Satellite Data Analysis for Disaster Management . . . . .
SDA_DEMO.mp4
. . . . . . 📊 Project Overview: Satellite Data Analysis for Disaster Management This project presents a comprehensive Streamlit-based dashboard designed for analyzing satellite imagery to assess environmental changes, particularly in the context of natural disasters. The solution leverages machine learning and deep learning algorithms—notably CNN, SVM, and KMeans—to perform tasks such as water body detection, land-use change monitoring, and disaster impact assessment.
Users can upload pre- and post-disaster satellite images, align them spatially, apply advanced classification models, and visualize changes via heatmaps, pie charts, and data tables. The system supports intelligent model selection, PCA preprocessing, and a modular page structure to ensure usability and clarity.
With its fusion of geospatial data, AI techniques, and intuitive visualization, this project offers a powerful tool for decision-makers in disaster response, urban planning, and environmental monitoring.
Let me know if you'd like to add highlights like accuracy, results, deployment links, or demo screenshots to this review.
✅ Step-by-Step Project Workflow 🔹 Step 1: Project Objective The aim of this project is to build a dashboard that:
Detects water bodies and calamity-affected areas from satellite images.
Compares before and after imagery using machine learning models.
Provides visual insights through maps, pie charts, and statistical summaries.
🔹 Step 2: Technology Stack Frontend: Streamlit (for dashboard UI)
Backend: Python
ML/DL Models: CNN, SVM, PCA
Libraries: OpenCV, scikit-learn, TensorFlow/Keras, Matplotlib, NumPy, Pandas
🔹 Step 3: Functional Modules 📁 Module 1: Image Upload Users upload two satellite images: Before and After a disaster event.
File validation and basic preprocessing are done.
📁 Module 2: Image Alignment Ensures spatial alignment of both images for accurate comparison.
Uses geometric transformations or keypoint matching (e.g., ORB, SIFT).
📁 Module 3: Model Selection Users choose a detection method:
CNN + SVM: For detailed classification.
SVM + KMeans: For faster segmentation and clustering.
Optionally applies PCA for dimensionality reduction and noise filtering.
📁 Module 4: Analysis Applies the selected model on both images.
Detects land-use classes, especially water and changed regions.
Highlights areas of interest and calculates:
% change in land usage
% increase or decrease in water body coverage
📁 Module 5: Results & Visualization Displays side-by-side comparisons of before and after images.
Generates:
Change maps/heatmaps
Pie charts excluding irrelevant classes like "Urban"
Tabular summaries of percentage change
Allows export/download of results
Step 5: Output & Impact 🛰️ Accurately identifies flood-affected or drought-prone areas.
📊 Provides stakeholders with clear visualizations and metrics.
🔄 Facilitates data-driven decisions for disaster management teams, urban planners, and NGOs