How Aetherion CityIntel Connects Data
This page describes the implemented scientific models, formulas, thresholds, interpretation methods, and model assumptions used by CityIntel. These components are integrated into the system and are used to generate the environmental and urban intelligence results presented throughout the platform.
Data flow
External source request
Abdullahs adapters request only a caller-selected source and retain the raw response.
Owner processing
Henri and the scientific owners choose products, interpret fields, align data, and calculate results.
Validated database records
The prepared records are validated before the existing importer writes them to PostGIS.
Backend routes
The frontend reads areas, risk, population, and the three supported map layers.
Approved source catalog
Availability and resolution depend on the exact product selected by the data owner.
NASA Earth Observation
NASA EOSDIS
Multispectral radiance, land surface temperature, and atmospheric profile data.
1km / Daily pass
Landsat 8 & 9
USGS / NASA
Thermal Infrared Sensor and multispectral satellite observations.
30m - 100m / 16-day cycle
Sentinel-2
European Space Agency
Multispectral imagery available for vegetation analysis by the data team.
10m - 20m / 5-day cycle
NASA GPM
NASA / JAXA
Global Precipitation Measurement catalog and downloadable products.
Product selection required
Open-Meteo
Open-Meteo
Weather and air-quality API responses passed raw into the processing pipeline.
Depends on selected model
WorldPop
University of Southampton
Downloadable gridded population datasets selected by the data team.
Dataset selection required
OpenStreetMap
OpenStreetMap contributors
Raw OpenStreetMap responses for agreed geographic selections.
Vector data
Scientific and AI disclosure
Aetherion CityIntel is currently a research and demonstration prototype. The platform’s backend data pipeline and live data integrations are not fully operational, and the environmental and urban datasets currently presented within the platform are simulated or placeholder data created for demonstration purposes. The risk assessments, environmental insights, and visualizations are therefore generated from this provided dataset rather than from live or externally validated measurements. The simulation system and AI-assisted analysis operate on this dataset as intended, demonstrating how the platform can process data, model potential scenarios, and generate contextual insights. The scientific models, risk thresholds, formula weights, simulation coefficients, and assumptions implemented by the Aetherion team are prototype-level and have not been externally validated. Consequently, the results should be interpreted as demonstrations of the platform’s capabilities and should not be used for real-world planning, environmental assessment, or other consequential decisions without validated data, appropriate scientific review, and further development of the underlying models. AI-generated interpretations may also contain inaccuracies. Users should consider the underlying dataset, model assumptions, and simulation parameters when interpreting the platform’s results. .