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GeoSpatial Info Processing and Decision Making System - Coggle Diagram
GeoSpatial Info Processing and Decision Making System
Data Processing System Overview
1. Data Ingestion Module
Purpose:
Collect and integrate diverse datasets, including geophysical surveys, geospatial imagery, and text-based documents.
Key Components:
APIs:
Integrate data from Sentinel-2, Landsat, USGS, and other sources.
Formats Supported:
CSV, GeoTIFF, SEG-Y, Shapefiles, JSON, XML.
Automation Tools:
Python (requests, sentinelsat) for automated downloading.
Technologies:
GDAL for geospatial raster data.
PyPDF2 and docx for document ingestion.
Database: PostgreSQL with PostGIS for spatial data storage.
2. Data Preprocessing Module
Purpose:
Clean, normalize, and transform raw data into a standardized format.
Key Components:
Cleaning:
Handle missing values, outliers, and duplicates using Pandas.
Normalization:
Reproject spatial data to EPSG:4326 using pyproj.
File Validation:
Check file integrity and compatibility.
Technologies:
GeoPandas for spatial preprocessing.
Fiona for reading/writing geospatial files.
Custom Python scripts for unit conversions (e.g., meters to feet).
3. Data Storage Module
Purpose:
Store and organize processed datasets for efficient retrieval.
Key Components:
Database Design:
Spatial database schema for handling vector, raster, and tabular data.
Indexing:
Use spatial indexing (e.g., R-trees) for fast queries.
Version Control:
Maintain dataset history for reproducibility.
Technologies:
PostgreSQL with PostGIS for geospatial data.
Elasticsearch for text-based document indexing.
File storage: AWS S3 for large raster datasets.
4. Anomaly Detection Module
Purpose:
Identify patterns and anomalies in geophysical and geospatial data.
Key Components:
Geophysical Analysis:
Filter magnetic and seismic data using FFT and derivatives.
Clustering:
Group anomalies using DBSCAN or K-means.
Threshold Detection:
Flag areas exceeding predefined geophysical thresholds.
Technologies:
Scikit-learn for clustering.
NumPy and SciPy for signal processing.
AI models: Gradient Boosting for pattern recognition.
5. Predictive Modeling Module
Purpose:
Build machine learning models to predict mineral prospectivity.
Key Components:
Training Data:
Use labeled historical datasets from the VIX Group.
Feature Engineering:
Extract features from geophysical and geochemical data.
Model Types:
Random Forest, SVM, and Neural Networks.
Technologies:
TensorFlow or PyTorch for deep learning.
Scikit-learn for classical ML models.
Validation: Cross-validation and grid search for hyperparameter tuning.
6. Geospatial Analysis Module
Purpose:
Process and analyze geospatial datasets for mapping and decision-making.
Key Components:
Layer Management:
Overlay raster and vector layers for combined analysis.
Terrain Analysis:
Generate DEMs, slopes, and hillshades.
Spatial Queries:
Identify intersections of anomalies and target zones.
Technologies:
QGIS and ArcGIS for manual validation.
GeoPandas and Shapely for automated spatial operations.
Visualization: Folium and Kepler.gl.
7. Visualization Module
Purpose:
Create interactive maps and charts to present insights.
Key Components:
Mapping:
Interactive maps with highlighted anomalies.
Charting:
Time-series plots for trends and histograms for distributions.
Dashboards:
Combine maps and metrics into a single interface.
Technologies:
Dash and Plotly for dashboards.
Matplotlib for static visualizations.
Leaflet.js for web-based maps.
8. Reporting Module
Purpose:
Generate reports summarizing findings and recommendations.
Key Components:
Automated Summaries:
Use NLP to create readable summaries of results.
Export Options:
PDF, HTML, and CSV formats for outputs.
Customization:
Allow users to include specific datasets or visualizations.
Technologies:
Jinja2 for templating.
WeasyPrint or ReportLab for PDF generation.
NLP: spaCy for summarization.
9. Notification System
Purpose:
Alert users to new data insights or updates.
Key Components:
Triggers:
Automatic alerts based on anomaly detection or new data uploads.
Delivery Methods:
Email, SMS, or in-app notifications.
Scheduling:
Allow users to set notification preferences.
Technologies:
SMTP for email.
Twilio for SMS.
In-app: WebSocket for real-time notifications.
10. User Interface Module
Purpose:
Provide a seamless interface for interacting with the system.
Key Components:
Data Uploads:
Drag-and-drop functionality for users to add datasets.
Map Navigation:
Pan, zoom, and toggle layers on maps.
User Management:
Authentication and role-based access control.
Technologies:
Front-End: React or Angular.
Back-End: Flask or FastAPI for API development.
Security: OAuth 2.0 for authentication.
System Workflow Overview
Data Ingestion:
Fetch raw data through APIs and user uploads.
Preprocessing:
Clean, normalize, and validate datasets.
Storage:
Save processed data in a spatial database for retrieval.
Analysis:
Detect anomalies and generate predictions using AI/ML models.
Visualization:
Display results on interactive dashboards and maps.
Reporting:
Generate detailed reports with maps, charts, and summaries.
Notification:
Alert users of findings via email, SMS, or in-app messages.
User Interaction:
Provide tools for uploading data, customizing views, and exporting results.