Earth Observation
Earth Observation is the discipline of making claims about reality from constrained measurements.
What Belongs Here
<ul><li>Interpretation: what sensor-derived signals can support</li><li>Change detection, harmonization, validation</li><li>The limits of general-purpose AI in spatial domains</li></ul>
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Why Geospatial Intelligence Resists General-Purpose AI
EO-001
General-purpose machine learning treats location as just another feature in a table. But spatial data has properties that systematically violate the assumptions underlying most AI architectures — non-stationarity, autocorrelation, heterogeneous observation networks, and sensor-specific physics. The most effective geospatial AI systems are not the largest or most general. They are the ones that encode domain knowledge into their structure. This has implications for how intelligence layers over Earth observation data should be designed.
Observational Grammar
OG-001
Observational Grammar (OG) is the idea that sensors — satellites, radar, spectrometers, thermal cameras — can form a language of evidence about reality that operates independently of human bias, market incentives, or bureaucratic approval chains. Just as grammar gives structure to language, OG gives structure to what instruments can claim about the physical world. It is M33's foundational concept: build systems that let reality set the table, then let markets and decisions work within those constraints, rather than the other way around.
Key Concepts
- Spatial leakage
- Random splits can lie.
- Ground truth
- Often a proxy; sometimes a myth.
- Harmonization
- Alignment across sensors is a design choice.
All Entries
Coming Next
Ground truth, Change detection (physics-first vs ML-first), Bias in global EO training data
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