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This project leverages data from satellite imagery, mapping services, and news articles to provide insights into green infrastructure, pollution levels, and related news.\n\n## Data Sources\n\n- **Sentinel Copernicus Satellite Imagery**: Provides high-resolution Earth observation data for environmental monitoring.\n- **OpenStreetMap (OSM)**: An open-source mapping platform offering detailed information on geographical features, including green spaces and infrastructure.\n- **Event Registry**: Aggregates global news articles, enabling analysis of environmental events and trends.\n\n## Data Processing and Graph Construction\n\n1. **Geospatial Data Conversion**:\n   - Converted geospatial data into efficient formats such as Parquet and GeoPackage (GPKG) to optimize storage and access.\n   - Performed spatial joins to integrate various geospatial datasets, aligning features based on spatial relationships.\n\n2. **News Data Processing**:\n   - Applied Named Entity Recognition (NER) techniques to extract entities like organizations, locations, and environmental terms from news articles.\n   - Utilized Large Language Models (LLMs) to contextualize these entities, linking them to existing graph nodes and uncovering new relationships.\n\n3. **Graph Database Schema**:\n   - **Nodes**:\n     - *Object*: Represents entities such as Power Generators, EV Charging Stations, Greenery Lands, Public Transport Stations, and Waste Recycle Facilities.\n     - *ObjectType*: Categorizes objects into specific types (e.g., solar power generator, park).\n     - *Country* and *City*: Geographical entities with associated attributes.\n     - *Grid*: Represents population density and gaseous pollutant levels (CO, CH₄, NO₂).\n     - *News*: Contains news articles with attributes like content and date.\n     - *NewsEntity* and *NewsEntityType*: Extracted entities from news articles and their classifications.\n\n   - **Edges**:\n     - `City` → `Country`: `located_in`\n     - `Object` → `City`: `located_in`\n     - `Grid` → `City`: `located_in`\n     - `Object` → `ObjectType`: `is_a`\n     - `News` → `NewsEntity`: `mentions`\n     - `News` → `City`: `related_to`\n     - `News` → `Country`: `related_to`\n     - `NewsEntity` → `NewsEntityType`: `belongs_to`\n\n## Agentic App Functionality\n\nThe Agentic App dynamically retrieves and processes natural language queries based on user intent. It offers:\n\n- **Geospatial Data Analysis**: Provides insights into the distribution and accessibility of green infrastructure and pollutant levels.\n- **News Retrieval**: Aggregates and analyzes news related to environmental issues to keep communities informed.\n\n*Example Queries*:\n\n- \"Find EV charging stations in Berlin.\"\n- \"How many greenery lands are in Hamburg?\"\n- \"Show me the location with the highest CO level in Bayern.\"\n\nHere how it's works\n\n![Structure](./docs/structure.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjfraziz%2Fadbh","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjfraziz%2Fadbh","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjfraziz%2Fadbh/lists"}