Cartographers and photogrammetrists
Also called Cartographer, Photogrammetrist, GIS cartographer, Mapping specialist, Geospatial analyst
In brief
In May 2025, BLS counted 14,260 US cartographers and photogrammetrists, with median pay of $81,390 a year. Of the BLS counts since 2003, the May 2025 count is the highest. The earliest figure here is an estimate of 2,500 from 1900, not measured the same way as today's count. The last full technology era for cartographers and photogrammetrists: LiDAR point clouds, cloud GIS, and commercial UAV mapping, 2010 to 2020. The UAV mapping boom directly increased demand for photogrammetrists who could operate drone-based survey pipelines end-to-end.
What the sources say
Official outlook
BLS projects employment to grow 7.4% from 2025 to 2035.
AI exposure studies
- 46% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.
Last updated .
Cite this page
Future History. (2026, October 11). Cartographers and photogrammetrists: jobs and pay since 1900 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/17-1021
Future History. "Cartographers and photogrammetrists: jobs and pay since 1900." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/17-1021.
@misc{futurehistory_17_1021, author = {{Future History}}, title = {Cartographers and photogrammetrists: jobs and pay since 1900}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/17-1021}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }
The access date is today's date on your device.
≈ $79,303 in 2024 prices (derived by us, CPI-adjusted)
French army officer Aime Laussedat begins experiments using photographs to produce topographic maps, work he demonstrates formally to the Academie des Sciences in 1859. He is retrospectively recognized as the "father of photogrammetry." The German architect Albrecht Meydenbauer coins the term "photogrammetrie" in his 1867 article "Die Photometrographie," naming the new discipline. These events lay the intellectual foundation for the industrial-scale mapping profession that would develop over the next century.
USGS launches the formal National Topographic Map program under the leadership of geographer Henry Gannett (1846-1914), often called the father of American topographic mapping. The program begins systematic surveying and mapping of the continental US at the 7.5-minute quadrangle scale, creating sustained federal demand for trained cartographic draftsmen that would persist for a century. By 1900, USGS employed several hundred cartographers at its Washington DC offices and regional field stations.
By the end of World War I, both Allied and German forces are photographing the entire Western Front twice daily and have taken over half a million aerial photographs since the beginning of the conflict. The mosaic technique, combining overlapping vertical photographs into a continuous photographic map, is standard operational practice. The war transforms aerial photography from an experimental curiosity into a proven production method, and thousands of trained photo interpreters and mapping specialists return to civilian life with transferable skills.
The American Society of Photogrammetry is founded, formalizing photogrammetry as a distinct professional discipline in the United States. The organization, later renamed the American Society for Photogrammetry and Remote Sensing (ASPRS) in 1985, becomes the primary professional body for the field, publishing the journal Photogrammetric Engineering (later Photogrammetric Engineering and Remote Sensing) from 1937 onward and certifying practitioners in aerial photography, photogrammetric mapping, and remote sensing.
The Army Map Service is established as the primary military cartographic agency of the US Department of Defense, consolidating mapping activities previously dispersed across Army Corps of Engineers units. Between 1941 and 1945, the AMS produces over 40,000 map types covering 400,000 square miles with more than 500 million copies distributed to military units worldwide. Civilian women are recruited and trained as "Military Mapping Maidens" after Pearl Harbor, with 200 completing the inaugural 1942 photogrammetric production training course. The AMS transforms cartography from a small specialist profession into a large-scale industrial operation.
The Defense Mapping Agency (DMA) is established on January 1, 1972, consolidating the Army Map Service, the Navy's Hydrographic Office, and the Air Force's Aeronautical Chart and Information Center into a single Department of Defense mapping agency. DMA becomes one of the largest mapping organizations in the world, employing thousands of civilian cartographers and photogrammetrists at production centers in Bethesda, Maryland and St. Louis, Missouri. It is redesignated as the National Imagery and Mapping Agency (NIMA) in 1996 and the National Geospatial-Intelligence Agency (NGA) in 2003.
ESRI releases ArcInfo, the first fully integrated commercial geographic information system, developed by Jack Dangermond's Environmental Systems Research Institute. ArcInfo marks the beginning of the end for analog cartographic drafting as the dominant production method. Over the following decade, GIS-based map production gradually replaces the ink-on-paper drafting workflow at federal agencies, engineering firms, and commercial mapping companies. By the time ArcInfo reaches Windows NT in 1994, the transition from the analog drafting table to the digital workstation is effectively complete for most production cartography.
The FAA releases Part 107, the small unmanned aircraft systems rule, on August 29, 2016, creating a formal legal framework for commercial UAV operations in the United States. For photogrammetrists, Part 107 removes the primary barrier to commercial drone survey work: operators no longer need a full pilot certificate, only the Part 107 Remote Pilot Certificate (a written test). The rule triggers a wave of small photogrammetry firms and individual consultants entering the drone mapping market. The commercial drone mapping and surveying segment reaches $5 billion globally by 2024 with a 15%+ compound annual growth rate.
Will AI replace cartographers and photogrammetrists? What the research says
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.
What's changing day to day for cartographers and photogrammetrists
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is sitting alongside you hereProcess drone survey deliverables using Pix4Dmapper AI or DroneDeploy AI: ingest raw drone imagery with embedded GPS metadata and ground control point (GCP) coordinates
Process drone survey deliverables using Pix4Dmapper AI or DroneDeploy AI: ingest raw drone imagery with embedded GPS metadata and ground control point (GCP) coordinates; configure the AI photogrammetric processing pipeline (tie point matching, bundle block adjustment, dense point cloud generation, DSM/DTM derivation, orthomosaic production); validate horizontal and vertical accuracy against GCP checkpoints to confirm deliverables meet project specification (e.g., ASPRS Accuracy Standards Class 1 or FEMA floodplain mapping 18-cm RMSE); export georeferenced orthomosaic, point cloud (.LAS), and digital elevation model to the project GIS.[4],[1]
Pix4Dmapper AI and DroneDeploy AI automate the bulk of drone photogrammetric processing (tie point matching, bundle adjustment, point cloud densification) but the AI pipeline is only as accurate as the ground control network the photogrammetrist designs and measures. The most common source of deliverable failure in AI-processed drone surveys is inadequate GCP distribution: if all GCPs cluster in one part of the project area, the AI bundle adjustment will produce low reported RMSE on those points while accumulating systematic error across the flight. Build a disciplined GCP layout practice: minimum several GCPs per block with perimeter coverage, independent checkpoints separate from control, and a pre-flight review of the AI processing report's reprojection error residuals before releasing any deliverable. A photogrammetrist who can diagnose and remediate AI bundle adjustment failures, rather than just launching the pipeline and accepting the output, commands a strong premium over operators who treat the AI as a black box.
AI is sitting alongside you hereExtract cartographic features from satellite or aerial imagery using ESRI ArcGIS Pro AI deep learning tools: train or apply pre-trained convolutional neural network models (e.g., ArcGIS Image Analyst's building footprint detector, road extractor, or land cover classifier) against multi-spectral or RGB imagery
Extract cartographic features from satellite or aerial imagery using ESRI ArcGIS Pro AI deep learning tools: train or apply pre-trained convolutional neural network models (e.g., ArcGIS Image Analyst's building footprint detector, road extractor, or land cover classifier) against multi-spectral or RGB imagery; review AI-extracted feature layers for completeness and commission/omission errors; manually correct edge cases (partially obscured features, shadows, mixed-pixel boundary ambiguity) that the model misclassifies; integrate verified AI-extracted features with existing authoritative GIS base data, resolving geometry conflicts and attribute schema alignment before committing the updated map layer.[3],[8]
ArcGIS Image Analyst AI feature extraction and Google Earth Engine ML classifiers can produce building footprints, road centerlines, and land cover polygons at scales and speeds no manual digitizing workflow can match, but AI-extracted features require systematic quality review before they are fit for authoritative mapping. The most reliable QA workflow is a stratified random sample: select a small share of the AI-extracted features across different land cover types, imagery conditions (shadowed areas, dense vegetation margins, urban canyon reflections), and resolution zones; compare each against the source imagery manually; compute precision/recall by stratum. When precision or recall drops below the project acceptance threshold in any stratum, identify the imagery conditions causing failures and either retrain the model with targeted examples or flag that stratum for manual digitizing. Cartographers who can characterize AI model failure modes by imagery condition are significantly more valuable than those who run the model and release the output without stratified QA.
AI is sitting alongside you herePerform large-area land cover classification and multi-temporal change detection using Google Earth Engine or Microsoft Planetary Computer: ingest multi-year Sentinel-2, Landsat, or commercial satellite imagery archives
Perform large-area land cover classification and multi-temporal change detection using Google Earth Engine or Microsoft Planetary Computer: ingest multi-year Sentinel-2, Landsat, or commercial satellite imagery archives; apply supervised ML classifiers (Random Forest, SVM, or neural network models) trained on known land cover samples; produce change detection maps showing conversion between classes (forest to cropland, wetland loss, urban expansion, wildfire extent) with accuracy statistics; generate cartographic products (GeoTIFF rasters, classified vector polygons, change area statistics tables) for agency clients, environmental reporting, or climate-monitoring programs.[8],[9]
Google Earth Engine and Planetary Computer ML pipelines can classify land cover across entire continents in hours, but ML classifier accuracy is highly dependent on the quality and representativeness of the training samples the cartographer provides. The most common failure mode is class imbalance in training data: if forest samples are collected only from dense old-growth stands, the classifier will misidentify degraded or edge-affected forest as shrubland. Build a systematic training sample collection practice: use a stratified random sampling design across the full range of spectral variability within each class, include samples from multiple seasons and multiple geographic sub-regions, and validate each classifier against an independent accuracy assessment set before any deliverable is released. For climate-monitoring and regulatory reporting products, document the classifier training sample provenance and accuracy statistics in the metadata: these become part of the audit trail for federal and international reporting.
Where cartographers and photogrammetrists go next
Natural next steps for someone with your foundation: not exits, evolutions.
- Remote Sensing Scientists and TechnologistsNo timeline for this role yet
- Urban and Regional Planners
- US median pay
- $89,320 (May 2025)
- BLS outlook 2025-35
- +3.9%
- GeographersNo timeline for this role yet
What each move takes
Remote Sensing Scientists and Technologists
Cartographers and photogrammetrists who specialize in satellite imagery analysis, multi-temporal change detection, and Google Earth Engine or Planetary Computer ML workflows are already performing the core work of Remote Sensing Scientists. The pivot formalizes this shift from map production to scientific analysis, moving from deliverable-oriented cartographic output toward publication-quality remote sensing research and data product development. Remote sensing scientists are specifically in demand for climate monitoring, ecological assessment, and defense imagery analysis programs that require both geospatial AI skill and the scientific rigor to document methodology and uncertainty for peer review or regulatory submission. The CRI delta is neutral because both roles face similar AI augmentation profiles and similar demand patterns in the 2025–2026 satellite imagery and climate monitoring boom.
- · Radiometric calibration and atmospheric correction: master top-of-atmosphere reflectance conversion, atmospheric correction algorithms (MODTRAN, 6S, Sen2Cor for Sentinel-2), BRDF normalization, and surface reflectance product validation against field spectroradiometer measurements, the scientific foundation that distinguishes remote sensing research from applied cartographic production
- · Scientific Python remote sensing stack: rasterio, xarray, GDAL, geopandas, and scikit-learn for satellite data analysis; rioxarray for multi-temporal raster stacks; matplotlib and cartopy for scientific visualization; numpy/scipy for statistical analysis of classification accuracy and trend detection
- · Accuracy assessment methodology: Olofsson et al. 2014 Good Practices framework for stratified random accuracy assessment; area estimation with confidence intervals from error matrices; sample design for rare change classes; producing publishable accuracy tables for journal submission or regulatory reporting
- · SAR (Synthetic Aperture Radar) basics: Sentinel-1 C-band SAR amplitude and coherence for surface deformation, flood mapping, and vegetation structure; SAR backscatter interpretation for soil moisture and freeze-thaw state; InSAR phase analysis for ground subsidence monitoring, a complementary modality to optical imagery that opens government and defense remote sensing programs
- · Scientific writing and data product documentation: producing FGDC and ISO 19115 compliant metadata, writing peer-reviewable methods sections for remote sensing classification studies, and structuring data products for submission to USGS, NASA EarthData, or ESA open data portals
How has the work of cartographers and photogrammetrists changed since 1852?
The tools that defined the work, one line per era. Select an era to see how it reshaped the work.
Ground survey instruments and hand drafting (theodolite, plane table, ruling pen)
Through the mid-19th century into the early 20th, cartographic production was entirely manual: field measurements from theodolites and plane tables were brought back to a drafting room, where cartographers transferred data to paper using ruling pens, compasses, and brush-and-ink techniques. A skilled draftsman could produce one quadrangle sheet in several weeks. The role required both mathematical precision and fine-motor artistic skill; the two were not separable. The USGS national topographic mapping program launched in 1884 employed hundreds of such draftsmen at its Washington DC and regional offices.
Effect on the workEmployment was constrained by the hand-drafting bottleneck: a skilled cartographic draftsman could produce perhaps one completed quadrangle map sheet per month. Output could only scale by hiring more people. The craft was highly skilled and entry barriers were high.
Aerial photography and terrestrial photogrammetry (WWI reconnaissance cameras, stereoplotters)
World War I transformed cartography by demonstrating that aerial photographs could map terrain faster and more accurately than any ground survey party. Both sides were photographing the entire Western Front twice daily by 1918, producing over half a million images. When hostilities ended, commercial aerial survey firms adapted military technology to civilian mapping. Sherman Fairchild photographed all of Manhattan from the air in the 1920s; Aerofilms Ltd in Britain was conducting vertical photographic surveys for ordnance mapping by 1921. The photogrammetric stereoplotter, which allowed a trained operator to reconstruct three-dimensional terrain from overlapping photographs viewed through a stereoscope, emerged as the core production tool. By World War II, photogrammetric production had scaled massively: the US Army Map Service produced over 500 million map copies between 1941 and 1945.
Effect on the workAerial photography and photogrammetry dramatically increased the speed of map production but required a new kind of specialist: someone who could operate optical-mechanical stereoplotters, calibrate cameras, and manage the photographic processing chain. The profession grew sharply from the WWI baseline through WWII, reaching an estimated 12,000 civilian cartographers and photogrammetrists in US federal programs alone at the 1945 wartime peak.
Precision analog stereoplotters (Wild A8, Kern PG2, Zeiss Planimat)
The postwar era produced the most precise analog stereoplotting instruments in history. The Wild A8, manufactured between 1952 and 1980 with over 1,070 units produced, became the workhorse of photogrammetric mapping agencies worldwide. The Kern PG2, introduced in 1960 with hundreds of units manufactured through 1985, matched the A8's accuracy in a slightly different optical configuration. These instruments allowed a trained operator to measure three-dimensional point positions from overlapping aerial photographs with centimeter-level accuracy. The Defense Mapping Agency (established 1972 from the Army Map Service lineage) employed thousands of photogrammetrists using these instruments for global military mapping. The operator stared through eyepieces at a "floating mark" and traced terrain contours by hand, keeping the mark on the apparent ground surface while a pantograph arm transferred the movement to a drafting sheet. Productive output was perhaps several hours of useful measurement per day per operator.
Effect on the workAnalog stereoplotters raised per-worker output substantially compared to purely ground-surveyed cartography but remained bottlenecked by the operator's physical work rate. Large mapping programs required dozens or hundreds of trained plotter operators; the USGS National Mapping Division and DMA were among the largest employers of this skill in the world. Employment remained relatively stable in the Cold War era, sustained by federal mapping mandates.
GIS software and analytical photogrammetry (ArcInfo 1981, analytical plotters, desktop mapping)
Two parallel revolutions reshaped cartography in the 1980s. On the mapping side, analytical stereoplotters (computer-controlled instruments that replaced the purely mechanical analog plotter) arrived in the late 1960s and became production-standard by the early 1980s; they allowed automatic calculation of three-dimensional positions rather than requiring the operator to physically guide a floating mark. On the cartographic side, ESRI's ArcInfo (1981) was the first fully commercial geographic information system, transitioning map production from ink-on-paper drafting to digital vector databases. ArcInfo reached Windows NT in 1994, placing GIS tools on standard office computers. By the mid-1990s, a single GIS analyst using digital data and desktop software could produce map compilations that had previously required a team of draftsmen. The cartographic drafting workforce contracted substantially.
Effect on the workThe GIS transition is the most disruptive technological shift in cartography's modern history. Manual map drafting was effectively eliminated as a distinct occupation; cartographers who did not acquire GIS skills found their roles automated. Federal mapping programs shed production draftsman positions as digital workflows replaced analog production. Employment among cartographers and photogrammetrists declined from the Cold War federal peak but stabilized as new GIS-based applications (urban planning, telecommunications network design, environmental assessment) created offsetting demand.
Softcopy photogrammetric workstations and digital aerial cameras (ImageStation, Z/I Imaging)
The final break from the analog stereoplotter era came with softcopy photogrammetric workstations, available from the early 1990s and production-dominant by the late 1990s. Systems such as Intergraph's Z/I Imaging ImageStation replaced the optical-mechanical stereoplotter with high-resolution display systems that rendered digital aerial imagery stereoscopically on computer monitors. Operators measured terrain using on-screen floating marks rather than physical eyepieces; photogrammetric calculations ran on standard computer hardware. The transition cut capital costs dramatically (a softcopy workstation cost a fraction of a precision analog plotter), raised throughput, and enabled digital photogrammetric outputs to flow directly into GIS databases without intermediate drafting steps. Digital aerial cameras (replacing film) arrived in the mid-2000s, completing the all-digital pipeline from aircraft to GIS.
Effect on the workSoftcopy workstations made photogrammetric production accessible to organizations that could not afford analog plotter inventories, expanding the private-sector photogrammetry market. At the same time, automation within the softcopy systems (automatic tie point extraction, batch bundle adjustment) reduced the manual measurement work per project. Employment held broadly flat through the 2000s as new commercial applications offset productivity-driven reductions in large federal programs.
LiDAR point clouds, cloud GIS (Google Earth Engine 2010), and commercial UAV mapping
Three technologies converged in the 2010s to expand the cartographer's toolkit dramatically. Airborne LiDAR, available since the late 1990s but production-mainstream by 2010, gave cartographers a tool for terrain mapping that penetrated forest canopy and produced millions of precise elevation points per square kilometer. Google Earth Engine (publicly available from 2010) put decades of global satellite imagery and cloud-scale ML computing tools in front of any researcher with a web browser. Commercial UAV (drone) mapping emerged as a distinct professional practice after the FAA's small UAS rule (Part 107) in 2016; photogrammetrists could now plan, fly, and process aerial surveys at a fraction of the cost of manned aircraft operations. The drone mapping and surveying market reached $5 billion globally by 2024 with compound annual growth.
Effect on the workThe UAV mapping boom directly increased demand for photogrammetrists who could operate drone-based survey pipelines end-to-end. Entry barriers dropped, attracting new practitioners from adjacent fields (civil engineering, environmental science, geology), while experienced photogrammetrists who added drone competency could command substantial salary premiums. Employment of 17-1021 began recovering from the GIS-transition trough, growing to 12,790 OEWS jobs by 2024.
AI feature extraction and automated photogrammetry (ArcGIS Pro AI, Pix4Dmapper AI, Google Earth Engine ML)
The AI tools that arrived at scale in 2022-2025 automate the parts of cartographic work that previously consumed the most operator time: feature extraction from imagery (building footprints, road networks, land cover classes), point cloud classification (ground vs. vegetation vs. structure), and bundle block adjustment in drone photogrammetric processing. ArcGIS Pro AI deep learning tools can extract building footprints from satellite imagery at continental scale in hours rather than weeks of manual digitizing. Pix4Dmapper AI processes drone survey data from raw images to georeferenced orthomosaic and elevation model with minimal operator intervention on standard projects. Google Earth Engine ML pipelines classify multi-decade land cover change across entire continents on shared compute. The cartographer's job is shifting from hands-on production operator to AI workflow designer, QA architect, and accuracy validator, with the highest-value work concentrating in legally accountable deliverables that AI cannot sign off on: boundary delimitations, regulatory floodplain maps, IPCC-reporting remote sensing products.
Effect on the workAI automation of routine digitizing and classification does not appear to be depressing employment in 2024-2026; demand for photogrammetrists who can operate AI-augmented pipelines, validate AI outputs, and manage production QA appears to be growing. BLS projects 6% occupational growth 2024-2034. The workforce gap in certified drone photogrammetrists and geospatial AI specialists is estimated at a large share of industry requirements by multiple market research analyses.
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Browse all rolesCartographers and Photogrammetrists employment and pay by year
View all 27 years of cited US data, each linked to its source
| Year | US employment | Annual pay (median unless marked) | Source | Report |
|---|---|---|---|---|
| 1900 | 2,500 | n/a | Est., USGS Circular 1050 Researched context | Report |
| 1945 | 12,000 | n/a | Est., NGA history Researched context | Report |
| 1965 | 9,000 | n/a | Est., Early Automation at the U.S. Geological… Researched context | Report |
| 2000 | 7,360 | $39,410 | BLS OEWS 2000 Official statistic | Report |
| 2003 | 8,940 | $44,170 | BLS OEWS May 2003 Official statistic | Report |
| 2004 | 9,870 | $46,080 | BLS OEWS May 2004 Official statistic | Report |
| 2005 | 11,260 | $48,250 | BLS OEWS May 2005 Official statistic | Report |
| 2006 | 11,430 | $48,240 | BLS OEWS May 2006 Official statistic | Report |
| 2007 | 11,460 | $49,970 | BLS OEWS May 2007 Official statistic | Report |
| 2008 | 11,690 | $51,180 | BLS OEWS May 2008 Official statistic | Report |
| 2009 | 11,750 | $53,050 | BLS OEWS May 2009 Official statistic | Report |
| 2010 | 11,670 | $54,510 | BLS OEWS May 2010 Official statistic | Report |
| 2011 | 11,240 | $55,970 | BLS OEWS May 2011 Official statistic | Report |
| 2012 | 11,490 | $57,440 | BLS OEWS May 2012 Official statistic | Report |
| 2013 | 11,290 | $58,840 | BLS OEWS May 2013 Official statistic | Report |
| 2014 | 11,610 | $60,930 | BLS OEWS May 2014 Official statistic | Report |
| 2015 | 11,970 | $61,880 | BLS OEWS May 2015 Official statistic | Report |
| 2016 | 12,100 | $62,750 | BLS OEWS May 2016 Official statistic | Report |
| 2017 | 11,440 | $63,990 | BLS OEWS May 2017 Official statistic | Report |
| 2018 | 11,050 | $64,430 | BLS OEWS May 2018 Official statistic | Report |
| 2019 | 11,300 | $65,470 | BLS OEWS May 2019 Official statistic | Report |
| 2020 | 12,600 | $68,380 | BLS OEWS May 2020 Official statistic | Report |
| 2021 | 12,610 | $68,900 | BLS OEWS May 2021 Official statistic | Report |
| 2022 | 13,270 | $71,890 | BLS OEWS May 2022 Official statistic | Report |
| 2023 | 12,330 | $76,210 | BLS OEWS May 2023 Official statistic | Report |
| 2024 | 12,790 | $78,380 | BLS OEWS May 2024 Official statistic | Report |
| 2025 | 14,260 | $81,390 | BLS OEWS May 2025 Official statistic | Report |
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