From Pixels to Policy

How Geospatial Data and AI can Propel the SDGs Forward

6 min readJun 3, 2025

When Peter Wilczynski stepped onto the red carpet at TED2025, he did not rely on hyperbole and buzzwords. Instead, the Maxar Intelligence executive quietly unboxed a living, 3-D replica of Earth — complete with streaming sensor feeds and the ability to test “what-if” scenarios in minutes. The demonstration offered a glimpse of a future where policy makers can preview the consequences of a zoning change or plan for wildfire response before either hits the real world.

For the Sustainable Development Goals (SDGs), technology like this, which combines geospatial data and artificial intelligence, demonstrates we are moving from rear-view-mirror reporting to a heads-up display of global progress. This promises to improve planning, program efficiency, and ultimately, impact

Maxar’s work is hardly a one-off. A new ecosystem of satellites, cloud platforms, and machine-learning models is turning the planet into the most measured — and potentially the most manageable — asset we have.

Building on this single demonstration, here are four ways how geospatial data fused with AI is already redefining “evidence-based” development policy — and what leaders can do next.

For more on TED2025, be sure to read our full coverage of the event and what it means for the SDGs.

1. Instant Insights for Action

Satellite photographs of our planet are not new — they have been around since the 1950s. What is new is the ability to capture high-resolution imagery that can be updated daily, or even hourly. Combined with AI analytics, this capability provides insights that were previously out of reach. Consider these technologies:

BlackSky Gen-3 streams 35 cm imagery and AI change-detection alerts within hours. While costly, it can allow for quickly flagging illegal mining operations, wildfire progress and tracking marine traffic. With timely information, regulators can plan and respond to emerging situations.
Environmental monitoring platforms such as Global Fishing Watch and Global Forestry Watch use timely geospatial data to track and report on environmental issues, offering real-time insight into progress and early warnings of emerging problems.
Flood Insights from ICEYE delivers building-level flood-depth maps within hours of peak water, guiding evacuation routes and insurance triage for resilient-city and climate-action targets.

Why it matters: These services compress the discovery-to-response window from months to days, allowing rapid action on emerging issues.

Screen capture from globalforestwatch.com

2. Measuring What Matters

For years, satellite imagery was more illustration than evidence. Machine-learning analytics now translate those pixels into frequently updated indicators that drop straight into SDG dashboards.

Climate TRACE estimates greenhouse-gas emissions from 660 million individual assets with roughly a 60-day lag, letting regulators see which refineries, power plants, or wildfires are driving climate targets.
Google Earth Engine combines a multi-petabyte public archive with cloud processing so analysts can map cropland expansion, surface-water loss, or urban-heat islands at national scale in hours, supporting sustainable development targets.
Digital Earth Africa turns decades of Landsat and Sentinel imagery into analysis-ready datasets that can be used for enabling climate action, improving food security, addressing the impacts of urbanization, and promoting sustainable resource use.

Why it matters: Earth-observation proxies can refresh hard-to-measure SDG indicators quarterly instead of once a decade, giving governments the lead time to adjust policies before progress veers off course.

3. Inside the UN’s Geospatial Toolbox

UN agencies are no longer waiting for member-state spreadsheets to catch up. They are including geospatial and AI analysis directly into their operational dashboards — and turning those insights into policy action.

WHO — Multi-Source Collaborative Surveillance (MSCS) overlays dengue case reports with rainfall, temperature, and mosquito data in Central Java. Early-2025 warnings prompted targeted larvicide campaigns that kept case counts below the previous year’s peak — showing how SDG 3 gains can be both data-driven and preventative.
WFP — HungerMap LIVE ingests satellite rain layers, conflict events, and phone-survey data to “nowcast” food insecurity across 90 countries, directing anticipatory cash transfers and supply-chain logistics in line with SDG 2.
NASA / USAID — SERVIR co-develops Earth-observation apps with regional hubs; the newest center in Costa Rica is building drought-risk dashboards for Central American ministries, embedding global science in local decision cycles.

Why it matters: When agencies refresh health, hunger, and hazard indicators in near real time, they can trigger resources — or policy pivots — before setbacks become crises, accelerating SDG progress where it counts most.

4. Geo-AI for the Front Line

These new technologies are not just for national agencies and corporations flush with cash — as geospatial data becomes more ubiquitous and AI more accessible, even local groups can leverage their power.

Humanitarian OpenStreetMap Team (HOT) trains volunteers to fly drones and trace roads, clinics, and flood shelters; in Sierra Leone’s 2025 Freetown project, fresh aerial imagery was able to be used for logistics tools within days of capture.
DroneDeploy assists with the December 2024 EF-4 tornado near Omaha, where local teams used a live-mapping feed to survey 300 km² of damage in 48 hours, guiding relief efforts and FEMA support.
Esri Nonprofit Program + mobile GeoAI offers steeply discounted ArcGIS licenses and deep-learning models that can run on a phone; field teams snap a photo and update 3-D city twins or extract building footprints without a procurement cycle.

Why it matters: When frontline actors can collect, analyze, and publish geospatial intelligence themselves, the data gap between global dashboards and village-level reality closes. That empowers faster targeting of resources, sharper accountability, and SDG progress that is visible — literally — on the ground.

Conclusion

Taken together, real-time imagery, AI-powered metrics, agency-scale dashboards, and off-the-shelf field tools form a comprehensive SDG toolkit: satellites detect change within hours, machine learning converts those pixels into indicators, UN systems respond with policy and resources, and community teams close the loop through on-the-ground action. The result is a development cycle that moves at the speed of events rather than reports.

This space is also ripe for social impact startups and entrepreneurs to be involved in a rapidly evolving tech sector while also contributing to global good.

If planners and implementers embed these geo-AI feeds into routine budgets, policies, and training — treating data as core infrastructure rather than a pilot project — the SDGs shift from distant targets to daily operating instructions, actionable at every level of development.

SDGCounting is a program of StartingUpGood and tracks the progress of counting and measuring the success of the SDGs. Follow us on social media:

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Note: Generative AI tools were used in the creation of this article to assist with research, summarization, and editing.

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