Compass - Memory Through Observation
Compass - Memory Through Observation
A living map of space and time.
A living map of space and time.
01 Overview
01 Overview
Compass is GRF’s experiment in anchoring human memory into space and time. It transforms scanning and observation into a shared process of constructing reality: mapping the physical world, layering it temporally, and embedding it in a decentralized collective memory. Unlike traditional 3D mapping, Compass positions every scan as a living observation—time-stamped, situated, queryable, and open to recombination.
Compass is GRF’s experiment in anchoring human memory into space and time. It transforms scanning and observation into a shared process of constructing reality: mapping the physical world, layering it temporally, and embedding it in a decentralized collective memory. Unlike traditional 3D mapping, Compass positions every scan as a living observation—time-stamped, situated, queryable, and open to recombination.
Attention → Evidence → Ascent.
Tomorrow is built from the quality of observations we record today.
Attention → Evidence → Ascent.
Tomorrow is built from the quality of observations we record today.
The goal with compass is to create situated, time-aware data anchors that let people and agents reason about what happened where and when — turning observation into actionable context.
The goal with compass is to create situated, time-aware data anchors that let people and agents reason about what happened where and when — turning observation into actionable context.
02 Core Idea
02 Core Idea
A living map: scans that become layered, time-anchored memories.
A living map: scans that become layered, time-anchored memories.
Compass is designed as a cognitive-observational system:
Users scan environments (live or via upload), the system reconstructs local volumetric “splats” and anchors them to time and location, and those splats are fused into an evolving spatial-temporal model. This model is queryable (human questions and agent flows), supports temporal traversal (4D scrubbing / time-lapse), and feeds downstream analytics and triggers (anomaly detection, construction progress checks, civic monitoring). The platform places the user’s attention (observations) at the center of knowledge creation: the more perspectives captured, the richer and more reliable the reconstructed memory.
Compass is designed as a cognitive-observational system:
Users scan environments (live or via upload), the system reconstructs local volumetric “splats” and anchors them to time and location, and those splats are fused into an evolving spatial-temporal model. This model is queryable (human questions and agent flows), supports temporal traversal (4D scrubbing / time-lapse), and feeds downstream analytics and triggers (anomaly detection, construction progress checks, civic monitoring). The platform places the user’s attention (observations) at the center of knowledge creation: the more perspectives captured, the richer and more reliable the reconstructed memory.
Observation = Claim. Every scan encodes a testable claim. A capture is more than data; it is an evidentiary statement anchored to time and space. Compass operationalizes this by (a) generating a lightweight spatial primitive (Gaussian splat) for efficient streaming and representation, (b) augmenting each splat with provenance and metadata (device, operator, timestamp, geolocation, quality score), and (c) anchoring a cryptographic digest of the provenance bundle to an immutable ledger or proof mechanism.
Memory as mesh.
Claims link into one another (spatial overlap, temporal adjacency, user citations), forming a collective memory graph that supports query, reasoning, and verification via orthogonal anchors (cryptographic + physics + social).
Trust through symmetry. Multiple, multi-modal anchors (photogrammetry + physics + social redundancy) produce robust trust.
Observation = Claim. Every scan encodes a testable claim. A capture is more than data; it is an evidentiary statement anchored to time and space. Compass operationalizes this by (a) generating a lightweight spatial primitive (Gaussian splat) for efficient streaming and representation, (b) augmenting each splat with provenance and metadata (device, operator, timestamp, geolocation, quality score), and (c) anchoring a cryptographic digest of the provenance bundle to an immutable ledger or proof mechanism.
Memory as mesh.
Claims link into one another (spatial overlap, temporal adjacency, user citations), forming a collective memory graph that supports query, reasoning, and verification via orthogonal anchors (cryptographic + physics + social).
Trust through symmetry. Multiple, multi-modal anchors (photogrammetry + physics + social redundancy) produce robust trust.
This echoes both cognitive science — where hippocampal place cells anchor memory in space — and distributed systems theory — where redundancy ensures resilience (Hamming, 1950).
This echoes both cognitive science — where hippocampal place cells anchor memory in space — and distributed systems theory — where redundancy ensures resilience (Hamming, 1950).
Compass is, in essence, a compass of truth: orienting communities through evidence-based claims.
Compass is, in essence, a compass of truth: orienting communities through evidence-based claims.
03 Research background & intellectual lineage
03 Research background & intellectual lineage
Philosophical / Manifesto Roots — GRF’s manifesto: observation as moral/epistemic act; belief needs accountability. Compass operationalizes this: claims must be recorded and measured.
Philosophical / Manifesto Roots — GRF’s manifesto: observation as moral/epistemic act; belief needs accountability. Compass operationalizes this: claims must be recorded and measured.
Cognitive & Systems Roots — photogrammetry analogies, Hamming-style error correction, and analog feedback loops inform Compass’s error-tolerant design: redundancy of viewpoints compensates for single-source drift; parity checks detect and correct “where-drift.”
Cognitive & Systems Roots — photogrammetry analogies, Hamming-style error correction, and analog feedback loops inform Compass’s error-tolerant design: redundancy of viewpoints compensates for single-source drift; parity checks detect and correct “where-drift.”
Narrative & Pedagogy — multiplayer seed-document mechanics teach systems thinking, provide error-correcting social protocols, and create experiments that both educate and produce high-quality data.
Narrative & Pedagogy — multiplayer seed-document mechanics teach systems thinking, provide error-correcting social protocols, and create experiments that both educate and produce high-quality data.
Industry Grounding — vendor-neutral DT models (IFC/OpenBIM, ISO/IIC/NIST patterns) show how Compass outputs can be integrated into construction and asset workflows as a temporal, low-cost observational layer.
Industry Grounding — vendor-neutral DT models (IFC/OpenBIM, ISO/IIC/NIST patterns) show how Compass outputs can be integrated into construction and asset workflows as a temporal, low-cost observational layer.
04 Process & methodology
04 Process & methodology
Surface
Surface
Trace & Observe — capture observations anchored in place and time.
Trace & Observe — capture observations anchored in place and time.
Iterate & Refine — reconstruct, rescan, and apply analytics to reduce uncertainty.
Iterate & Refine — reconstruct, rescan, and apply analytics to reduce uncertainty.
Query & Act — surface insights, trigger agents, and enable decisions.
Query & Act — surface insights, trigger agents, and enable decisions.
Operational
Operational
Capture: live phone scan, video upload, or splat upload. Record canonical metadata fields (device, timestamp, lat/lon, capture mode, quality heuristics). This map of fields is taken from the Compass capture schema.
Capture: live phone scan, video upload, or splat upload. Record canonical metadata fields (device, timestamp, lat/lon, capture mode, quality heuristics). This map of fields is taken from the Compass capture schema.
Contextualize: normalize and enrich metadata; assign to collection/project; run initial heuristics (coverage, overlap).
Contextualize: normalize and enrich metadata; assign to collection/project; run initial heuristics (coverage, overlap).
Reconstruct: generate Gaussian splats as view-dependent primitives; compute thumbnails and fast streaming proxies. The splat design choices trade absolute fidelity for streaming speed and mobile practicality.
Reconstruct: generate Gaussian splats as view-dependent primitives; compute thumbnails and fast streaming proxies. The splat design choices trade absolute fidelity for streaming speed and mobile practicality.
Layer & Rescan: store successive temporal versions; compute volumetric deltas; fuse multi-view evidence to reduce uncertainty.
Layer & Rescan: store successive temporal versions; compute volumetric deltas; fuse multi-view evidence to reduce uncertainty.
Analyze(in pipeline): run ML/vision for semantic tagging, anomaly detection, and progress estimation (construction). Agents produce higher-order claims for human review.
Analyze(in pipeline): run ML/vision for semantic tagging, anomaly detection, and progress estimation (construction). Agents produce higher-order claims for human review.
Act & Close: queries, request/fulfilment (credit exchange), update provenance ledgers, and create audit trails for claims. This closes the evidentiary loop and produces traceable outcomes for pilots.
Act & Close: queries, request/fulfilment (credit exchange), update provenance ledgers, and create audit trails for claims. This closes the evidentiary loop and produces traceable outcomes for pilots.
05 UI in practice
05 UI in practice
How to Scan
How to Scan
Capture guidance: angles, overlap — scans ready for layering.
Capture guidance: angles, overlap — scans ready for layering.
Explore (Map + Heatmap)
Explore (Map + Heatmap)
A heatmap of available spaces and a main action panel.
Search
Search
Search spaces by place and time.
Search spaces by place and time.
Explore (Map + Heatmap)
Explore (Map + Heatmap)
Tap a heatmap to view space overview.
Tap a heatmap to view space overview.
Scan & Upload Flow
Scan & Upload Flow
Options to live scan, upload a video, or add a splat. Each requires metadata: source, timestamp, location, scan type, title, description, and visibility.
Options to live scan, upload a video, or add a splat. Each requires metadata: source, timestamp, location, scan type, title, description, and visibility.
Scenes Tab
Scenes Tab
All your captures, collections, and shared spaces together.
All your captures, collections, and shared spaces together.
Request Tab
Request Tab
Request a scan of an area for credits; fulfillers upload data, and once approved, credits transfer.
Request a scan of an area for credits; fulfillers upload data, and once approved, credits transfer.
Splat Viewer
Splat Viewer
Explore splats in detail with fly or orbit mode. See scan data, metadata, and location context together.
Explore splats in detail with fly or orbit mode. See scan data, metadata, and location context together.
06 System architecture
06 System architecture
Layers & components
Layers & components
Capture layer (edge)
Devices: smartphone, drone, cam.
Capture types: point-and-shoot depth sequence, video upload, pre-computed splat.
Immediate metadata: UTC timestamp, device model, IMU/GPS dump, capture mode.
Capture layer (edge)
Devices: smartphone, drone, cam.
Capture types: point-and-shoot depth sequence, video upload, pre-computed splat.
Immediate metadata: UTC timestamp, device model, IMU/GPS dump, capture mode.
Splat representation layer
Unit: Gaussian splat primitive (position, radius, normal, color, confidence). Splats are cheap to store & stream.
Temporal stacks: splats augmented with T (time) to build 4D scenes.
Splat representation layer
Unit: Gaussian splat primitive (position, radius, normal, color, confidence). Splats are cheap to store & stream.
Temporal stacks: splats augmented with T (time) to build 4D scenes.
Preprocessing & compression
On-device overlap scoring, angle coverage heuristics, delta delta compression (store changed splats only). This reduces average upload payload and enables efficient diffs over time.
Preprocessing & compression
On-device overlap scoring, angle coverage heuristics, delta delta compression (store changed splats only). This reduces average upload payload and enables efficient diffs over time.
Anchoring layer
Primary: cryptographic anchor of hash bundle to Layer-1 (transaction id stored in provenance).
Secondary: physics anchors — celestial alignment (sun angle, star positions) and environmental signatures (wifi SSID patterns) as orthogonal proofs.
Anchoring layer
Primary: cryptographic anchor of hash bundle to Layer-1 (transaction id stored in provenance).
Secondary: physics anchors — celestial alignment (sun angle, star positions) and environmental signatures (wifi SSID patterns) as orthogonal proofs.
Memory graph & index
Node model: splat_id, time, bbox, derived metrics, provenance.
Edges: spatial overlap, collection membership, user citations, chronology.
Memory graph & index
Node model: splat_id, time, bbox, derived metrics, provenance.
Edges: spatial overlap, collection membership, user citations, chronology.
AI agent layer
Tasks: change detection, anomaly detection, object extraction, summarization, query routing. Agent outputs form higher-order claims (e.g. “structure progressed 12% this week”).
AI agent layer
Tasks: change detection, anomaly detection, object extraction, summarization, query routing. Agent outputs form higher-order claims (e.g. “structure progressed 12% this week”).
07 Use cases & proposed pilots
07 Use cases & proposed pilots
Design pilots as measurable experiments with clear hypotheses, metrics, and timelines.
Design pilots as measurable experiments with clear hypotheses, metrics, and timelines.
Pilot A — Construction progress verification
Hypothesis: Weekly splats at critical work packages allow accurate progress verification vs. schedule.
Experiment: Weekly scans of key zones; compare scheduled %complete vs. scan-derived progress.
Metrics: rescan variance, %match to visual QA, time-to-render, file size.
Pilot A — Construction progress verification
Hypothesis: Weekly splats at critical work packages allow accurate progress verification vs. schedule.
Experiment: Weekly scans of key zones; compare scheduled %complete vs. scan-derived progress.
Metrics: rescan variance, %match to visual QA, time-to-render, file size.
Pilot B — HOA / Landscaping monitoring
Hypothesis: Reward-based scan requests increase coverage for perimeter and compliance checks.
Experiment: Community posts requests; track fulfilment rate and detection of violations.
Metrics: request fulfillment rate, coverage increase, false-positive rate.
Pilot B — HOA / Landscaping monitoring
Hypothesis: Reward-based scan requests increase coverage for perimeter and compliance checks.
Experiment: Community posts requests; track fulfilment rate and detection of violations.
Metrics: request fulfillment rate, coverage increase, false-positive rate.
Pilot C — Learning & engagement
Hypothesis: Gamified onboarding improves scanning quality and collaboration speed.
Experiment: Use Seed Document + Compass UI with students; measure capture quality and completion time.
Metrics: capture quality score distribution, collaborative correctness (error correction rates).
Pilot C — Learning & engagement
Hypothesis: Gamified onboarding improves scanning quality and collaboration speed.
Experiment: Use Seed Document + Compass UI with students; measure capture quality and completion time.
Metrics: capture quality score distribution, collaborative correctness (error correction rates).
Pilot D — Civic Infrastructure & Public Works
Hypothesis: Distributed community scanning of public works will detect early-stage degradation and provide auditable evidence for maintenance cycles.
Experiment: Partner with a local government agency. Select 2–3 infrastructure assets (e.g. footbridge, playground, park area).
Residents scan bi-weekly. System flags anomalies (e.g. cracks, erosion). Compare alerts to official inspection reports.
Metrics: Detection accuracy vs. manual inspection.
Coverage rate (% of assets scanned regularly). Time-to-flag from capture. Reduction in missed issues.
Pilot D — Civic Infrastructure & Public Works
Hypothesis: Distributed community scanning of public works will detect early-stage degradation and provide auditable evidence for maintenance cycles.
Experiment: Partner with a local government agency. Select 2–3 infrastructure assets (e.g. footbridge, playground, park area).
Residents scan bi-weekly. System flags anomalies (e.g. cracks, erosion). Compare alerts to official inspection reports.
Metrics: Detection accuracy vs. manual inspection.
Coverage rate (% of assets scanned regularly). Time-to-flag from capture. Reduction in missed issues.
08 Closing Insight
08 Closing Insight
By grounding observation in evidence, Compass transforms scanning into accountable knowledge. What begins as a single capture becomes a testable claim, a layered record, and eventually a decision-making tool. In this way, Compass proves the Gradient Rising Foundation’s belief: vision gains power when it is measured, refined, and shared.
By grounding observation in evidence, Compass transforms scanning into accountable knowledge. What begins as a single capture becomes a testable claim, a layered record, and eventually a decision-making tool. In this way, Compass proves the Gradient Rising Foundation’s belief: vision gains power when it is measured, refined, and shared.
× Hamming, R. “Error Detecting and Error Correcting Codes” (1950).
× O’Keefe, J., Nadel, L. The Hippocampus as a Cognitive Map (1978).
× FOAM Protocol (decentralized geospatial consensus).
× Arweave (permanent collective storage).
× Tao et al. (2018). “Five-Dimensional Digital Twin Model.”
× Bunce, G. (2020, January 30). Cognitive maps - The science behind our brain's internal mapping and navigation system. Utah Geospatial Resource Center. https://gis.utah.gov/blog/2020-01-29-cognitive-maps/
× Danchin, A., & Fenton, A. A. (2022). From analog to digital computing: Is Homo sapiens' brain on its way to become a Turing machine? Frontiers in Ecology and Evolution, 10. https://doi.org/10.3389/fevo.2022.796413
× Geodetic Services, Inc. (2017). Basics of photogrammetry. Geodetic Services, Inc. https://www.geodetic.com/basics-of-photogrammetry/#basics-photogrammetry-_Toc496190789
× Lengen, C., & Kistemann, T. (2012). Sense of place and place identity: Review of neuroscientific evidence. Health & Place, 18(5), 1162–1171. https://doi.org/10.1016/j.healthplace.2012.01.012
× Ronchi, R., Park, H. D., & Blanke, O. (2018). Bodily self-consciousness and its disorders. In Handbook of clinical neurology (Vol. 151, pp. 313–330). Elsevier B.V. https://doi.org/10.1016/B978-0-444-63622-5.00015-2
× The Decision Lab. (2021). Distributed Cognition. The Decision Lab. Retrieved October 2, 2025, from https://thedecisionlab.com/reference-guide/neuroscience/distributed-cognition
× Wright, G. (2022, July 11). Hamming code. TechTarget: WhatIs. https://www.techtarget.com/whatis/definition/Hamming-code
Compass - Memory Through Observation
A living map of space and time.
01 Overview
Compass is GRF’s experiment in anchoring human memory into space and time. It transforms scanning and observation into a shared process of constructing reality: mapping the physical world, layering it temporally, and embedding it in a decentralized collective memory. Unlike traditional 3D mapping, Compass positions every scan as a living observation—time-stamped, situated, queryable, and open to recombination.
Attention → Evidence → Ascent.
Tomorrow is built from the quality of observations we record today.
The goal with compass is to create situated, time-aware data anchors that let people and agents reason about what happened where and when — turning observation into actionable context.
02 Core Idea
A living map: scans that become layered, time-anchored memories.
Compass is designed as a cognitive-observational system:
Users scan environments (live or via upload), the system reconstructs local volumetric “splats” and anchors them to time and location, and those splats are fused into an evolving spatial-temporal model. This model is queryable (human questions and agent flows), supports temporal traversal (4D scrubbing / time-lapse), and feeds downstream analytics and triggers (anomaly detection, construction progress checks, civic monitoring). The platform places the user’s attention (observations) at the center of knowledge creation: the more perspectives captured, the richer and more reliable the reconstructed memory.
Observation = Claim. Every scan encodes a testable claim. A capture is more than data; it is an evidentiary statement anchored to time and space. Compass operationalizes this by (a) generating a lightweight spatial primitive (Gaussian splat) for efficient streaming and representation, (b) augmenting each splat with provenance and metadata (device, operator, timestamp, geolocation, quality score), and (c) anchoring a cryptographic digest of the provenance bundle to an immutable ledger or proof mechanism.
Memory as mesh.
Claims link into one another (spatial overlap, temporal adjacency, user citations), forming a collective memory graph that supports query, reasoning, and verification via orthogonal anchors (cryptographic + physics + social).
Trust through symmetry. Multiple, multi-modal anchors (photogrammetry + physics + social redundancy) produce robust trust.
This echoes both cognitive science — where hippocampal place cells anchor memory in space — and distributed systems theory — where redundancy ensures resilience (Hamming, 1950).
Compass is, in essence, a compass of truth: orienting communities through evidence-based claims.
03 Research background & intellectual lineage
Philosophical / Manifesto Roots — GRF’s manifesto: observation as moral/epistemic act; belief needs accountability. Compass operationalizes this: claims must be recorded and measured.
Cognitive & Systems Roots — photogrammetry analogies, Hamming-style error correction, and analog feedback loops inform Compass’s error-tolerant design: redundancy of viewpoints compensates for single-source drift; parity checks detect and correct “where-drift.”
Narrative & Pedagogy — multiplayer seed-document mechanics teach systems thinking, provide error-correcting social protocols, and create experiments that both educate and produce high-quality data.
Industry Grounding — vendor-neutral DT models (IFC/OpenBIM, ISO/IIC/NIST patterns) show how Compass outputs can be integrated into construction and asset workflows as a temporal, low-cost observational layer.
04 Process & methodology
Surface
Trace & Observe — capture observations anchored in place and time.
Iterate & Refine — reconstruct, rescan, and apply analytics to reduce uncertainty.
Query & Act — surface insights, trigger agents, and enable decisions.
Operational
Capture: live phone scan, video upload, or splat upload. Record canonical metadata fields (device, timestamp, lat/lon, capture mode, quality heuristics). This map of fields is taken from the Compass capture schema.
Contextualize: normalize and enrich metadata; assign to collection/project; run initial heuristics (coverage, overlap).
Reconstruct: generate Gaussian splats as view-dependent primitives; compute thumbnails and fast streaming proxies. The splat design choices trade absolute fidelity for streaming speed and mobile practicality.
Layer & Rescan: store successive temporal versions; compute volumetric deltas; fuse multi-view evidence to reduce uncertainty.
Analyze(in pipeline): run ML/vision for semantic tagging, anomaly detection, and progress estimation (construction). Agents produce higher-order claims for human review.
Act & Close: queries, request/fulfilment (credit exchange), update provenance ledgers, and create audit trails for claims. This closes the evidentiary loop and produces traceable outcomes for pilots.
05 UI in practice
How to Scan
Capture guidance: angles, overlap — scans ready for layering.
Explore (Map + Heatmap)
A heatmap of available spaces and a main action panel.
Search
Search spaces by place and time.
Explore (Map + Heatmap)
Tap a heatmap to view space overview.
Scan & Upload Flow
Options to live scan, upload a video, or add a splat. Each requires metadata: source, timestamp, location, scan type, title, description, and visibility.
Scenes Tab
All your captures, collections, and shared spaces together.
Request Tab
Request a scan of an area for credits; fulfillers upload data, and once approved, credits transfer.
Splat Viewer
Explore splats in detail with fly or orbit mode. See scan data, metadata, and location context together.
06 System architecture
Layers & components
Capture layer (edge)
Devices: smartphone, drone, cam.
Capture types: point-and-shoot depth sequence, video upload, pre-computed splat.
Immediate metadata: UTC timestamp, device model, IMU/GPS dump, capture mode.
Splat representation layer
Unit: Gaussian splat primitive (position, radius, normal, color, confidence). Splats are cheap to store & stream.
Temporal stacks: splats augmented with T (time) to build 4D scenes.
Preprocessing & compression
On-device overlap scoring, angle coverage heuristics, delta delta compression (store changed splats only). This reduces average upload payload and enables efficient diffs over time.
Anchoring layer
Primary: cryptographic anchor of hash bundle to Layer-1 (transaction id stored in provenance).
Secondary: physics anchors — celestial alignment (sun angle, star positions) and environmental signatures (wifi SSID patterns) as orthogonal proofs.
Memory graph & index
Node model: splat_id, time, bbox, derived metrics, provenance.
Edges: spatial overlap, collection membership, user citations, chronology.
AI agent layer
Tasks: change detection, anomaly detection, object extraction, summarization, query routing. Agent outputs form higher-order claims (e.g. “structure progressed 12% this week”).
07 Use cases & proposed pilots
Design pilots as measurable experiments with clear hypotheses, metrics, and timelines.
Pilot A — Construction progress verification
Hypothesis: Weekly splats at critical work packages allow accurate progress verification vs. schedule.
Experiment: Weekly scans of key zones; compare scheduled %complete vs. scan-derived progress.
Metrics: rescan variance, %match to visual QA, time-to-render, file size.
Pilot B — HOA / Landscaping monitoring
Hypothesis: Reward-based scan requests increase coverage for perimeter and compliance checks.
Experiment: Community posts requests; track fulfilment rate and detection of violations.
Metrics: request fulfillment rate, coverage increase, false-positive rate.
Pilot C — Learning & engagement
Hypothesis: Gamified onboarding improves scanning quality and collaboration speed.
Experiment: Use Seed Document + Compass UI with students; measure capture quality and completion time.
Metrics: capture quality score distribution, collaborative correctness (error correction rates).
Pilot D — Civic Infrastructure & Public Works
Hypothesis: Distributed community scanning of public works will detect early-stage degradation and provide auditable evidence for maintenance cycles.
Experiment: Partner with a local government agency. Select 2–3 infrastructure assets (e.g. footbridge, playground, park area).
Residents scan bi-weekly. System flags anomalies (e.g. cracks, erosion). Compare alerts to official inspection reports.
Metrics: Detection accuracy vs. manual inspection.
Coverage rate (% of assets scanned regularly). Time-to-flag from capture. Reduction in missed issues.
08 Closing Insight
By grounding observation in evidence, Compass transforms scanning into accountable knowledge. What begins as a single capture becomes a testable claim, a layered record, and eventually a decision-making tool. In this way, Compass proves the Gradient Rising Foundation’s belief: vision gains power when it is measured, refined, and shared.
× Hamming, R. “Error Detecting and Error Correcting Codes” (1950).
× O’Keefe, J., Nadel, L. The Hippocampus as a Cognitive Map (1978).
× FOAM Protocol (decentralized geospatial consensus).
× Arweave (permanent collective storage).
× Tao et al. (2018). “Five-Dimensional Digital Twin Model.”
× Bunce, G. (2020, January 30). Cognitive maps - The science behind our brain's internal mapping and navigation system. Utah Geospatial Resource Center. https://gis.utah.gov/blog/2020-01-29-cognitive-maps/
× Danchin, A., & Fenton, A. A. (2022). From analog to digital computing: Is Homo sapiens' brain on its way to become a Turing machine? Frontiers in Ecology and Evolution, 10. https://doi.org/10.3389/fevo.2022.796413
× Geodetic Services, Inc. (2017). Basics of photogrammetry. Geodetic Services, Inc. https://www.geodetic.com/basics-of-photogrammetry/#basics-photogrammetry-_Toc496190789
× Lengen, C., & Kistemann, T. (2012). Sense of place and place identity: Review of neuroscientific evidence. Health & Place, 18(5), 1162–1171. https://doi.org/10.1016/j.healthplace.2012.01.012
× Ronchi, R., Park, H. D., & Blanke, O. (2018). Bodily self-consciousness and its disorders. In Handbook of clinical neurology (Vol. 151, pp. 313–330). Elsevier B.V. https://doi.org/10.1016/B978-0-444-63622-5.00015-2
× The Decision Lab. (2021). Distributed Cognition. The Decision Lab. Retrieved October 2, 2025, from https://thedecisionlab.com/reference-guide/neuroscience/distributed-cognition
× Wright, G. (2022, July 11). Hamming code. TechTarget: WhatIs. https://www.techtarget.com/whatis/definition/Hamming-code