Cartographers and Photogrammetrists
17-1021.00Median wage $81,390/yr14,260 employed (US)Rank #65 of 923 scored · top 7% by substitution
Research, study, and prepare maps and other spatial data in digital or graphic form for one or more purposes, such as legal, social, political, educational, and design purposes. May work with Geographic Information Systems (GIS). May design and evaluate algorithms, data structures, and user interfaces for GIS and mapping systems. May collect, analyze, and interpret geographic information provided by geodetic surveys, aerial photographs, and satellite data.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution55
Exposure52
Augmentation74
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
14 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%52
panel mean rating 3.1/5 → substitution pressure 52/100
Technical feasibility todayw 20%51
panel mean rating 3.0/5 → substitution pressure 51/100
Cost vs. human wagew 15%56
panel mean rating 3.2/5 → substitution pressure 56/100
Adoption barriersw 20%inverted — strong barriers lower the score65
panel mean rating 2.4/5 (barrier strength) → substitution pressure 65/100
Sector adoption velocityw 10%50
panel mean rating 3.0/5 → substitution pressure 50/100
Task breakdown (14 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Identify, scale, and orient geodetic points, elevations, and other planimetric or topographic features, applying standard mathematical formulas.
67CI 59–75 · exposure 67 · augmentation 88 · importance 4.1/5 · click for rater detail
Identify, scale, and orient geodetic points, elevations, and other planimetric or topographic features, applying standard mathematical formulas.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Geospatial and surveying organizations have been rapidly adopting automated photogrammetry, drone-based data collection, and AI-assisted feature extraction for years. Deployment is measured and widespread in government agencies, utilities, and mapping firms. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Geospatial and surveying sectors have adopted automated photogrammetry tools substantially, but full-scale replacement of specialist judgment remains uneven across smaller firms and agencies. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools significantly augment human cartographers by automating routine feature detection and coordinate calculation, freeing experts to focus on validation, edge-case handling, and interpretation. This is a textbook case of high-productivity assistance. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI-driven feature extraction and automated orientation calculations significantly speed up cartographers' workflows while they retain oversight for accuracy and standards compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI and computer vision systems can automatically detect, scale, and orient geodetic features from aerial/satellite imagery and apply mathematical formulas to planimetric and topographic data with high speed and accuracy. While some manual verification of edge cases may be needed, the bulk of the work meets the ≥50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | Modern photogrammetry and GIS software already automate much of point identification, scaling, and orientation via bundle adjustment and feature-matching algorithms, but complex or ambiguous terrain still requires expert review and correction.atability partial. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers prevent automation; cartography and photogrammetry are not licensed professions in most jurisdictions. Organizational inertia and quality-assurance preferences are the main friction, not hard barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Some outputs feed into legal/regulatory land surveys or government mapping standards requiring licensed professional sign-off, creating moderate liability and certification barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated photogrammetry and GIS processing cost per output is typically 1/10th to 1/5th the loaded wage of a skilled cartographer or photogrammetrist, especially for large-volume surveys and iterative refinement. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated processing pipelines drastically cut per-project labor hours compared to manual computation, though software licensing and computing costs remain nontrivial versus fully manual work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production photogrammetry software (Agisoft Metashape, Pix4D, etc.) and GIS tools routinely automate geodetic point identification, elevation extraction, and coordinate transformation at scale in real organizations. These systems are mature and deployed, though some tasks still benefit from human oversight. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Commercial photogrammetric suites (e.g., Pix4D, Agisoft Metashape, ArcGIS) reliably perform automated point orientation and geodetic referencing in production workflows today, though edge cases need manual QA. |
Collect information about specific features of the Earth, using aerial photography and other digital remote sensing techniques.
67CI 55–79 · exposure 62 · augmentation 75 · importance 4.0/5 · click for rater detail
Collect information about specific features of the Earth, using aerial photography and other digital remote sensing techniques.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | GIS, mapping, and geospatial industries are digitally native and early adopters; major tech companies, government agencies, and surveying firms have broadly deployed automated feature extraction pipelines in recent years. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Geospatial and mapping industries have adopted automated remote sensing analytics steadily, with pilots and partial production deployment common but not yet universal across all mapping functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists cartographers by automating routine feature detection and change mapping, allowing humans to focus on validation, context-aware refinement, and complex spatial analysis rather than manual digitization. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up feature identification, change detection, and classification, letting cartographers focus on verification, integration, and specialized interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can autonomously process aerial imagery and extract geospatial features (building footprints, roads, water bodies) using computer vision and machine learning, achieving significant time savings over manual digitization. However, complex feature classification and quality assurance typically still require human review, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 3/5 | AI-driven remote sensing and image classification can automate substantial portions of feature extraction from aerial/satellite imagery, but data collection (flying sensors, calibrating instruments, selecting sources) still requires human planning and validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers prevent automation of image collection and feature extraction itself; data access and intellectual property considerations exist but do not legally require human cartographers to perform the task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandate requires a human cartographer to personally collect this data, though some government mapping standards and quality certifications create moderate procedural friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven feature extraction from readily available satellite/aerial data costs orders of magnitude less than employing human cartographers to manually map features, with minimal marginal cost per additional area processed. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated image processing pipelines reduce labor for large-scale feature extraction, but sensor deployment, licensing of imagery, and compute/integration costs keep overall cost roughly comparable to skilled human labor for many projects. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature commercial products (Esri, Google Earth Engine, Planet Labs, Maxar) reliably perform automated feature extraction from satellite and aerial imagery at scale in production environments. Minor limitations exist in edge cases and novel feature types, but core capability is well-established. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed GIS and remote sensing platforms (e.g., Esri, Google Earth Engine, satellite imagery providers) use ML for feature detection and classification, but accuracy varies by terrain/feature type and often requires human QA in production workflows. |
Examine and analyze data from ground surveys, reports, aerial photographs, and satellite images to prepare topographic maps, aerial-photograph mosaics, and related charts.
67CI 59–75 · exposure 62 · augmentation 100 · importance 4.0/5 · click for rater detail
Examine and analyze data from ground surveys, reports, aerial photographs, and satellite images to prepare topographic maps, aerial-photograph mosaics, and related charts.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Geospatial and remote-sensing sectors have rapidly adopted automated photogrammetry and image analysis tools; government agencies, surveying firms, and tech companies deploy these systems routinely. Adoption is well beyond pilot stage in digitized organizations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Geospatial and surveying industries have adopted automated photogrammetry and remote sensing tools steadily, but overall sector digitization and AI integration lag behind fully digital-native industries like finance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI-powered feature detection, automated mosaicking, and real-time visualization assist human cartographers in rapid analysis and validation of imagery. Cartographers use AI outputs to focus on complex interpretation and manual refinement, substantially raising productivity. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically accelerates image analysis, feature detection, and preliminary map generation, letting cartographers focus on quality control, judgment calls, and specialized cartographic design. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can now automatically extract topographic features from aerial and satellite imagery using computer vision and deep learning, generate orthomosaics, and produce preliminary map products with significant time savings. However, some manual validation and integration of heterogeneous ground survey data typically requires human oversight, preventing a full 5-rating. |
| Task automatability | claude-sonnet-5 | 3/5 | AI/computer vision can automate photogrammetric processing, feature extraction, and mosaic stitching, but integrating heterogeneous ground survey data and final cartographic judgment still requires human oversight for accuracy and edge cases. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory barriers exist for automated map production; no legal requirement for a licensed human to perform the analysis itself. However, organizational adoption may lag due to institutional preference for human cartographers and potential liability concerns for critical applications. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandate universally requires a human cartographer for map production, though government/military mapping standards and quality certification processes create moderate institutional friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based and open-source photogrammetry tools (GDAL, OpenDroneMap, commercial SaaS) cost a fraction of manual cartography labor per output map tile. Processing costs are typically 10–20% of the loaded labor cost for equivalent output, though integration and QA add overhead. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated photogrammetry pipelines process large imagery datasets far faster than manual methods, substantially cutting per-unit-area costs versus skilled cartographer labor, though software licensing and computation add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed geospatial software (e.g., Esri, Pix4D, Agisoft) routinely performs automated feature extraction, image stitching, and map generation in production. Products are reliable for standard orthophoto and DEM generation, though edge cases (poor weather, occluded areas) still require expert intervention. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | GIS software (Pix4D, ESRI, satellite analytics platforms) reliably automates orthomosaic generation and basic feature classification, but complex topographic mapping with multi-source data fusion still needs expert review, limiting full production reliability. |
Delineate aerial photographic detail, such as control points, hydrography, topography, and cultural features, using precision stereoplotting apparatus or drafting instruments.
65CI 55–75 · exposure 62 · augmentation 75 · importance 4.5/5 · click for rater detail
Delineate aerial photographic detail, such as control points, hydrography, topography, and cultural features, using precision stereoplotting apparatus or drafting instruments.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Surveying, mapping, and GIS sectors have rapidly adopted automated feature extraction and orthomosaic generation over the past decade, with AI-powered tools now standard in many government and private mapping organizations. Adoption is deep in digitized sectors, though some legacy workflows persist. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | GIS and photogrammetry firms have moderately adopted automated feature extraction and AI-assisted image analysis, but many workflows still rely heavily on manual stereo-compilation, indicating uneven, mid-level adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at rapidly generating initial feature delineations and flagging candidate areas, which cartographers then validate and refine, dramatically accelerating the workflow. This human-in-the-loop pattern significantly boosts cartographer productivity when quality standards demand verification. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted feature detection, edge extraction, and semantic classification significantly speed up the identification and delineation process while photogrammetrists verify and refine results, providing strong productivity gains with human oversight retained. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Modern AI/ML can automatically detect and delineate control points, hydrography, topography, and cultural features from aerial imagery using computer vision and object detection, achieving substantial time savings. However, precision verification and quality control for high-stakes cartographic work often still requires human oversight, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 3/5 | Modern photogrammetry software (e.g., automated feature extraction, semantic segmentation of aerial/satellite imagery) can automate much of hydrography and topography delineation, but cultural feature identification and control point precision often still require human verification and correction, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some specialized surveying may require licensed surveyors to sign off on final deliverables, the delineation task itself has no legal requirement that a human personally perform it. Organizational adoption is primarily driven by quality confidence and customer acceptance, not hard regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandate requires a human to perform this specific delineation, though final map products used for legal/government purposes often require professional certification and quality review, creating a moderate organizational check. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated aerial feature extraction costs (software licenses + computation + minimal human oversight) are substantially cheaper than manual stereoplotting by trained cartographers. The cost per unit output is likely 3–5× lower, though not quite a full order of magnitude when oversight is included. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated stereoplotting and feature extraction reduce labor hours substantially, but software licensing, computing infrastructure, and required human review keep costs only moderately below a skilled cartographer's fully loaded wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (e.g., Esri, Pix4D, Agisoft automated orthomosaics and feature extraction) routinely perform feature delineation in production environments, though occasional errors in complex terrain or edge cases require review. Production use is widespread in surveying and mapping firms, though not 100% error-free. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Commercial photogrammetry suites (Pix4D, ESRI, Trimble) and AI-based feature extraction tools are deployed in production, but accuracy for complex cultural features and edge cases still requires manual QA, so reliability is narrow rather than universal. |
Prepare and alter trace maps, charts, tables, detailed drawings, and three-dimensional optical models of terrain using stereoscopic plotting and computer graphics equipment.
65CI 55–75 · exposure 62 · augmentation 75 · importance 4.5/5 · click for rater detail
Prepare and alter trace maps, charts, tables, detailed drawings, and three-dimensional optical models of terrain using stereoscopic plotting and computer graphics equipment.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Government agencies, surveying firms, and tech companies are rapidly adopting photogrammetry automation and AI-assisted cartography in production. Satellite/drone imagery processing pipelines already deploy these tools at scale, reflecting fast and deepening adoption in geospatial sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | GIS and photogrammetry sectors have adopted automated tools steadily over the past decade, but full pipeline automation with AI agents remains at pilot/production-mixed stage rather than deep, fast adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted feature extraction, automated contour generation, and intelligent map generalization significantly augment cartographer productivity. Humans remain in the loop for quality control, artistic refinement, and contextual decision-making, making this a strong augmentation scenario. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Stereoscopic plotting and computer graphics tools significantly speed up model creation and editing, letting cartographers focus on verification and refinement rather than manual drafting. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Stereoscopic plotting and 3D terrain model generation from aerial/satellite imagery is now largely automatable with photogrammetry software (e.g., Pix4D, Agisoft Metashape) and AI-assisted feature extraction. Map preparation and alteration can be significantly accelerated with vector graphics automation, though some manual refinement and cartographic judgment typically remain, making full end-to-end automation slightly below the threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI and automated photogrammetry software can generate 3D terrain models and draft map elements from imagery, but final editing, feature verification, and cartographic judgment still require human oversight, so only part of this multi-step task meets the 50% time-saving bar end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or licensing barriers exist for deploying automated photogrammetry—no licensed human signature is required. Adoption is primarily gated by organizational inertia and user familiarity with new tools rather than regulatory or liability constraints. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensure blocks AI-assisted production, though government/military mapping standards and accuracy requirements create moderate quality-control friction before deployment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based or subscription photogrammetry software and AI-assisted GIS tools cost significantly less per map/model than hiring skilled cartographers for the same output, especially at scale. All-in inference and platform costs are orders of magnitude lower than loaded human wages for detailed terrain mapping. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software licenses and compute costs are moderate, and while automation reduces some labor, skilled cartographer review and correction remain necessary, keeping costs roughly comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature, production-deployed photogrammetry and GIS software automate large portions of stereo plotting and 3D model generation reliably. AI-assisted tools for feature detection and map generalization are increasingly integrated into commercial workflows (e.g., Esri, Autodesk, open-source QGIS plugins), though quality assurance and artistic cartographic decisions still require oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed photogrammetry and GIS software (e.g., Pix4D, ESRI tools) reliably automate stereo model generation and basic map production, but complex trace map alteration and detailed cartographic drawing still require significant manual refinement. |
Build and update digital databases.
65CI 55–75 · exposure 62 · augmentation 75 · importance 3.8/5 · click for rater detail
Build and update digital databases.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Technology and geographic/surveying firms have rapidly adopted automated data pipelines, cloud databases, and AI-assisted validation tools. Pilot and production adoption of these systems is well-established in information and professional services sectors that employ cartographers. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | GIS and geospatial industries show moderate AI adoption—automated data pipelines and cloud-based GIS tools are increasingly used, but many organizations still rely on manual QA and legacy systems, placing this in the middle range. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists cartographers and photogrammetrists by automating data cleaning, anomaly detection, and bulk updates while the human reviews exceptions and maintains database integrity. This substantially raises human productivity in database management while preserving human judgment over data quality standards. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly assists in automating repetitive data entry, format conversion, anomaly detection, and update scheduling, letting cartographers focus on complex spatial analysis and quality control. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Building and updating digital databases of geographic/photogrammetric data can be substantially automated through data ingestion pipelines, ETL tools, and AI-assisted data validation and deduplication. While some manual quality review may remain necessary, current systems can handle >50% of the workflow autonomously, meeting the time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | Portions of database construction/updating (data ingestion, format conversion, validation scripting) can be automated with AI/ETL pipelines, but integrating heterogeneous geospatial data and QA still require human judgment and domain expertise.rent tools do not yet fully replace this end-to-end.rentThus roughly half the task is automatable with significant setup. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory or licensing barriers to automating database construction and updates itself; no law requires a human cartographer to personally build a database. Organizational friction around adopting new pipelines exists but is not a hard barrier to substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human cartographer specifically perform database updates, though organizational data-governance and accuracy standards create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based database management and automated data pipelines cost significantly less per update cycle than paying a cartographer's loaded salary to manually curate and validate data, particularly for routine updates. The cost advantage widens with scale and frequency of updates. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated ETL and AI tools reduce labor costs for routine database updates, but licensing, integration, and specialized geospatial data handling keep costs roughly comparable to skilled human labor for complex datasets. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products exist for automated database construction and ETL pipelines (e.g., AWS data services, specialized GIS tools) that reliably handle large-scale data ingestion and updates in production environments. Minor errors in edge cases or data quality issues sometimes require human review, but the core task is demonstrably deployed at scale. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | GIS platforms (ArcGIS, QGIS) and AI-assisted data pipelines exist and are used in production for database updates, but they require substantial configuration and human oversight, and error rates in automated geospatial data integration remain non-trivial. |
Compile data required for map preparation, including aerial photographs, survey notes, records, reports, and original maps.
64CI 55–72 · exposure 62 · augmentation 75 · importance 4.5/5 · click for rater detail
Compile data required for map preparation, including aerial photographs, survey notes, records, reports, and original maps.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Geographic information systems (GIS) and government mapping agencies have adopted some automation for data ingestion and organization, but adoption remains uneven—lagging in smaller jurisdictions and advanced in large federal/commercial operations. Pilots are common; production deployment at scale is mixed. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Geospatial and surveying sectors have adopted automation for data processing steadily but unevenly, with many agencies still using semi-manual workflows for legacy dataset integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI strongly augments cartographers by automatically organizing and cross-referencing large datasets, flagging missing or inconsistent records, and presenting curated source materials. Humans retain oversight and final compilation decisions, but productivity on data assembly and quality checks rises substantially. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven image recognition, OCR, and data fusion tools substantially speed up locating, extracting, and organizing survey and imagery data, greatly aiding human compilers even though final integration and quality judgment remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Data compilation from structured sources (aerial photographs, survey records, reports, maps) can be substantially automated through document scanning, OCR, database queries, and file indexing. Modern AI systems can extract, organize, and consolidate these materials with high reliability, achieving well over 50% time savings, though some verification and quality control may remain manual. |
| Task automatability | claude-sonnet-5 | 3/5 | AI/GIS tools can automatically ingest, tag, and organize much aerial imagery, survey data, and reports, but reconciling heterogeneous legacy records, verifying provenance, and resolving discrepancies still requires human judgment for a substantial portion of the workflow. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent automation of data compilation itself; the main friction is organizational (preference for human review of source materials, legacy system incompatibility, data governance). No licensing requirement exists for automating the compilation step specifically. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensure strictly requires a human to compile source data, though official mapping agencies often mandate quality control and certification of geospatial products, creating moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated document ingestion, OCR, and data organization cost significantly less than human manual compilation and filing. AI-driven workflows can process thousands of records at a fraction of professional labor costs, easily achieving a 3–5x cost advantage once systems are deployed. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated data ingestion pipelines reduce labor costs for standardized digital inputs, but cost savings shrink when older paper records, inconsistent formats, and validation checks require skilled human review. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products and workflows exist for document management, OCR, and spatial data extraction (GIS integrations, cloud storage automation, metadata parsing). Production systems routinely handle large-scale compilation of cartographic source materials, though integration with legacy survey systems may require custom setup. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Commercial GIS and remote-sensing platforms (e.g., Esri, photogrammetry suites) already automate ingestion and preprocessing of aerial imagery and geospatial data, but full compilation from mixed-format legacy records and reports is still handled with significant manual curation. |
Revise existing maps and charts, making all necessary corrections and adjustments.
64CI 55–72 · exposure 62 · augmentation 88 · importance 4.2/5 · click for rater detail
Revise existing maps and charts, making all necessary corrections and adjustments.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Government agencies, tech companies (Google, Apple, OpenStreetMap partners), and GIS firms are actively adopting automated revision pipelines for routine updates, but adoption is mixed. Many cartographic organizations still rely on manual revision workflows, and smaller firms lag significantly. Widespread production use in large organizations, but not yet industry-standard. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Geospatial/mapping industry has moderate AI adoption—automated change detection and ML-based feature extraction are increasingly used, but many agencies still rely on manual verification cycles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments cartographer productivity by automatically flagging needed corrections, suggesting edits, and handling repetitive revision tasks, while cartographers focus on complex judgment calls, validation, and final quality control. This human-in-the-loop model is already standard practice and dramatically increases throughput per cartographer. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up identification of areas needing revision and can auto-generate draft corrections, letting cartographers focus on verification and complex edits. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Map and chart revision involves systematic error detection and correction—tasks well-suited to automated comparison, flagging, and adjustment of spatial data. Current AI can identify discrepancies between source data and existing maps, suggest corrections, and auto-adjust elements like labels, projections, and feature placement. Human review of final output is typically still needed, but the majority of revision work can be automated with existing geospatial tools and computer vision systems. |
| Task automatability | claude-sonnet-5 | 3/5 | AI/GIS tools can automatically detect changes from updated satellite imagery and propagate edits into vector map layers, but final corrections still require human review for accuracy, legal boundaries, and edge cases. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard regulatory or licensing barriers apply to automated map revision itself, though some governmental and defense mapping operations have data access restrictions. Most barriers are soft—organizational inertia, quality assurance requirements, and preference for human cartographers on sensitive projects. No legal mandate typically requires a human to sign off on map corrections in most civilian contexts. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing generally required to revise commercial maps, though official government charts (e.g., nautical, aeronautical) may require certified review, creating some but not universal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated map revision through existing geospatial AI and cloud processing is substantially cheaper per map corrected than hiring cartographers to manually inspect, edit, and validate. Infrastructure costs are low relative to human labor for large-scale revision projects, though oversight and quality assurance add some overhead. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated pipelines reduce labor for bulk updates significantly, but the need for human verification, licensing of imagery, and integration costs keeps total cost roughly comparable rather than order-of-magnitude cheaper for many revision tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed GIS software, satellite imagery analysis tools, and AI-powered change detection systems already perform routine map corrections in production at cartographic agencies and tech companies. Systems reliably identify outdated features, generate update suggestions, and auto-correct common errors. Some edge cases and complex revisions still require human judgment, but core revision workflows are demonstrably operational at scale. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Production systems (e.g., automated change-detection pipelines used by mapping agencies and companies like Google/HERE) exist and are used, but they still require human QA loops and struggle with ambiguous or low-quality source data. |
Inspect final compositions to ensure completeness and accuracy.
54CI 30–79 · exposure 50 · augmentation 75 · importance 4.2/5 · click for rater detail
Inspect final compositions to ensure completeness and accuracy.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | The geospatial and mapping sectors have rapidly adopted automated QA and computer vision inspection tools in production environments, driven by volume, cost pressure, and the digitized nature of the work. Major tech firms and government mapping agencies deploy such systems at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Cartography/surveying is a niche, moderately digitized field with slow AI tool adoption compared to mainstream information sectors; automated QA tools are used but not pervasively. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems effectively assist cartographers by pre-flagging anomalies, highlight inconsistencies, and automating routine checks, allowing human experts to focus on complex judgment calls and creative refinement rather than tedious manual inspection. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted anomaly detection, automated topology checks, and image comparison tools meaningfully speed up the review process even though final judgment remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI image recognition and quality assurance systems can systematically detect missing data, geometric inconsistencies, and labeling errors across map compositions with minimal human intervention. However, final judgment on aesthetic composition and contextual accuracy may require some human oversight, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 2/5 | Final QA of maps/geospatial products requires domain judgment about correctness, cartographic conventions, and data fidelity that current AI can only partially replicate; automated checks catch some errors but not holistic accuracy review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Cartography and photogrammetry are data-driven technical fields with minimal licensing barriers for automation itself. Some organizations prefer human sign-off for critical infrastructure maps, but legal or regulatory requirements to manually inspect are rare and not systematic. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate specifically for this inspection step, but liability for inaccurate maps (used in navigation, legal boundaries, engineering) creates strong incentive for human sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated inspection via computer vision inference costs pennies per map sheet compared to the loaded hourly cost of a trained cartographer to manually review compositions. The cost advantage is substantial and well-established in GIS workflows. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automated validation scripts are cheap for narrow checks, but achieving equivalent human-level completeness/accuracy review still requires costly human oversight, keeping overall cost comparable to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed computer vision and automated QA systems (used by geospatial firms and mapping platforms) reliably detect defects, missing tiles, and coordinate misalignments in production pipelines. Minor edge cases and context-dependent assessments still require occasional human review, but core inspection is mature and operational. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some GIS/QA tools flag topology or attribute errors automatically, but no deployed product performs comprehensive final-composition inspection reliably without human review. |
Travel over photographed areas to observe, identify, record, and verify all relevant features.
52CI 18–87 · exposure 45 · augmentation 63 · importance 2.9/5 · click for rater detail
Travel over photographed areas to observe, identify, record, and verify all relevant features.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | GIS, mapping, and surveying sectors are rapidly adopting AI-powered feature extraction and orthomosaic analysis; government, tech, and infrastructure firms are in production deployment phases, reflecting high digitization and measurable adoption momentum. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Cartography/surveying is a moderately digitized field with growing use of drones and remote sensing, but physical ground-truthing travel itself sees little AI-driven displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at highlighting candidate features and anomalies in imagery, allowing cartographers to focus verification effort on uncertain areas; AI-assisted review workflows significantly improve human productivity and coverage compared to manual scanning alone. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help plan routes, prioritize areas of interest, and analyze imagery before or after field visits, but cannot replace the human presence needed for on-site verification. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Modern AI systems can automatically process aerial and satellite imagery to detect, classify, and verify geographic features (buildings, roads, water, vegetation) at scale, meeting the 50% time-saving threshold; human verification of results is still common but the core analytical work is automatable. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical field task requiring travel to real-world locations to observe and verify ground truth against imagery; no AI system can perform physical travel or on-site verification.It requires embodied presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement mandates human verification; organizations can and do deploy automated feature extraction without human sign-off, though professional standards and customer preference for validation create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically requires a human for field verification, but the inherent physical nature of travel and observation is a structural barrier to automation rather than a regulatory one. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated image analysis via cloud-based AI systems costs a fraction of field crews or manual photo analysts traveling and reviewing imagery; inference, storage, and oversight together are orders of magnitude cheaper than loaded human labor for equivalent coverage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical travel and on-site inspection component, so the human cost is unavoidable regardless of AI assistance elsewhere in the workflow. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (Esri, Google Earth Engine, specialized photogrammetry platforms) reliably perform feature detection and classification on large image datasets in production; some edge cases and validation still require human review, but the core capability is mature. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical ground-truthing travel; this remains a human field task, though AI may assist in pre-selecting areas to visit. |
Select aerial photographic and remote sensing techniques and plotting equipment needed to meet required standards of accuracy.
47CI 30–65 · exposure 45 · augmentation 63 · importance 3.7/5 · click for rater detail
Select aerial photographic and remote sensing techniques and plotting equipment needed to meet required standards of accuracy.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Geospatial and surveying sectors are moderately to rapidly adopting AI-assisted tools for workflow optimization, with remote sensing and GIS platforms increasingly embedding recommendation engines. Production adoption is evident in large enterprises and government agencies, though smaller firms remain slower to integrate. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Geospatial/surveying sectors show moderate but slow AI integration, with automation concentrated in data processing rather than upstream methodology selection. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems demonstrate strong augmentation potential by rapidly synthesizing accuracy requirements, terrain characteristics, and equipment specifications to surface optimal combinations for human experts to review and finalize. This transforms the speed and comprehensiveness of candidate solution generation while keeping the licensed professional in the decision loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted tools can help evaluate sensor specifications, simulate accuracy outcomes, and support technique comparisons, aiding but not replacing the cartographer's judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can now recommend appropriate remote sensing techniques and equipment parameters based on accuracy requirements, survey specifications, and terrain analysis, automating a significant portion of the technical selection process. End-to-end automation approaches ≥50% time savings with current tools that analyze project requirements and match them to equipment/method specifications. |
| Task automatability | claude-sonnet-5 | 2/5 | Selecting appropriate imaging technique and equipment requires domain judgment about terrain, accuracy specs, sensor tradeoffs, and project constraints that current AI cannot reliably reason through end-to-end without expert oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Professional standards (e.g., National Map Accuracy Standards, ASPRS) and regulatory requirements for surveying and mapping typically require licensed practitioners to certify selections and oversee methodology, creating oversight friction. However, no strict legal prohibition prevents AI from making preliminary recommendations subject to human approval. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed in all jurisdictions, professional certification and accuracy/liability requirements in surveying and mapping create meaningful barriers to full automation of this decision task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Once-deployed AI systems can evaluate accuracy standards and recommend equipment/methods at negligible per-task cost compared to the labor hours a skilled photogrammetrist spends on manual equipment selection and method evaluation. Inference costs are minimal relative to loaded human wages for specialized technical judgment. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can reduce some analysis time but the specialized judgment and liability involved in equipment/technique selection still require paid expert time, keeping costs comparable to human-only workflows. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | GIS software and remote sensing platforms (e.g., Esri, Pix4D, Agisoft) incorporate AI-assisted tools for workflow optimization and sensor selection, but these require human expertise for final validation of complex accuracy standards. Deployed products assist with selection but have not fully replaced the expert judgment needed for specialized or non-standard accuracy requirements. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously selects sensing techniques and plotting equipment to meet accuracy standards; existing GIS/photogrammetry software assists computation but decision-making remains human-driven. |
Determine map content and layout, as well as production specifications such as scale, size, projection, and colors, and direct production to ensure that specifications are followed.
33CI 30–35 · exposure 25 · augmentation 75 · importance 3.8/5 · click for rater detail
Determine map content and layout, as well as production specifications such as scale, size, projection, and colors, and direct production to ensure that specifications are followed.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Cartography and photogrammetry are moderately digitized sectors, but adoption of automation for content and layout decisions is slower than in purely digital fields; most organizations still rely on human cartographers for strategic decisions despite having digital tools available. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | placeholder |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment a cartographer's productivity by auto-suggesting layouts, generating specification templates, automating color and scale analysis, and flagging potential production issues, allowing the human to focus on content validation and creative refinement. |
| Augmentation potential | claude-sonnet-5 | 4/5 | placeholder |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with some layout and specification suggestions, but determining map content requires domain expertise, stakeholder consultation, and creative judgment that current systems cannot fully automate. The 'directing production' component involves oversight and decision-making that remains largely human-driven. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a judgment-heavy design and directorial task combining client requirements, cartographic conventions, and quality oversight; AI can assist with drafts but cannot autonomously make final content/layout decisions and direct production reliably."},"feasibility":{"rating":2,"rationale":"GIS software has automation for symbolization and layout templates, but no deployed product autonomously determines full map specifications and directs production without a cartographer's oversight."},"cost_ratio":{"rating":2,"rationale":"Software tools reduce some labor but a skilled cartographer's judgment and quality control still dominate cost; AI alone doesn't replace the full task at dramatically lower cost."},"barriers":{"rating":2,"rationale":"No formal licensing requirement generally, but organizational quality standards, client sign-off, and domain expertise create moderate friction against full automation."},"adoption_velocity":{"rating":2,"rationale":"Cartography/GIS is a niche technical field with slow-to-moderate AI adoption; automation is mostly in supporting tools rather than full task replacement."},"augmentation":{"rating":4,"rationale":"AI-assisted design tools, auto-layout suggestions, and generative mapping aids can meaningfully speed up specification drafting and production oversight while the cartographer retains final control."}}</br>```json{ |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Cartography often requires stakeholder sign-off on content decisions and regulatory compliance (e.g., for government or utility mapping), creating some friction. However, there are no hard legal requirements that a licensed human must perform the task, though organizational norms and liability concerns around accuracy create moderate adoption resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | placeholder |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for layout and specification assistance have moderate costs, but integrating them into production workflows and maintaining quality oversight requires significant human labor, keeping total cost close to or exceeding the cost of a domain-expert cartographer. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | placeholder |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools can generate layout suggestions and process technical specifications, no deployed product reliably determines map content and directs production end-to-end. Tools exist for isolated steps (like suggesting scales or color palettes) but not for the full workflow with production oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | placeholder |
Determine guidelines that specify which source material is acceptable for use.
28CI 25–30 · exposure 20 · augmentation 50 · importance 4.0/5 · click for rater detail
Determine guidelines that specify which source material is acceptable for use.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While GIS and mapping sectors are moderately digitized, guideline-setting is a periodic, higher-level task rather than a frequent operational activity, limiting rapid AI adoption patterns in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Cartography and GIS is a moderately digitized field with growing AI tool use, but standard-setting and governance tasks lag behind more operational tasks in AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by summarizing source material specifications, flagging quality metrics, and identifying gaps in documentation, helping human cartographers make faster, more systematic guideline decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help synthesize existing standards, summarize data quality metrics, and compare source datasets, aiding the human who ultimately sets guidelines. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist in analyzing source material metadata and flagging potential quality issues, but determining acceptable guidelines requires domain expertise, legal judgment, and organizational policy understanding that current systems cannot reliably automate end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires domain judgment about data quality, provenance, and fitness-for-purpose standards that current AI cannot reliably determine autonomously; AI can assist drafting criteria but not decide them end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Standards bodies (USGS, NSGIC) and regulatory frameworks often require human cartographer sign-off on methodological guidelines; organizational liability concerns around data quality also create friction for full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement mandates a human specifically, but organizational standards, accountability for spatial data accuracy, and institutional review processes create meaningful friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI analysis of source material candidates provides some cost reduction, but the expertise and judgment required to establish defensible acceptance guidelines means human cartographers/GIS specialists remain necessary, keeping costs comparable or higher than pure automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot perform the core judgment task independently, any AI use requires substantial human oversight, keeping costs comparable to or only marginally below human-only performance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some tools exist to evaluate image quality and metadata, but no deployed product reliably determines organizational acceptance guidelines for diverse source materials in production settings without human expert review. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously set source-material acceptability guidelines for cartographic work; this remains a human policy-setting function. |
Study legal records to establish boundaries of local, national, and international properties.
25CI 25–25 · exposure 25 · augmentation 63 · importance 4.3/5 · click for rater detail
Study legal records to establish boundaries of local, national, and international properties.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Property boundary work is conducted by specialized professionals (cartographers, surveyors, land attorneys) in relatively low-digitization sectors. Adoption of AI-driven solutions is slow; most organizations still rely on manual legal review and expert interpretation rather than automated or AI-assisted workflows. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Cartography and surveying are a niche, moderately digitized field with slow, cautious AI adoption due to legal stakes and specialized data formats. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by automating document search, extracting key legal terms and dates, flagging ambiguities, and organizing records for human expert review, thus raising efficiency. However, the human expert remains essential for interpreting law and making final boundary determinations. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly speed up searching, summarizing, and cross-referencing legal records, greatly aiding cartographers even though final boundary determination remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and summarize text from legal documents, establishing property boundaries requires interpreting complex legal language, resolving ambiguities, cross-referencing historical records, and applying jurisdiction-specific law—tasks that demand expert judgment and legal knowledge that current AI systems lack. Partial automation (document retrieval, text extraction) is feasible, but end-to-end boundary establishment with 50% time savings at equal quality is not reliably achievable today. |
| Task automatability | claude-sonnet-5 | 2/5 | Extracting boundary information from legal records requires nuanced legal interpretation, resolving ambiguities, and cross-referencing historical documents, which current AI can assist but not fully automate to equal quality end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Establishing property boundaries carries high liability risk and often requires licensed surveyors or attorneys to sign off, especially for disputed or international properties. Regulatory frameworks and legal standing demands mean a qualified human must validate and assume responsibility for the determination. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Boundary determination often has legal and liability implications, and licensed professionals (surveyors, cartographers) are typically required to certify results, creating significant regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for document processing is cheap, but legal expertise required for oversight and validation is expensive; the human expert still must perform the critical judgment work. The all-in cost (AI + human expert review) remains comparable to or exceeds hiring a qualified cartographer or surveyor familiar with property law. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply process large volumes of text, but the need for expert legal/cartographic review to avoid costly boundary disputes keeps overall cost comparable to skilled human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Document analysis tools and optical character recognition exist and work at scale, but no mature product reliably performs the full task of legal boundary establishment without expert human review. Current systems can assist with document discovery and extraction but cannot independently interpret conflicting legal claims or render binding boundary determinations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some document-analysis and NLP tools can parse deeds and surveys, but no deployed product reliably establishes legal property boundaries autonomously in production settings. |
Related occupations — Architecture & Engineering
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.