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Communication & Infrastructure

Latency: From Orbit to Application COM-001 Latency in satellite systems is the total time between a sensor observing something on Earth and that observation becoming usable information. It is not one delay — it is a chain of them. Propagation through space, queuing at ground stations, decryption, format conversion, atmospheric correction, reprojection, tiling, indexing, and delivery. Some links are governed by physics and cannot be shortened. Others are engineering choices. Understanding where latency lives determines what questions you can answer with the data. A flood map delivered in 15 minutes can direct evacuations. The same map delivered in 48 hours is a historical record.

Data & Architecture

Analysis-Ready Data DAT-004 Analysis-Ready Data (ARD) is satellite imagery that has been processed to a standard where it can be used directly for analysis without additional preprocessing. This means the image has been geometrically corrected (pixels are in the right geographic locations), radiometrically calibrated (pixel values represent meaningful physical quantities like surface reflectance rather than arbitrary digital numbers), atmospherically corrected (the atmosphere's distortion has been removed), and often cloud-masked (unusable pixels are flagged). ARD is the difference between receiving raw ingredients and receiving a prepared, measured, recipe-ready mise en place.

Earth Observation

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.

Philosophy of Observation

Epistemic Architecture PHI-003 Epistemic architecture is the idea that how you structure a system for handling information determines what that system can know. Not just what data it collects — what understanding it can produce. A library organized by color looks different from one organized by subject. Both contain the same books, but one produces connections the other cannot. The same principle applies to satellite data, sensor networks, and every pipeline between observation and decision. The architecture is not neutral. It shapes the knowledge. Information Networks & Truth PHI-004 The structure of an information network — not just the data flowing through it — determines whether that network produces truth or delusion. A network with self-correction mechanisms, error detection, and distributed verification tends toward truth. A network optimized for speed, engagement, or institutional convenience tends toward whatever narrative serves its operators. This principle, drawn from Yuval Noah Harari's Nexus, is foundational to how M33 designs its data architecture: provenance is not a feature but a structural requirement for any system that claims to represent reality. 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.

Satellites & Platforms

Sentinel-2 SAT-002 Sentinel-2 is a pair of optical satellites operated by the European Space Agency (ESA) as part of the Copernicus Earth observation programme. Each satellite carries a Multispectral Instrument (MSI) that captures images in 13 spectral bands — from visible light through near-infrared to shortwave infrared — at resolutions of 10, 20, and 60 meters. With two satellites in orbit (Sentinel-2A and 2B), the combined revisit time is approximately 5 days at the equator and 2-3 days at mid-latitudes. The data is free and open access. Sentinel-2 is the most widely used optical satellite for land monitoring, agriculture, forestry, water resources, and disaster response worldwide.

Security & Provenance

Chain of Custody in Multi-Sensor Fusion SEC-002 When multiple sensor datasets are combined — SAR with optical, optical with terrain models, thermal with multispectral — the provenance record is no longer a chain. It is a graph. Most processing systems were designed for linear workflows and cannot adequately represent what happens when data from independent sources converges into a single product. This is the central unsolved problem in geospatial data provenance. Data Provenance SEC-001 Data provenance is the complete, verifiable record of where a piece of data came from, every transformation it underwent, and who or what performed those transformations. In satellite imagery and remote sensing, provenance is not a nice-to-have audit trail — it is the difference between evidence and hearsay. Space Cybersecurity: The Attack Surface Above Us SEC-004 Space systems are among the most critical and least defended digital infrastructure on Earth. Satellites underpin GPS navigation, financial transaction timing, weather forecasting, military communications, and Earth observation — yet most were designed with security as an afterthought, operate on decades-old firmware that cannot be patched remotely, and communicate over radio frequency links that are inherently exposed to interception, jamming, and spoofing. The attack surface spans three segments — ground, link, and space — each with distinct vulnerabilities. As the orbital population grows past 15,000 active satellites and commercial dependence deepens, the gap between threat sophistication and defensive capability is widening. Trusted Execution Environments for Geospatial Processing SEC-003 A Trusted Execution Environment (TEE) is a hardware-enforced isolated region within a processor where code and data are protected from the rest of the system — including the operating system, hypervisor, and anyone with physical access to the machine. In geospatial processing, TEEs enable cryptographic proof that a specific transformation was applied to specific data, generated by hardware that the operator cannot tamper with. This is the mechanism that turns provenance from a claim into a proof.

Sensors & Physics

SAR Fundamentals SEN-005 Synthetic Aperture Radar (SAR) is a radar imaging system carried on aircraft or satellites that creates high-resolution images of the Earth's surface by emitting microwave pulses and measuring what bounces back. Unlike optical sensors that rely on sunlight, SAR generates its own illumination — so it works at night. Unlike optical sensors that are blocked by clouds, SAR's microwave frequencies pass through clouds, rain, and smoke. SAR does not see color or reflected light. It sees surface roughness, moisture content, and geometric structure. This makes it essential for flood mapping, deforestation monitoring, ground deformation measurement, and any application where persistent, all-weather observation is required.

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