Return on Investment and Economic Impact of a NASA-Funded Swath Mapping Laser Altimeter Orbiter for Earth Surface Elevations Situational Awareness in 2031–2040

Abstract

This pathfinder business modeling paper presents a first-order economic analysis of a proposed Earth orbital swath mapping laser altimeter (EDGE: Earth Dynamics Geodetic Explorer), a mission concept targeted for launch in the early 2030s under the openly competed NASA’s Earth System Explorers program. EDGE represents a potentially significant leap in global Earth elevation mapping, aiming to achieve approximately 0.10-m vertical accuracy, which is more than a 10-fold improvement over existing global benchmarks of 5–10 m. We evaluate the economic return via a stacked AI-based modeling approach on the estimated $400–$450 million upfront satellite mission investment through a multisector impact assessment extending to ∼2040. Our business-oriented analysis builds upon established figures-of-merit frameworks from comparable Earth observation (EO) programs, particularly the United States Geological Survey (USGS) 3D Elevation Program (3DEP), which demonstrates a 5:1 return ratio from high-precision elevation data at regional scales. Our stacked AI/machine learning-modeling analysis estimates that EDGE’s nearly global coverage of Earth’s solid surfaces (including ice-covered regions, forests, bare land, agricultural areas, and coastal zones) will generate economic benefits of approximately $3–$5 billion annually by ∼2035, escalating to $8–$10 billion annually by 2040 under specific input parameters as commercial applications mature. Assuming launch in 2031, a 2-year mission life, and 1 year for data processing and release, initial economic benefits would begin accruing by ∼2034–2035. This would translate to a projected net present value (NPV) under a moderate adoption case of ∼$33B (at 3% discount) within 5 years of final EDGE mission data release, and a potential benefit–cost return on investment >50:1 (present value) by ∼2040 as the data ecosystem expands in response. Our analysis explores how EDGE’s unprecedented vertical elevation accuracy could realistically catalyze value across multiple sectors, including disaster management, infrastructure development, natural resource management, agriculture, and environmental change adaptation planning. Furthermore, we evaluate how this mission could potentially stimulate a public–private marketplace for orbital topographic data services analogous to the evolution seen in commercial satellite (2D) land imaging, with potential applications extending to lunar and Martian 3D mapping in direct support of NASA’s Moon-to-Mars human exploration program’s highest-priority data and technical gaps circa 2026.

INTRODUCTION

The Earth Dynamics Geodetic Explorer (EDGE) mission is a concept-stage NASA mission (target launch ∼2031 within NASA’s Earth System Explorers [ESE] program) designed to provide near-global, high vertical accuracy surface elevation data primarily for Earth science “environmental monitoring” of forests, ice-covered regions, and coastal zones.^1,2 EDGE employs swath mapping geodetic laser altimetry using 40 simultaneous laser beams to create contiguous coverage. With an estimated one-time investment of ∼$400M and required 2-year mission life in low Earth orbit, EDGE would achieve improved vertical elevation accuracy over current global benchmarks with ultra-high vertical ranging precision that no other active remote-sensing method delivers at this time. While current satellite-derived digital elevation models (DEMs) typically exhibit vertical errors of ∼5–10 m, EDGE aims to achieve approximately 0.10-m vertical accuracy—a more than 10-fold improvement over existing global benchmarks. This order-of-magnitude leap in vertical accuracy and spatial sampling resolution would be strategic ³ ; it would not only fulfill scientific needs^1,2 but also serve as a potential catalyst for a new generation of “situational-awareness” imaging/topography orbiters with new commercial partners. By providing a public, high-quality global elevation baseline for up to ∼80% of the Earth’s solid surfaces (ice-covered, forested, bare land, coastal zone, agricultural regions), EDGE is envisioned to jump-start a public–private ecosystem of orbital topographic mapping services between ∼2035 and 2040+, potentially akin to how 1970s government-funded Earth-imaging missions (e.g., Landsat) paved the way for today’s commercial imaging constellations such as Vantor’s WorldView and Legion. In short, EDGE’s ultra-precise 3D mapping of Earth’s surface would (under current assumptions) lay the foundation for measurable downstream economic and societal benefits while de-risking technology for future commercial follow-ons. ⁴

TECHNICAL OVERVIEW

EDGE builds on decades of NASA laser altimeter expertise (e.g., Mars Orbiter Laser Altimeter—MOLA, ⁵ and the Earth-orbiting Ice, Cloud, and land Elevation satellites [ICESat and ICESat-2] orbiters ² ) but advances the state of the art via swath mapping geodetic laser altimetry capabilities, using 40 simultaneous laser beams with “ranging” pixels to map a broad swath of terrain ( Fig. 1 ) in each orbital pass.^2,6 This yields contiguous high-resolution spatial mapping coverage rather than sparse tracks as is the current state of the art. Compared with optical stereo elevation mapping, laser altimetry provides much finer deterministic vertical precision (and accuracy) and all-weather, day/night consistency.^2,3,6 In fact, next-generation multi-beam or pixelated laser altimetry could increase mapping resolution and coverage by orders of magnitude (vs. single-beam instruments).^1,2 EDGE’s data in the form of near-global DEMs with ∼0.10 m vertical accuracy and high horizontal resolution represent a potential game-changer for the situational awareness of surface elevations and their short- and long-term changes on Earth’s surface. It would dramatically improve our knowledge of Earth’s fundamental topography ( Fig. 1 ), enabling more accurate models for everything from coastal lowland flood-prone zones to agricultural crop yields. Just as importantly, if NASA makes these data openly available (as is required for civil Earth science missions in this era of Open Science protocols), it can broadly enlarge surface elevation data access, allowing governments, businesses, and researchers worldwide to integrate it into decision-making. Via the opening up of elevation data access, our preliminary analyses suggest the creation of an EDGE platform interface (i.e., perhaps building on historical precedents such as these examples: National Oceanic and Atmospheric Administration (NOAA) weather data systems—specialized data requiring processing but with broad economic applications; USGS topographic mapping programs—direct precedent for elevation data; or GPS/GNSS data streams—foundational infrastructure enabling diverse applications). The EDGE mission’s strategic goal is thus twofold: deliver direct benefits, including better environmental hazard mapping (at new spatiotemporal scales), resource management, carbon-offset parameters, sea-level rise predictions, and serve as a catalyst by potentially spurring complementary commercial services in the near future (e.g., private satellites continually updating the state of Earth surface elevations at high vertical accuracy from spaceborne systems and value-added analytics companies). We employ stacked AI-based modeling approaches [Economic Impact Analysis Framework (SAMA Approach) section and Supplementary Data] to evaluate the potential for a commercial services model and its business figures of merit for the EDGE mission as a catalyst by means of a preliminary assessment, subject to improvement as input parameters are refined.

Fig. 1.

EDGE swath mapping laser altimeter orbiter mission concept showing 40-beam pattern sampling on different Earth surface types, which is under consideration by NASA for flight in the early 2030s as part of the Earth System Explorers program.^1,2 EDGE, Earth Dynamics Geodetic Explorer.

ECONOMIC IMPACT ANALYSIS FRAMEWORK (SAMA APPROACH)

To evaluate the EDGE mission’s potential economic return, we employ a Stacked AI Modeling Approach (SAMA), a structured toolkit developed with MIT Sloan that integrates traditional cost–benefit analysis with advanced AI/machine learning (ML) techniques (see Supplementary Data including Supplementary Tables S1 and S5). This approach allowed us to simulate net present value (NPV), benefit–cost return on investment (ROI), and related metrics for EDGE under a range of scenarios rather than relying on single-point estimates. In essence, SAMA “stacks” multiple ML models to capture how EDGE’s high-precision surface elevation data could drive economic benefits across sectors through 2040. By design, it provides transparency into assumptions and a structured exploration of uncertainties: users can adjust data adoption rates, mission costs, or benefit parameters and immediately see the impact on projected NPV and ROI outcomes. This makes the analysis more robust and informative for decision-makers, especially in early mission formulation (Phases A and B), by highlighting which factors (e.g., adoption speed or discount rate) most influence the business case.

Methodology Overview

Information pertaining to the modeling methodology, sensitivity tests, and architecture is also provided in Supplementary Data, Parts I, II, and III. In summary, our SAMA framework proceeds through several key steps, each combining domain knowledge with AI/ML components (Supplementary Table S1: AI/ML Pipeline Flowchart). This high-level flowchart aims to visually represent the AI-based modeling pipeline workflow that created Figures 2 – 6 in the primary paper text. The workflow is as follows:

Fig. 2.

Examples of how total net present value (NPV) of benefits (cumulative 2031–2040, in 2025 US$) and the benefit-cost ratio (ROI) vary under different adoption-rate scenarios and discount rates. At a 3% discount rate (commonly used for long-term projects), the baseline moderate adoption case yields about $33.3B in NPV. If adoption is faster, NPV rises roughly 10%–12% to ∼$37B, whereas slower uptake reduces NPV by ∼11% to ∼$29.6B. Using a higher 5% discount rate (which places less weight on later-year benefits) lowers each NPV by approximately 20% (e.g., baseline NPV drops from $33.3B to ∼$26.6B). Even in the slow-adoption, 5% case, the most pessimistic combination, the present value of benefits is on the order of $23–$24B, which still vastly exceeds the ∼$0.45B mission cost. The ROI remains extraordinarily high across all scenarios. In the baseline scenario (∼$33B NPV @3% vs. $450M mission cost), ROI is about 74:1, meaning $74 of economic benefit per $1 of sunk cost. Even under slow adoption and a 5% discount scenario, ROI remains around ∼52:1, far above the break-even point. Lowering the mission cost (to ∼$400M) further boosts ROI by ∼12%–13%, so in a fast adoption scenario, 3% discount case the ROI reaches ∼82:1 (or ∼92:1 if cost is $400M). These results underscore that EDGE’s benefits outsize its upfront costs under any reasonable scenario. Thus, the EDGE mission would pay for itself many times over via these economic metrics in all cases evaluated. ROI, return on investment.

Fig. 3.

A 2D heatmap of the total NPV of benefits (in billions of U.S.$) as a function of the adoption rate (horizontal axis: slow, moderate, fast uptake of EDGE data into the digital economy) and the discount rate scenario (vertical axis: 3% vs. 5%). The baseline scenario (moderate adoption, 3% discount) yields ∼$33.3B in present value benefits. If adoption is fast (rightmost column), NPV increases to ∼$37.0B. In contrast, slow adoption (left column) produces about $29.6B at 3%. Moving from a 3% to a 5% discount rate (comparing the top and bottom rows) significantly reduces the NPV in each case, roughly a 20% drop (e.g., baseline NPV falls from $33.3B to ∼$26.6B). Notably, even in the least favorable combination (slow adoption + 5% discount, bottom-left), the NPV is still on the order of $23–$24B. This is an order of magnitude greater than the upfront mission ∼$0.45B cost, indicating strong net economic value for EDGE’s mission under the realistic evaluated scenarios we considered.

Fig. 4.

Tornado diagrams illustrating which uncertain inputs have the largest influence on EDGE’s NPV (left panel) and ROI (right panel). Each horizontal bar corresponds to one input factor and spans from the outcome under a low-case assumption (left end of the bar) to the outcome under a high-case assumption (right end). The vertical red line indicates the baseline value (NPV ≈ $33.3B at 3% discount, moderate adoption; ROI ≈ 74:1 at 3%, $450M upfront mission cost).

Fig. 5.

Projected annual economic benefits by sector from expected EDGE data ¹ under one specific illustrative adoption scenario (values in U.S.$ billions per year). By ∼2040, the combined impact across major sectors reaches nearly $9B/year in this model, vastly exceeding the one-time mission cost. Actual outcomes will depend on data uptake and complementary investments, but a high ROI is anticipated (>50:1). Values shown with ± represent plausible ranges under conservative versus optimistic adoption scenarios (derived from AI/ML sectoral forecasts; see Supplementary Data, as well as Figs. 2 – 4 , 6 ).

Fig. 6.

Summary graphic showing benefit–cost ROI (vertical axis) versus time horizon (horizontal axis) for three scenarios (slow, moderate, fast) for adoption rate, with NPV illustrated by sector with annual values shown by business sector (see Supplementary Data, Part III).

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  Sector identification (unsupervised ML): We first identified the economic sectors most likely to benefit from improved elevation data. Using unsupervised learning (K-Means and hierarchical clustering), the model analyzed industry datasets to cluster sectors by “elevation data sensitivity”; essentially, how strongly better topography data could improve outcomes in that sector. This data-driven step validated the focus on four primary sectors: risk and insurance, agriculture, infrastructure/urban planning, and digital services, which align with expert expectations. (The Supplementary Data, Part I, provides an overview of selected methodology and data sources for this sector analysis.)

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  Sector adoption and impact forecasting (supervised ML): For each identified sector, we built ensemble machine learning regressors (e.g., Random Forest and Gradient Boosted Trees) to predict how the sector’s performance (productivity, cost savings, loss reductions, etc.) might respond over time to the availability of EDGE data. These models were trained and validated on historical analogs, for instance, past adoption rates of new EO data (e.g., uptake of USGS aircraft LiDAR data) and sector growth indicators, then anchored to known benefit benchmarks from existing programs (USGS 3D Elevation Program [3DEP] and NOAA’s National Enhanced Elevation Assessment [NEEA] study). This helped ensure that the simulated economic impacts for each sector remained in line with real-world observations (see Supplementary Data, Part III, for modeling architecture and validation).

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  Temporal dynamics (deep learning): Next, a recurrent neural network model long short-term memory (LSTM) was used to capture the timing of benefits, that is, how quickly and broadly EDGE data might be adopted from its initial release (∼2031) to maturity (∼2040). The LSTM learned from analogous technology diffusion curves to simulate adoption lags and compounding effects (e.g., early adopters spurring wider usage). This provides year-by-year benefit trajectories instead of assuming immediate full uptake. By 2035, for example, moderate adoption might only realize a portion of total potential benefits, ramping up significantly by 2040 as applications spread. These temporal adoption scenarios (fast vs. moderate vs. slow uptake) are explicitly modeled and carried through the ROI analysis (details in Supplementary Data, Part II).

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  Spatial amplification and risk mapping (CNN analysis): In parallel, we incorporated a spatial dimension using convolutional neural networks. CNN-based models evaluated where on the globe EDGE’s data would have the greatest impact by overlaying high-resolution elevation improvements on known risk and resource distributions. For instance, the model considered global floodplain maps, coastal urban centers, agricultural zones, etc., to identify “hotspots” where 10 cm DEM accuracy would yield outsized benefits (e.g., flood damage avoided in low-lying cities, or yield gains in precisely leveled farmlands). This spatial layer helped ensure our sector benefit estimates account for geographic concentration of benefits (for example, insurance benefits concentrate in flood-prone regions). It also supports transparency; stakeholders can see where EDGE data have the most economic leverage.

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  Economic translation layer: The final layer of SAMA translates the AI-predicted usage and efficiencies into dollar values. We applied established economic multipliers and benefit coefficients from analogous programs to convert increased data usage into monetary benefit estimates. For example, if the model predicts that better elevation data will improve crop yields by X% in certain areas, we multiply that by economic values (crop prices, etc.) to compute dollar benefits. Critically, we calibrated these conversions using real benchmarks, for example, the 2011 NEEA quantified hundreds of millions of dollars per year in savings for applications like precision farming and flood mitigation at national scale. We used such figures to sanity-check our outputs for each sector. Similarly, NASA’s prior missions Global Ecosystem Dynamics Investigation (GEDI) and ICESat-2 informed plausible ranges for environmental benefits (e.g., carbon, water resource improvements) and helped ensure our upper-bound scenarios were realistic. The outcome of this step is a set of annual benefit estimates by sector, which we then aggregate.

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  Uncertainty and sensitivity analysis: These were explicitly integrated into the SAMA to ensure that projected economic benefits are not treated as deterministic point estimates. Model uncertainty was quantified using ensemble variance from the supervised learning components (Random Forest and Gradient Boosting), providing confidence bounds on sector-level benefit forecasts. In addition, structured scenario-based sensitivity analyses were performed across the most influential exogenous parameters, including technology adoption rates (slow, moderate, and fast uptake corresponding to approximately ±20% variation from baseline), discount rates (3% and 5%), mission cost assumptions, and sector-specific benefit ranges derived from benchmark studies. Each parameter was varied independently around the baseline while holding others constant, consistent with standard economic sensitivity analysis.

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  Aggregation and ROI calculation: SAMA finally rolls up all sectoral benefits year by year and computes the cumulative NPV and ROI for the mission (Supplementary Table S1). We discount future benefits to present value (using a 3% real discount rate by default, per OMB/NASA guidance) and compare against the mission’s lifecycle cost (assumed ∼$450M upfront + operations). The result is a range of NPV outcomes and benefit–cost ratios for each scenario. ROI exceeds 50:1 across all scenarios analyzed, ranging from ∼52:1 (conservative) to ∼100:1 (optimistic). In our baseline case (moderate adoption, 3% discount), the model projects roughly $33 billion NPV (through 2040) for EDGE’s data, against ∼$0.45B cost, an ROI on the order of 70:1. Even under more conservative assumptions, for instance slower user uptake or a higher discount rate, the net present benefits remain an order of magnitude greater than costs (e.g., ∼$23–$24B NPV in a pessimistic case). We emphasize that these figures are not single-value predictions but part of a spectrum of scenario results that SAMA produces, complete with error bars and sensitivity ranges (shown in Figs. 2 – 4 , 6 ; Supplementary Data, Part II).

Overall, this stacked modeling approach scaffolds a transparent, modular framework for economic analysis. Each layer of the pipeline corresponds to an interpretable element of the business case (e.g., “How many users adopt?” → “How much do they gain?” → “What is that worth in dollars?”), and each can be examined or stress-tested independently. By performing Monte Carlo simulations and sensitivity analyses (see Supplementary Data, Part II) across key variables, such as adoption rate and discount rate, we quantify the uncertainty in outcomes and identify which inputs drive the most variance in results. For example, Figure 4 ’s tornado chart reveals that the overall adoption rate and the agriculture sector’s benefit assumptions are among the biggest drivers of NPV variability (each can swing NPV by roughly ±$3–$4B). This insight underscores the value of exploring different adoption scenarios: if uptake of EDGE data is slower than expected, the payback is delayed but still strongly positive; if adoption is faster or broader (e.g., spurred by commercial partnerships), the ROI could climb even higher. In all cases explored, EDGE’s benefits outweighed costs by a large margin, giving confidence in the mission’s economic robustness. The SAMA framework thus not only yields headline metrics like “50:1 ROI by 2040,” but more importantly provides a systematic, traceable way to arrive at those values, allowing stakeholders to better see why, and under what conditions such returns might be realized ( Fig. 6 ). Key underlying assumptions, model parameters, and computational steps are fully documented in Supplementary Data (i.e., Supplementary Data, Part III, contains a view of the modeling architecture and workflow diagrams with input sources), ensuring that this approach can be independently assessed and validated.

Table 1 summarizes key economic input assumptions used in our analysis, and Table 2 outlines the SAMA modeling architecture, scaffolding, and data sources, including an overview of key AI models and datasets that underpin the results (see also Supplementary Data).

Table 1.

Key Economic Input Assumptions (for ROI/NPV Modeling of EDGE)

Assumption	Value/Scenario
Adoption rate scenarios	Slow: ∼30% user uptake by 2035 (reaching ∼60% by 2040); Moderate: ∼50% by 2035 (∼80% by 2040); Fast: ∼70% by 2035 (∼90%+ by 2040). These scenarios bracket the plausible range of how quickly governments and industries adopt EDGE-derived data and services
Discount rate	3% real discount (baseline, reflecting long-term public investment rate); 5% used in sensitivity tests (higher rate reduces NPV by ∼20% relative to 3% case). Note: Even at 5%, EDGE’s NPV remains ∼$26B in the baseline scenario, far exceeding costs
Mission cost (lifecycle)	∼RO$400–$450 million upfront mission cost (Phases A–D, 2025 U.S.$) for satellite development and launch; ∼2-year nominal mission life. Plus, ∼$100M in data platform and operations costs over the life of the mission. (Sensitivity: lower cost ∼$400M increases ROI ∼10%–15%)
Annual benefit by 2035	∼$3–$5B/year (total across all sectors) under the moderate adoption/base case. By 2035 (within ∼5 years of data release), significant but not yet full market penetration is assumed—yielding roughly half of the eventual annual benefits
Annual benefit by 2040	∼$8–$10B/year (total across sectors) under moderate assumptions. By 2040, EDGE’s data ecosystem is near full maturity in this scenario, roughly tripling the 2035 annual benefits as applications spread
Benefit by sector (2035)	Risk and insurance: ∼$1B/year; agriculture: ∼$1–$2B/year; infrastructure: ∼$0.5–$1B/year; digital/other: ∼$0.3B/year. (Illustrative breakdown of the ∼$4B total in 2035, showing agriculture and insurance as leading early beneficiaries)
Benefit by sector (2040)	Risk and Insurance: ∼$2–$3B/year; agriculture: ∼$3–$4B/year; infrastructure: ∼$1.5–$2B/year; digital/other: ∼$1B/year. By 2040 the annual total ∼$9B is distributed across major sectors, with agriculture likely the largest single segment (due to global scale of farming), followed by insurance/risk reduction, infrastructure, and new digital geospatial services

EDGE, Earth Dynamics Geodetic Explorer; NPV, net present value; ROI, return on investment.

Table 2.

Modeling Architecture and Data Sources (Stacked AI Modeling Approach)

Model/Technique	Role in SAMA pipeline	Data sources/Benchmarks used
Unsupervised learning: e.g., K-Means, hierarchical clustering	Identify key beneficiary sectors for EDGE data by grouping industries with similar elevation data needs/sensitivities.	Used economic sensitivity data from national studies (e.g., NEEA 2012 sector benefit data) to cluster sectors by potential benefit. Verified four main sectors (insurance, agriculture, infrastructure, digital) as top beneficiaries.
Ensemble ML regression: e.g., Random Forests, Gradient Boosted Trees	Forecast sector-specific adoption rates and benefit growth as EDGE data become available. Models nonlinear relationships between data quality and economic outcomes.	Trained on historical Earth observation adoption and impact data (e.g., Landsat/remote-sensing uptake, prior LiDAR project outcomes). Calibrated against USGS 3DEP and NOAA 3D Nation (NEEA) benefit findings by sector to ensure realism. Validated by comparing model predictions with documented results of earlier elevation programs (where available).
Temporal dynamics (RNN): e.g., long short-term memory (LSTM) networks	Model the time-dependent uptake of EDGE data and resulting benefits from initial release (∼2031) through 2040. Captures adoption lags and compounding effects over time.	Ingested analog time-series data for technology diffusion and policy adoption (e.g., rate of GIS/DEM integration in planning over past decades). In the absence of a direct historical analog for global LiDAR, the model’s scenarios were cross-checked with expert projections and adoption curves in Supplementary Data Part II.
Spatial analysis (CNN): e.g., convolutional neural networks	Incorporate spatial variability of benefits; projects where improvements in elevation data yield the highest returns (geographic “hotspots”).	Utilized global datasets for risk and exposure: e.g., flood hazard maps (for insurance sector), agricultural land distribution (for farming benefits), coastal urban elevation data. Also leveraged NASA DEM products (Shuttle Radar Topography Mission, etc.) as baselines. CNN scenario outputs helped estimate how benefits concentrate in certain regions (e.g., large floodplains) under EDGE’s global coverage.
Economic value translation: e.g., ROI/NPV computation layer	Convert model outputs (usage, efficiency gains) into monetary benefits; aggregate sector results into total NPV and ROI.	Applied economic multipliers and unit benefits from analogous programs: USGS 3DEP, NEEA (quantified $ benefits by use-case, informing our dollar-per-unit assumptions), and NASA mission data (GEDI, ICESat-2 outcomes used as reference for environmental benefits). Discounted cash flow analysis (3% base rate) for NPV. Ensured consistency with known economic assessments (e.g., benefit of better elevation data in flood control, per FEMA/USGS studies).

SAMA, Stacked AI Modeling Approach.

References: Key datasets and programs for simulation: (NASA and USGS data resources underpinning economic modeling utilized herein)^7–10

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USGS 3D Elevation Program (3DEP)—USGS 3DEP Program Page.

Description: A national program to acquire high-resolution three-dimensional elevation data (mostly via LiDAR) across the United States. 3DEP’s documented benefits (e.g., more efficient infrastructure design, better flood risk mapping) provided a real-world benchmark for this study; notably, a ∼5:1 benefit–cost ratio for precision elevation data in early implementations. We used 3DEP’s results to help validate EDGE’s projected ROI and to derive sector-specific benefit assumptions (e.g., millions saved annually in agriculture, and insurance from improved DEMs).

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NEEA—Dewberry 2012 NEEA Final Report.

Description: A landmark 2011–2012 study that assessed nationwide requirements for enhanced elevation data and estimated the potential economic benefits across multiple sectors. NEEA (sponsored by USGS & partners) found that an improved national elevation program could generate on the order of $1.2–$13 billion in new benefits per year once fully realized. Its detailed breakdown of benefits by use-case (e.g., precision farming, flood control, infrastructure planning) was used to calibrate our model’s input assumptions and validate that EDGE’s benefit projections in each sector were plausible (when scaled globally). NEEA effectively justified the creation of 3DEP, making it a foundational reference for our ROI analysis.

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NASA GEDI—NASA Earthdata GEDI Info.

Description: A NASA LiDAR instrument deployed on the International Space Station (2019–2023) that measured forest canopy height and 3D structure of ecosystems across the tropics and temperate regions. GEDI provided unprecedented data for carbon stock estimation and forest management. While primarily a scientific mission (no formal ROI in dollar terms), GEDI demonstrated the value of spaceborne LiDAR for environmental monitoring. In our study, GEDI’s data and findings served as a proof of concept for global laser altimetry, for example, showing how forest structure data can inform carbon markets or biodiversity models. We used GEDI as a qualitative benchmark to ensure EDGE’s forestry and climate-related benefit estimates were grounded in existing research (e.g., better above-ground biomass data → improved carbon accounting, which has long-term economic implications).

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NASA ICESat-2 (Ice, Cloud, and land Elevation Satellite-2)—NASA ICESat-2 Mission Page.

Description: A satellite mission launched in 2018 carrying a green laser altimeter that maps Earth’s surface, with a focus on the cryosphere (ice sheet elevations, sea-ice thickness) but also collecting global terrain and vegetation height along its ground tracks. ICESat-2 provides precise elevation change measurements critical for climate science (e.g., tracking sea-level rise, glacier melt). Its data have indirect economic value by improving climate forecasts and hazard planning (for instance, better elevation data for coastal areas improves flood risk models). We treated ICESat-2 as a contextual data source; while it doesn’t directly generate revenue, it proved the impact of precision altimetry for applications like water resource management and informed our assumptions about EDGE’s incremental value. In short, ICESat-2’s success in measuring Earth’s changing topography reinforced the feasibility and importance of missions like EDGE, and its datasets (available via NASA/NSIDC) were leveraged for validating our elevation models and cross-checking global terrain inputs.

From the details provided above, we projected annual economic benefits through 2035 as well as 2040 by aggregating AI-generated forecasts with established economic multipliers (e.g., adoption rate, discount scenarios) from historical benchmarks (Supplementary Data, Part II). Cumulative benefits and benefit–cost ratios (ROI) were derived through time-series integration of the ML-generated annual projections. This comprehensive methodology provided rapid, scenario-based economic assessments potentially suitable for early-phase NASA mission decision-making, demonstrating projected annual benefits of $3–$5B by 2035 and $8–$10B by 2040, with benefit–cost ratios potentially exceeding 50:1 over the mission lifecycle, as well as associated NPV benefits (i.e., ∼$33B at the 3% discount case). Figure 6 summarizes these results graphically.

Sensitivity Analysis of Methodology

As described in the section “Economic Impact Analysis Framework (SAMA Approach)” and via additional details in Supplementary Data (Part II), the stacked AI-modeling approach employed in this assessment of the economic impact potential of the EDGE mission can be evaluated using a sensitivity analysis to assess potential benefits as realistic “trends” ( Figs. 2 – 4 ). Here we utilize NPV and benefit–cost ratios (ROI) across critical variables such as “adoption rate” and “discount scenarios” as are typical in classical economic forecast analysis. Figures 2 – 4 illustrate the full range of these business metrics across standard economic modeling parameters with outcomes that we discuss in terms of specific business sectors in sections that follow. While preliminary, these sensitivity analyses support the outcomes ( Fig. 6 ) of the approach utilized for the first time for a new NASA mission (EDGE) under consideration for flight in the early 2030s via NASA’s competitive mission acquisition processes.

For NPV ( Fig. 4 , left side), the adoption rate and agriculture sector benefits are the most influential drivers, as each can swing NPV by roughly ±$3.7B (approximately ±11% of the $33B baseline). This reflects agriculture’s large share of total benefits and the fact that system-wide adoption rate affects all sectors simultaneously. The discount rate is the next biggest factor. Using 5% instead of 3% reduces NPV by about $6.7B (a ∼20% drop). In comparison, the risk and insurance and infrastructure benefit assumptions have more moderate impacts (each around ±$1.8B), and the digital/commercial benefits have the smallest effect (∼±$0.7B). Notably, in all cases the NPV remains strongly positive—even the lowest outcome (high discount + low adoption) remains in the ∼$26–$30B range. This trend is consistent and supports the overall conclusions of this pathfinding analysis of missions in the same complexity class as EDGE.

In terms of ROI ( Fig. 4 , right side), discount rate, adoption, and agriculture are again the dominant sources of variability. Raising the discount rate to 5% (while holding other factors at baseline levels) pulls ROI down from ∼74:1 to about 59:1. Conversely, faster adoption or maximizing agricultural sector gains can push ROI into the 80–90:1 range. Mission cost also matters. If the upfront mission cost were reduced to $400M, ROI would improve from 74:1 to roughly 83:1, whereas a hypothetical cost overrun to $500M would lower it to roughly 59:1. By contrast, uncertainties in the insurance and infrastructure benefits sectors move ROI by only a few points (roughly 70–78:1 across their low/high cases), and the digital services sector has a negligible effect (remaining around 72–76:1). Critically, none of these plausible variations threaten the EDGE mission’s strong economic payoff—even under the most conservative combination of assumptions, ROI remains on the order of 50–60:1. This “trend” appears to be consistent at least within the margins of error and the limitations of the stacked AI-based modeling approach adopted (i.e., please see Supplementary Data for more complete details).

In essence, Figure 4 treats one critical question: “Which individual input assumptions matter most for NPV and ROI, when varied one at a time around the baseline?” Figure 4 embraces the standard definition of a tornado chart, and thus we can conclude: adoption rate; discount rate; agriculture benefits; risk and insurance benefits; infrastructure benefits; digital benefits; mission cost (for ROI) are all valid entries within the Figure 4 framework. Most importantly, they are not being treated as the same type of variable, but instead as candidate drivers of uncertainty. Thus, Figure 4 presents a one-at-a-time sensitivity analysis. As such, each parameter is varied independently around the baseline while all other inputs are held constant. On this basis, we suggest that the conclusions from Figure 4 are reasonable for the EDGE mission impact in the relevant context described herein.

Discussion of Sensitivity Analysis

These projections ( Figs. 2 – 4 ) account for the full lifecycle costs of data processing, storage, and distribution infrastructure, as required for all openly competed NASA science missions. Based on NASA’s experience with similar types of active laser altimeter missions (i.e., ICESat-2, GEDI on ISS), we estimate annual data operations costs of approximately $10–$15 million, including ground-based processing systems, cloud storage infrastructure, and public data distribution through NASA’s Earthdata platform. While these ongoing costs reduce the net ROI and impact NPV, the impact is modest—even accounting for a decade of operations costs (perhaps ∼$100–$150M in total), the benefit–cost ratio remains strongly positive at over ∼40:1 by 2040 (and more likely >50:1). These values are consistent with parametric estimates used in classical Phase A studies of missions of similar complexity to EDGE with cost-capped “Phase E” flight operations that are historically less than ∼$15M/year after initial commissioning using current best practices at NASA.

Investing in the flight of EDGE is expected to yield a high NPV as well as appreciable benefit–cost ROI, both in direct economic savings/gains and in broader societal value on a global scale, as showcased graphically in Figure 6 . Studies of high-accuracy elevation data programs provide strong evidence of outsized benefits. For example, the USGS 3DEP, ⁷ which incrementally acquires airborne laser altimetry across the United States at “local scales”, has an estimated 5:1 ROI—about $690M in annual benefits nationwide from a ∼$36M/year program ⁷ to over $1B over a decade. Moreover, a comprehensive national (U.S.) assessment found that enhanced elevation data could ultimately generate $1–$13 billion in annual benefits (conservative vs. full potential) once fully implemented. ⁸ These figures indicate that every dollar invested in high-resolution elevation mapping can return many dollars in economic value through better-informed decisions and avoided costs. ⁹ Thus, such strategic knowledge of Earth surface elevations relates directly to key aspects of digital economic security. ¹

EDGE’s nearly global elevation measurement scope and factor-of-ten improvement in vertical elevation accuracy (∼0.10 m relative to an Earth Center of Mass reference frame such as International Terrestrial Reference Frame) would amplify these benefits significantly by 2035–2040. After initial data release (i.e., after 1 year of the baseline 2-year mapping mission), anticipated in ∼2032, adoption (and adoption rate) would grow across industries and a growing public-user community. By around 2035, our models predict potential annual benefits on the order of a few billion dollars per year, ramping up further by 2040 as usage matures ( Figs. 2 – 4 ).

For instance, one specific scenario analysis (illustrative only: Fig. 5 and Table 3 ) suggests that by ∼2035 the EDGE data might enable on the order of $3–$5 billion each year in economic value across key sectors, and by 2040 on the order of $8–$10B/year (once a commercial follow-on to EDGE is in play). This implies that within ∼7 years of launch, the cumulative benefits would far exceed the ∼$0.45B cost, yielding an ROI well into the double digits by 2035 (e.g., >10:1), and rising toward two orders of magnitude by the 2040s as the ecosystem reacts and expands ( Fig. 6 ). In other words, a modest upfront investment in EDGE could unlock tens of billions of dollars in value over the subsequent decade or two, representing a compelling case for business planners (see also Supplementary Data, Part II). This level of amplification of value is somewhat unprecedented ( Fig. 6 ) and could apply beyond Earth if “EDGE”-class surface elevation mapping were to be accomplished for the Moon and Mars as off-ramps (i.e., as part of NASA’s Moon to Mars initiative for human spaceflight that includes Artemis crewed missions to the Moon in the near term ¹¹ ).

Table 3.

EDGE Investment Versus Projected Returns (Scenario-Based Estimates as Illustrative)

Metric	Value/Outcome (∼2035)	Value/Outcome (2040)
Mission cost	∼$400–$450M (one-time, development + launch+ ops [2 years])	No additional (assumes one mission)
Mission data	Global DEM, ∼0.10 m vertical, ∼10–15 m horizontal, 2–5-year mapping coverage	(If extended mission or follow-ons, updated global coverage and better change detection)
Key beneficiary sectors	Re/Insurance, agriculture, infrastructure, others (global)	Same sectors, plus expanded commercial services
Annual economic benefit	∼$3–$5B/year (across sectors, moderate adoption scenario)	∼$8–$10B/year (full global adoption + commercial augmentation)
Cumulative benefits (since data release)	Roughly $10–$15B (assuming ramp-up from 2031 to 2035); see Figures 2 – 4	Tens of $ billions (est. $40–$50B total 2031–2040 in scenario)
Benefit–cost ROI	Exceeds 10:1 by 2035 (benefits ≫ cost within ∼5 years); see Figure 6	On the order of 50–100:1 by 2040 (runaway returns as ecosystem grows)

GENERAL BENEFIT–COST RATIO (ROI) ANALYSIS OUTCOMES

Overall ROI

Several factors drive this robust modeled ROI ( Figs. 2 – 4 , 6 ; see also Supplementary Data, Parts II, III). First, productivity gains and cost avoidance occur once decision-makers have more accurate elevation information. ¹⁰ Small efficiency improvements across huge sectors (agriculture, insurance, and infrastructure) compound into large dollar impacts. Second, risk reduction and loss mitigation yield direct savings such as better flood maps can prevent property loss or optimize insurance pricing. Third, EDGE’s open data can stimulate innovation and new services, creating indirect economic activity (jobs, new markets) that are not counted in direct benefits. Crucially, EDGE’s data would serve as critical input for AI/ML models and digital twins within these domains, enabling more advanced analytics and scenario forecasting—effectively AI-amplifying the raw data’s value. For example, ML algorithms could ingest the high-resolution EDGE DEMs along with satellite imagery and environmental data to predict crop yields or flood extents with higher levels of accuracy, further boosting the real-world utility and economic payoff of EDGE’s information. To quantify the ROI through 2035 and 2040, we consider major sectors individually (as they contribute to the total economic value) and then the aggregated impact.

Reinsurance and Risk Management (Flood and Disaster Risk)

High-precision terrain data directly improves flood and fire risk mapping, catastrophe modeling, and insurance pricing. Insurers and reinsurers often rely on relatively coarse, 12–30 m x,y global-scale DEMs (i.e., NASADEM, DLR’s TanDEM-X SAR), which leads to significant uncertainty as evidenced by events like Hurricane Harvey, where many flooded sites lay outside outdated 100-year flood zones. With EDGE, those maps can be rapidly redrawn with decimeter accuracy, narrowing this gap. Insurance companies can use EO data, including topography, to assess risk more accurately and even offer new parametric insurance products, ¹⁰ while reinsurers can pinpoint accumulations of risk with greater confidence. A 10× improvement in vertical accuracy means models can distinguish safe versus vulnerable properties on a very fine scale (e.g., which house lies just above a floodplain vs. just below it). This reduces uncertainty buffers in premiums and reinsurance rates, effectively right-pricing risk. In high-risk areas that were previously underestimated, premiums may rise (improving solvency and prompting mitigation), whereas low-risk areas might see lower premiums.

The net effect is a more efficient risk transfer market. For example, better elevation data for U.S. flood mapping was valued at roughly $295M/year in conservative benefits, ⁸ primarily from improved flood risk management (e.g., optimizing Federal Emergency Management Agency flood maps, insurance decisions). With a near-global DEM and 10–15× improved vertical accuracy, the benefit would be far larger—likely billions per year globally when considering to better prepare for disasters. Reinsurers explicitly note that current assessments using 2D maps are often inadequate and that moving to ∼10 cm vertical accuracy (achievable with drones, or in this case spaceborne laser altimetry) is a “next level” for identifying vulnerabilities. ¹² EDGE would effectively deliver this level of vertical precision everywhere, on demand, which represents a capability that could save significant portions of the ∼$10–$20B in annual global flood losses (insured and uninsured) through better planning. Even a few percent improvement in loss avoidance or pricing efficiency would translate to hundreds of millions saved per year. We project that by late 2035, widespread use of EDGE data in flood risk models could yield on the order of $1B/year in insurance/reinsurance industry value (through avoided losses and optimized premiums), growing to a few $B/year by 2040 as environmental change risks intensify and the data become integral to underwriting worldwide. These benefits also have public-sector analogs; governments can more cost-effectively invest in mitigation (knowing exactly which levee or drainage upgrade yields the best ROI in risk reduction), multiplying the societal benefit beyond the insurance ledger.

Agriculture and Precision Farming: Optimizing Every Acre with High-Resolution Elevation Data

Agriculture is widely expected to be the single largest beneficiary of EO data by ∼2030,10 and adding high-precision digital terrain models with change detection will further boost this sector. Modern precision agriculture already uses satellite imagery and GPS-guided equipment (UAS); however, microscale elevation maps enable new levels of optimization: improved irrigation design (gravity-fed flow models for each field), targeted erosion control (identifying exactly where soil is being lost after rains), and smarter planting decisions on variable terrain. Smoother, well-drained fields can significantly increase yield and reduce input costs. The 2011 National Enhanced Elevation Assessment (NEEA) ⁸ found U.S. precision farming could see $122M/year in immediate benefits from better elevation data (even with limited adoption), and potentially up to $2.0B/year if fully implemented^8,9 (e.g., if airborne laser altimetry were used nationwide to fine-tune farming practices to a greater extent). One major agribusiness estimated that just optimizing drainage could save ∼$5 per acre, which extrapolated to nearly $1.7B in savings if applied across all U.S. cropland. ⁸

Furthermore, global agriculture is many times larger than the United States alone. Countries in Asia, Africa, and Latin America have vast farmlands that currently lack high-quality elevation data. EDGE’s near-global ∼0.10 m vertical accuracy DEM could be a boon for regions that struggle with water management; for example, enabling precision leveling of rice paddies in South Asia, or guiding contour farming in Africa to reduce runoff. By 2035, assuming even moderate adoption rates (farmers using EDGE-based apps for irrigation planning, government soil conservation programs using it for guidance), the annual impact might reach ∼$1–$2B worldwide (through higher crop yields, water savings, and reduced fertilizer runoff). By 2040, with deeper integration and agritech firms leveraging AI models on EDGE data (for instance, to forecast yields more accurately field by field), this could grow to several $B/year in value. In humanitarian terms, better elevation data also means improved food security—farming is more resilient to droughts and floods when the land’s micro-topography is well understood. These benefits directly and indirectly flow into the broader global economy through more efficient resource use, creating cascading economic effects well beyond agricultural operations.

Infrastructure, Urban Planning, and Coastal Resilience: Building Smarter and Safer with Precise Topography

Infrastructure projects, from highways and rail lines to dams and coastal defenses, depend on vertically accurate elevation data for design and risk assessment. Having a high-resolution, up-to-date elevation map enables engineers and planners to identify hazards (flood zones, landslide-prone slopes, sinking land) with far greater confidence. For example, a coastal city planning new housing developments can overlay EDGE’s near-global DEM with sea-level rise projections to decide where floodwalls or elevated construction are needed. Planners can simulate 100-year flood inundation on a fine grid or pinpoint which neighborhoods are subsiding due to groundwater withdrawal. The economic benefits here come from avoiding costly mistakes and damages. If a city avoids building on a soon-to-be floodplain, it averts future disaster losses; if an engineer spots a subtle low point and raises a road by an extra 0.5 m, that road might stay passable during storms, saving future repair and disruption costs. Under 3DEP, U.S. infrastructure and construction management was estimated to gain about $206M/year in direct benefits^8,9 from better elevation data (e.g., more efficient project planning, reduced survey costs), with potential benefits up to $942M/year ⁸ when accounting for unquantified uses (e.g., many U.S. states could not fully estimate benefits, suggesting the real impact could be much larger). ⁸ With EDGE’s data openly available, even smaller cities and developing countries’ planners who may not be able to afford detailed surveys for every local project can participate in environmental change resilience and infrastructure safety.

By late 2035, as government agencies and private companies worldwide incorporate EDGE data into their workflows, we anticipate on the order of $0.5–$1B/year in efficiency gains and risk reduction (e.g., more cost-effective flood control investments, smarter land-use zoning). By 2040, this could rise, especially as environmental change adaptation efforts ramp up, such as massive coastal defense projects that heavily rely on precise elevation models to allocate billions of dollars in the right places. Coastal resilience in particular benefits: a vertical accuracy boost means flood models can be calibrated to decimeter level, improving predictions of storm surge impact and coastal erosion. Reinsurers and governments can then closely price and prepare for these risks. In addition, land subsidence monitoring, which is a growing issue for many megacities, will become more actionable. Combining EDGE’s baseline with new Interferometric Synthetic Aperture Radar deformation data (e.g., NASA’s NISAR²) authorities can quantify subsidence rates down to centimeters and take early action, such as by restricting groundwater pumping or reinforcing foundations, before major damage occurs. These preventative measures, enabled by better data, have enormous potential ROI. In summary, the infrastructure sector sees both immediate cost savings in planning and long-term savings by avoiding damage—a dynamic where data-driven planning today averts huge expenditures tomorrow.

Digital Economy and Commercial Expansion: Unlocking Indirect Value and New Markets

Beyond the direct sectoral impacts, EDGE’s data would generate substantial indirect value through commercialization and the digital services economy in space, ⁴ which goes back to the Landsat program starting in 1972. While EDGE differs from Landsat in important ways—surface elevation data require more technical expertise to utilize than intuitive optical imagery—the mission’s potential impact is validated by substantial demonstrated user demand. Unlike the direct 2D imaging services market that emerged from optical EO (e.g., Landsat), EDGE’s commercial applications would likely focus on value-added analytics rather than competing elevation datasets. However, the EDGE mission team has received dozens of letters of support ¹ from potential users across government agencies, conservation organizations, and private companies that demonstrate clear pathways from data to decision-making. Users ranging from the U.S. Geological Survey (“capabilities will impact strategic planning regarding risk reduction and tactical decision-making on active wildfire incidents”) to private sector firms calling EDGE data “an essential backbone” for the nature market’s projected growth to $37.55 billion by 2032 have identified specific applications where EDGE’s high vertical precision elevation data would directly improve their operations. Organizations see EDGE as “a leap forward in how rural communities can access and apply Earth science to solve real-world problems,” while international bodies like UNESCO’s International Hydrologic Program view it as “a critical asset to the global water community.” This already documented user demand suggests that while EDGE may follow a different commercialization pathway than classical 2D optical imaging satellites (i.e., from Landsat to Maxar WorldView) by serving as foundational infrastructure for specialized analytics rather than spawning direct competitors. In EDGE’s case the economic benefits would be realized through immediate integration into existing workflows across multiple high-value sectors.

By around 2040, some models also predict the emergence of new commercial laser altimeters piggybacking on EDGE’s success in the early 2030s, analogous to how dozens of commercial imaging satellites now augment government-funded missions including those utilizing imaging radar (e.g., Umbra, Capella, ICEYE).

Finally, the indirect economic value for this sector comes from new products and services built on EDGE data: advanced GIS software, insurance tech platforms, precision agriculture equipment tuned to the data, AR/VR applications using real-world 3D maps, and so forth. Many of these are part of the expanding digital economy, which includes software and analytics leveraging the new class of high vertical accuracy DEM. By ∼2040, a robust public–private data infrastructure could emerge where NASA’s baseline (EDGE) is periodically refreshed or enhanced by commercial sources, and value-added resellers are packaging the data into sector-specific solutions. This expansion could easily represent several additional billions per year in economic activity (such as revenues of geospatial firms and efficiency gains for consumers of those services). Perhaps the most important “digital” value is that open EDGE data feed into countless AI/ML models—whether it is a global climate model’s land surface or a local autonomous vehicle’s navigation system—improving their ultimate accuracy and hence economic utility. That improvement is challenging to put in dollars, but it ultimately yields better outcomes (e.g., safer self-driving cars thanks to better maps, or more accurate environmental change impact predictions informing policy). All told, the indirect and induced economic benefits from EDGE-enabled technology could rival or exceed the direct benefits in the long run, much as the downstream industries of EO now far exceed the initial government investment. Table 3 outlines the ensemble of several potential benefits (see Supplementary Data, Parts II, III).

It should be emphasized that these estimates are illustrative, modeled on industry research and analogous programs with reasonable assumptions (see also Supplementary Data, Parts II, III). Actual outcomes could vary, but the trend suggests that EDGE would pay for itself, potentially many times over ( Fig. 6 ). Even under conservative assumptions (limited early use cases), ROI is high (e.g., U.S.-only benefits already justify the cost ⁷ ). Under optimistic scenarios (broad global use + innovation), EDGE’s economic impact could be well beyond these initial model-based projections. Thus, accounting for uncertainty, agriculture and insurance remain the dominant beneficiaries of EDGE by 2040, with infrastructure and other digital services also contributing significantly.

BEYOND EARTH: EXTENDING EDGE’S STRATEGY TO MOON AND MARS

The same strategic rationale for situational awareness surface elevation mapping orbiters applies not only to Earth ( Fig. 1 ) but to other planetary bodies where scientific exploration and nascent commercial activity are growing rapidly.^6,13,14 We extend the EDGE case to the Moon and Mars, where NASA’s human exploration plans are now a major U.S. civilian space priority.^11,14 Deploying orbiters with high-resolution topographic mapping and monitoring capabilities around the Moon and Mars can catalyze more cost-effective and efficient human exploration ¹¹ as well as economic development (i.e., as in recently released U.S. National Space Policy via the President’s Executive Order dated December 18, 2025), ¹⁴ just as EDGE would for Earth.^1,2

Moon (Artemis)

The Moon is on the cusp of increased activity with NASA’s Artemis program and commercial endeavors (landing services, proposed mining of water ice at the poles, He ³ ). A lunar situational-awareness orbiter equipped with a swath mapping laser altimeter as well as 2D imaging could provide continuously updated high-resolution topography, terrain change detection, and hazard mapping for the lunar surface tied to needs, goals, and objectives summarized for NASA’s Moon-to-Mars Exploration program architecture. ¹¹ This would have immediate exploration/scientific benefits, including (for example) mapping polar crater interiors in detail to locate water ice or monitoring the structure of lava tubes/caves as potential habitats (or safe havens) for human explorers. It also has clear commercial and operational value; for instance, supporting safe operations of lunar landers and robotic rovers by providing a constantly refreshed 3D map of the terrain (crucial for navigation in the permanently shadowed polar regions near planned Artemis basecamp facilities at the lunar south pole). NASA’s Lunar Reconnaissance Orbiter (LRO) already mapped the Moon’s topography (i.e., the Lunar Orbiter Laser Altimeter instrument ¹⁵ ) but at ∼10–15 m spatial resolution and <1.5 m vertical accuracy. A lunar orbital variant of EDGE could improve this to ≤0.20 m vertical accuracy quickly, given the global surface area of the Moon is about that of Africa.

A next-generation mission employing a swath imaging laser altimeter (i.e., as anticipated for Artemis support via tech/data gaps in the NASA Architecture Definition Document ¹¹ ) could provide much finer detail and broader coverage ¹⁵ especially in the challenging solar illumination environment of the south polar region, where the Artemis basecamp is currently planned to be located. This would help in site planning (civil engineering) for lunar basecamp(s), optimally choosing flat, stable areas, assessing line-of-sight for communications, and locating resources. This would essentially provide lunar human explorers with the “Google Maps” equivalent that we take for granted for Earth. The presence of such orbital infrastructure could also spur commercial investment: companies may feel more confident building lunar infrastructure if they have reliable real-time situational data (for example, a service might emerge to monitor regolith dust movement or track spacecraft across the surface). In analogy to Earth, a government-led lunar 3D mapping orbiter can pave the way for commercial lunar satellites that offer specialized services (i.e., imagine privatized lunar satellites for dust and space weather monitoring or high-resolution optical mappers for specific regions of interest like resource extraction sites at 5–10 cm deterministic scales). The strategic payoff is like that of EDGE: a relatively small investment in 3D mapping yields an amplifying effect, enhancing the safety and efficiency of all other activities on the Moon. High-precision lunar DEMs tied to the Moon’s geodetic network would also anchor other datasets (imagery, gravity, compositional mapping) and enable AI-driven lunar terrain analytics (e.g., automated hazard detection algorithms to assist lander guidance, which is effectively an extension of terrain-relative navigation but continuously improved from orbit).

Mars

Mars, while likely farther off in terms of human sustained presence at the surface, already has a bustling robotic program (NASA’s Mars Exploration Program operative since 2000) and future ambitions for both science and potentially resource utilization (via identification and accessibility). A Mars situational-awareness topographic mapping orbiter could similarly revolutionize our ability to monitor and utilize the Martian environment and accessible resources, including subsurface water ice deposits. Scientifically, Mars has been mapped extensively (via MOLA on Mars Global Surveyor gave a global 3D map at ∼300 m horizontal postings at <1.5 m vertical accuracy ⁵ ), but finer-scale topography (submeter-level vertical precision) is only known for limited areas (i.e., from rover-based data or high-resolution orbital stereo images). An EDGE-like orbiter around Mars could produce a high-resolution global DEM (i.e., submeter vertical accuracy of ∼0.20 m at spatial mapping scales of 7–15 m), aiding geologists in identifying small-scale landforms, active processes (sand dune migration, slope avalanches), or potential sinkholes and caves. For future exploration, especially if we anticipate eventual human missions or advanced robotic networks, situational awareness is key, as described in NASA’s latest Mars-to-Mars Architecture Definition Document. ¹¹ Consider a future Mars human outpost or in situ resource utilization surface operation site; an orbital swath mapping laser altimeter could track dust storm fronts in 3D, map out safe routes for rovers between habitats and resources (including power stations), or even detect critical relief changes in the landscape (e.g., dust deposition after a global dust storm that might impede equipment or influence power delivery systems).

The economic benefit angle on Mars is thus longer term, but one can envision that once Mars exploration becomes partially commercial (i.e., via companies prospecting for water ice or rare minerals for in situ resource utilization with commercially delivered payloads), having a reliable orbital data service (mapping in 3D and at hyper-resolution, telecommunications, weather, space weather) is part of the critical infrastructure that enables that “beyond Earth” economy. Just as EO satellites drive trillion-dollar decisions on Earth, ¹⁰ a Mars orbiter providing continuous data would be a strategic asset, initially for agencies (to maximize science return and mission safety) and eventually for private stakeholders. The approach described herein (building off EDGE) implies that by investing in strategic orbital mapping capabilities around these bodies, NASA can accelerate the timeline in which exploration and associated scientific discovery translate into practical utilization. On Mars, a high vertical precision exploration-oriented DEM could, for example, help pinpoint flat areas rich in specific minerals for landing ISRU-oriented robots or identify ideal locations for solar array power farms (accounting for terrain shading) or for positioning critical fission power stations. It could also support Mars domain awareness in a safety sense; if multiple robotic rovers from different nations/companies are operating, an orbiter can track their 3D positions and help avoid conflicts or accidents (an analog to air traffic control, but for surface rovers and even airborne drones). While Mars does not yet have commerce, these capabilities shorten the gap between exploration and exploitation by providing the foundational data layer.

In summary, extending EDGE’s measurement advancement strategy to the Moon and Mars (in a digital economic sense and connected to NASA’s priorities for human exploration^11,14) suggests that wherever we anticipate significant activity (human exploration or commercial), an early investment in orbital 3D situational awareness yields outsized returns (see Figs. 2 – 4 ). It creates a virtuous cycle: better data → better decisions → more successful missions → more investment in those destinations. Just as EDGE would likely pay back its cost manyfold in Earth’s digital economy (and associated economic security metrics), a lunar or Martian equivalent could pay back in terms of mission risk reduction, scientific breakthroughs, and enabling future commercial endeavors (which themselves could become economically significant in a couple of decades). In fact, NASA is already investigating technologies like scanning and swath “imaging” laser altimetry for lunar mapping,^6,16 recognizing that high-resolution topography will be vital for Artemis basecamp planning and beyond. Recent NASA verification tests of hazards descent imaging laser altimeter instruments (i.e., NASA Hazard Detection Lidar ¹⁷ ) are well suited for enabling safe planetary landings and ready for flight demonstration at the Moon. By the 2035–2040 timeframe, we could envision a “Moon 3D Mapper” orbiter providing near real-time 3D geodetic-quality maps to support Artemis astronauts and associated robots, and perhaps a next-generation Mars exploration orbiter doing the same (via EDGE-class swath mapping geodetic laser altimetry) for the first human surface missions or advanced precursory robotic rovers and cargo/ISRU landers. These topographic mapping assets would echo EDGE’s Earth impact by improving 3D situational awareness to such a degree that they become catalysts for all other investments in those realms.

CONCLUSION AND NEXT STEPS

The proposed EDGE mission exemplifies a high-leverage investment; it tackles a fundamental data gap (ultra-high vertical accuracy for Earth surface elevations and above-ground biomass, global elevation knowledge, ice cover and dynamics) with far-reaching applications.^2,14 The trend-oriented stacked AI modeling-based analysis approach (Supplementary Table S1) presented herein indicates strong ROI and NPV ( Fig. 6 ), with billions per year in benefits across reinsurance, agriculture, infrastructure, and other sectors by the 2030s, vastly outweighing the upfront cost (Supplementary Data). Moreover, EDGE’s true legacy may be its catalytic effect: jump-starting a commercial ecosystem in EO 3D mapping, perhaps as Landsat catalyzed the $700B EO industry (in 2D optical imaging) that is operative today.^8,10 For decision-makers considering this class of mission, one trend is evident: EDGE is not a short-term science-only experiment but rather represents a strategic infrastructure investment for the planet relevant to the digital economy with strong ties to economic security metrics including NPV. It would deliver critical situational awareness in an era of rapid environmental change and growing resource challenges, essentially providing an information platform upon which innovations and cost savings can build. Through scenario modeling and stacked AI-assisted forecasts, we see that by ∼2040, EDGE could enable on the order of $50B+ in cumulative economic value, touching industries that together comprise a significant fraction of U.S. GDP (agriculture, finance, construction, tech). The EDGE mission concept is a compelling case where a government-funded science-oriented mission could enhance economic development and resilience.

Finally, when we extend our digital economic analyses beyond low Earth orbit, the same paradigm offers a roadmap for maximizing returns on our upcoming human exploration investments at the Moon and Mars. A situational-awareness swath imaging laser altimeter orbiter at the Moon or Mars ¹¹ could similarly multiply the impact of every dollar spent on those frontiers while improving safety, guiding resource use, and encouraging private sector participation. In essence, strategic mapping missions are reasonable force multipliers for both applied science and commerce, whether in Earth’s economy or the emerging deep space economy (i.e., at the Moon and/or Mars). EDGE represents but one example of a template going forward: invest in strategic knowledge, share it widely, and reap the amplified benefits for years to come with business benefits for decades.^2,14,16

AUTHORS’ CONTRIBUTIONS

Acknowledged contributions using CRediT format are as follows: J.B.G. (conceptualization, methodology, writing, supervision, funding acquisition); P.M.-S. (conceptualization, methodology, AI/ML software, writing, funding acquisition); R.M.P. (methodology, writing, supervision); H.A.F. (supervision, writing, data curation, funding acquisition); J.D.A. (writing, data curation); J.B.B. (conceptualization, writing, data curation); S.B.L. (conceptualization, writing, data curation). All work is original. ORCID ID for senior author (JBG): 0000-0003-1606-5645.