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- 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.
- GDAL for geospatial raster data.
- PyPDF2 and docx for document ingestion.
- Database: PostgreSQL with PostGIS for spatial data storage.
- 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.
- GeoPandas for spatial preprocessing.
- Fiona for reading/writing geospatial files.
- Custom Python scripts for unit conversions (e.g., meters to feet).
- 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.
- PostgreSQL with PostGIS for geospatial data.
- Elasticsearch for text-based document indexing.
- File storage: AWS S3 for large raster datasets.
- 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.
- Scikit-learn for clustering.
- NumPy and SciPy for signal processing.
- AI models: Gradient Boosting for pattern recognition.
- 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.
- TensorFlow or PyTorch for deep learning.
- Scikit-learn for classical ML models.
- Validation: Cross-validation and grid search for hyperparameter tuning.
- 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.
- QGIS and ArcGIS for manual validation.
- GeoPandas and Shapely for automated spatial operations.
- Visualization: Folium and Kepler.gl.
- 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.
- Dash and Plotly for dashboards.
- Matplotlib for static visualizations.
- Leaflet.js for web-based maps.
- 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.
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- WeasyPrint or ReportLab for PDF generation.
- NLP: spaCy for summarization.
- 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.
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- In-app: WebSocket for real-time notifications.
- 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.
- Front-End: React or Angular.
- Back-End: Flask or FastAPI for API development.
- Security: OAuth 2.0 for authentication.
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- Purpose: Collect and integrate diverse datasets, including geophysical surveys, geospatial imagery, and text-based documents.
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- Purpose: Clean, normalize, and transform raw data into a standardized format.
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- Purpose: Store and organize processed datasets for efficient retrieval.
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- Purpose: Identify patterns and anomalies in geophysical and geospatial data.
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- Purpose: Build machine learning models to predict mineral prospectivity.
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- Purpose: Process and analyze geospatial datasets for mapping and decision-making.
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- Purpose: Create interactive maps and charts to present insights.
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- Purpose: Generate reports summarizing findings and recommendations.
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- Purpose: Alert users to new data insights or updates.
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- Purpose: Provide a seamless interface for interacting with the system.
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- 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.