train supervised learning models.

► Implemented transfer learning techniques to improve model accuracy and reduce training time.

 

3. Disaster Burden Prediction

► Integrated socioeconomic and environmental data (e.g., population density, infrastructure maps) with disaster predictions to estimate potential impacts.

► Developed regression models to quantify disaster burdens in terms of affected areas, economic losses, and resource requirements.

 

4. Mapping and Visualization

► Converted processed data into interactive maps using GIS tools like QGIS or ArcGIS.

► Visualized canopy health, disaster risk zones, and burden estimates using color-coded heatmaps and overlays.

► Exported geospatial data in formats compatible with web mapping libraries (e.g., Leaflet.js, Mapbox).

 

5. Web-Based Application Development

► Built a responsive web app using frameworks like Flask/Django (backend) and React.js (frontend).

► Integrated REST APIs to fetch real-time data and display dynamic maps and predictions.

► Ensured scalability and performance optimization for handling large datasets and concurrent users.

 

Tools and Technologies

► Programming Languages : Python, JavaScript

► Libraries and Frameworks : PyTorch, NumPy, Pandas, OpenCV, Folium, Leaflet.js

► Data Processing : GDAL, Rasterio, GeoPandas

► Web Development : Django, React.js, HTML/CSS

► Cloud Platforms : Google Earth Engine (for satellite data processing)

► Visualization Tools : Matplotlib, Plotly

 

 

 

 

Final Review

The "Disaster Management" project demonstrates the power of combining satellite imagery, deep learning, and web development to address real-world challenges. By transforming raw data into actionable insights, this project has the potential to save lives, protect ecosystems, and mitigate economic losses caused by natural disasters.