GeoLens-Europa

GeoLens: An Experimental Spatial Evidence Engine

GeoLens is designed to synthesize real-world environmental observations, terrain data, and infrastructure inputs into traceable runoff and downstream physical states. It emphasizes transparency in its results by ensuring that every outcome can be explained in terms of its sources, transformations, and uncertainties.

Core Principles

GeoLens operates on the fundamental principle that "A result is useful only when we can explain where it came from, how it was transformed, and what is still unknown." This adherence to strict evidential standards distinguishes GeoLens from other systems that may obscure missing data rather than highlighting it.

Approach and Functionality

GeoLens efficiently integrates multiple data sources to connect rainfall, terrain, land cover, and drainage infrastructure. The workflow involves the following steps:

1. Acquire real environmental data for a specified area and timeframe.
2. Document provider details, dataset versions, timestamps, and original resolutions.
3. Calculate an inspectable quantity of runoff.
4. Aggregate runoff data over defined surfaces or catchments.
5. Connect to observed infrastructure only when there is authoritative backing.
6. Propagate this information through known network directions without making assumptions about missing links.
7. Return results alongside their provenance, assumptions, and any state of missing data.

In instances where data is unavailable, GeoLens clearly communicates this instead of defaulting to a value of zero, reinforcing the importance of accurate data reporting.

Ongoing Proof and Validation

The project currently showcases three key proofs and an active historical benchmark:

Case	Outcome	Proven	Unresolved
Trento Proof 0	The complete software chain functions from real inputs to a result.	Evidence composition, deterministic runoff, etc.	Small drainage network is not surveyed municipal infrastructure.
Amsterdam Observed	Successfully connects real stormwater infrastructure with runoff data.	Observed topology, real rainfall/terrain/land cover.	Missing surface-to-pipe attachment blocks sewer propagation.
Emilia-Romagna 2023	Terrain-based concentration hypothesis against an observed flood.	Historical benchmark and withholding evaluation data.	Requires more discharge and terrain evidence for replay.

Insights from Current Applications

• Trento Proof 0 confirmed an effective transformation chain, yielding verified runoff contributions from real environmental conditions.
• Amsterdam's urban drainage enabled the linkage of real data with observed stormwater pipe networks, showcasing the detailed environmental context.
• The Emilia-Romagna benchmark tested hypotheses against real-world events, demonstrating the need for comprehensive datasets before deriving actionable predictions.

Key Features

• Spatial Evidence Engine: Intentionally designed to prioritize real environmental data.
• Transparent Data Handling: Each piece of evidence is retained with its respective metadata, ensuring clarity in both successes and failures.
• Inspectability: The transformations and results are subjected to scrutiny, fostering trust in the findings.

Limitations and Distinctions

It is crucial to note that GeoLens is not a flood forecasting tool or a hydraulic sewer simulation. Instead, it serves as a foundation for future environmental and infrastructure applications, dedicated to precise and transparent data reporting.

Through its innovative approach, GeoLens aims to transform how environmental evidence is utilized, ensuring that every piece of data is accounted for and understandable, paving the way for more informed decision-making in stormwater management and urban planning.