AI Geo Navigators Private Limited

The first Global Dialogue on AI Governance succeeds if it produces: 1. Actionable governance frameworks with teeth Not principles mechanisms. Concrete models for transboundary AI incident response, liability allocation for cross-border harms, and enforceable standards for frontier model evaluation. Success means countries leave with templates they can adapt, not aspirational declarations. 2. Credible capacity-building commitments Binding pledges from high-capacity states and multilaterals to fund compute access, technical training, and regulatory expertise in the Global South. Track record matters: attach funding amounts, timelines, and accountability metrics. Vague "partnerships" are failure. 3. Interoperability on risk assessment Divergent AI regulations are inevitable, but fragmented risk frameworks create compliance chaos and stall deployment. Align on shared evaluation protocols for model capabilities (dual-use potential, bias, robustness) so a safety case in one jurisdiction translates elsewhere. Singapore's AI Verify and the EU AI Act evaluation standards could converge here. 4. A functioning coordination body Establish a lean, technically competent secretariat not a bloated UN agency. Mandate: maintain a live registry of national AI governance approaches, coordinate incident information-sharing, and convene technical working groups on emerging risks. If it takes 18 months to issue a report, it has failed. 5. Progress on open vs. closed models Resolving the open-source safety debate is unrealistic, but clarity on guardrails is not. Define acceptable use policies for open-weight models, liability for misuse, and red lines for capability disclosure. If we exit with the same stalemate, the Dialogue was performative. Success is not consensus. It is infrastructure: shared tools, funded programs, and institutions that reduce coordination costs for the hard governance work ahead.

• Safe, secure and trustworthy AI
• AI capacity-building
• Transparency, accountability, and human oversight
• Protection and promotion of human rights

Safe, secure and trustworthy AI: Geospatial AI models drive high-stakes decisions-flood forecasts, wildfire response, crop insurance payouts. Without validated safety standards, errors cascade into humanitarian disasters. We need adversarial robustness testing (models manipulated through spoofed sensor data), evaluation protocols for geographic bias, and alignment between model outputs and ground truth across diverse ecosystems. AI capacity-building: Most countries lack infrastructure to process satellite archives or train geospatial models. This creates dependencies: they import black-box solutions from foreign vendors, cannot audit performance locally, and miss opportunities to apply AI to context-specific problems like informal settlement mapping or locust swarm tracking. Capacity-building means compute subsidies, open dataset curation, technical training, and regulatory expertise to govern geospatial AI independently. Transparency, accountability, and human oversight: Geospatial AI opacity is rampant. Models trained on proprietary satellite data, undisclosed performance metrics, and opaque failure modes. We need enforceable disclosure: training dataset geography, validation environments, known biases (does your deforestation model fail in cloud-heavy regions?), and liability when predictions cause harm. Human oversight matters-automated land use classification should not override local knowledge without review. Open-source software, open data and open AI models: Geospatial AI thrives on open data (Landsat, Sentinel, MODIS) and open-source tools (GDAL, TensorFlow, PyTorch). Closing this ecosystem fragments progress. Prioritise because open-weight foundation models (Prithvi, Satlas) democratise access, enable local fine-tuning, and reduce vendor lock-in. Balance with safety: open models need acceptable use policies and red lines for dual-use capabilities. Why not others? Human rights and interoperability matter but are addressed through the four above. Safe AI protects rights; transparency enables interoperability.

1. Environmental and compute costs of geospatial AI Training satellite imagery foundation models requires enormous compute. Cloud providers and research labs rarely disclose energy consumption, water use, or carbon footprint. Governance should mandate lifecycle emissions reporting for geospatial AI, especially for models retrained frequently on updated imagery. Without this, climate-focused AI projects undermine climate goals. 2. Data sovereignty and satellite imagery governance High-resolution commercial satellites can image any territory. Countries have limited control over how their land is monitored, analyzed, or monetised. Governance must address: consent frameworks for sub-meter imagery, restrictions on selling analysis of sensitive infrastructure, and equitable access to imagery of a country's own territory. Current systems privilege wealthy buyers. 3. Geospatial AI for dual-use and conflict The same models that map refugee camps for humanitarian response can target military strikes. Governance needs guardrails: prohibited use cases for certain capabilities, disclosure requirements for dual-use models, and export controls on high-resolution analysis tools. AI-driven geospatial intelligence is already a conflict accelerant. 4. Validation data scarcity and geographic bias Geospatial AI models fail in underrepresented geographies-tropical forests, arid regions, informal settlements-because validation datasets cluster in North America and Europe. Governance should fund ground-truth data collection in undersampled regions, mandate geographic performance disclosure, and penalise models deployed outside their validated range. 5. Concentration of satellite and compute infrastructure A few firms control commercial satellite constellations (Maxar, Planet) and cloud compute (AWS, Google, Microsoft). This centralisation limits competition, entrenches dependencies, and gives private actors outsized influence. Governance must address antitrust concerns, public compute investment, and interoperability mandates to prevent vendor lock-in.

Safe, secure and trustworthy AI - Challenges: Pakistan and the broader South Asia region deploy geospatial AI for flood forecasting, agricultural monitoring, and urban planning without local validation infrastructure. Models trained on temperate datasets fail during monsoons—cloud cover breaks optical satellite analysis, and flood extent models underestimate inundation in flat deltaic plains. No regulatory framework requires geographic performance disclosure, so agencies adopt foreign models blindly. Recent Indus Basin flood predictions relied on global datasets that missed localized drainage patterns, delaying evacuations. AI capacity-building - Gaps: Regional institutions lack compute to process Sentinel-2 archives or fine-tune foundation models on local landscapes. Universities train students on global datasets (ImageNet, COCO) with no exposure to Earth observation pipelines. Governments cannot audit vendor-supplied crop yield models or validate land cover classifications independently. This creates dependencies on foreign consultancies and prevents deployment of AI for region-specific problems like rice disease detection or groundwater depletion mapping. Transparency and accountability - Failures: Commercial providers sell deforestation alerts, crop insurance products, and infrastructure risk assessments with zero disclosure of training data geography, model limitations, or failure modes. When predictions fail—incorrect drought severity triggering premature water rationing—no liability framework exists. Farmers and local governments bear the costs. Open-source/open data - Opportunities: Open Sentinel and Landsat archives enable cost-effective monitoring, but processing requires cloud compute most institutions cannot afford. Open-weight geospatial foundation models (Prithvi, Satlas) offer fine-tuning potential, but lack of local expertise prevents adoption. Governance supporting compute subsidies and technical training would unlock regional AI development—Pakistan could build flood models validated on Indus hydrology, Bangladesh could map cyclone vulnerability in the Sundarbans, India could monitor Himalayan glacier retreat. Bottom line: Governance gaps strand the region as a consumer of unsuitable imported models rather than a developer of locally validated, climate-adapted geospatial AI.

1. Harmonize geospatial AI evaluation standards across jurisdictions Current fragmentation means a satellite-based flood model validated in Europe cannot be certified for use in Asia without complete retesting. The Dialogue should coordinate shared evaluation protocols: geographic performance benchmarks, adversarial robustness tests for satellite imagery, and bias assessment frameworks. This reduces duplication and accelerates cross-border deployment for climate adaptation and disaster response.2. Broker compute-sharing agreements for Earth observation AI High-capacity states and cloud providers should commit funded compute access for processing satellite archives in low-capacity regions. The Dialogue can structure these agreements: allocate GPU hours for Sentinel-2 processing, provide technical support for model training, and establish accountability metrics. This moves beyond vague partnerships to binding resource transfers.3. Coordinate incident response for AI-driven geospatial failures When flawed wildfire extent predictions trigger unnecessary mass evacuations or incorrect crop yield forecasts cause market panic, no international mechanism exists to investigate, share findings, or update models. The Dialogue should establish a rapid response protocol: incident reporting, root cause analysis, and coordinated model updates across affected regions.4. Mediate dual-use geospatial AI governance Satellite imagery analysis enables both humanitarian response and military targeting. Countries need forums to negotiate acceptable use policies, export controls for high-resolution analysis tools, and confidence-building measures to prevent AI-driven conflicts. The Dialogue can convene technical working groups to draft these frameworks where existing arms control mechanisms lack AI expertise.5. Facilitate South-South geospatial AI collaboration Pakistan's experience with monsoon flood modeling benefits Bangladesh; Kenya's drought forecasting methods apply to Ethiopia. The Dialogue should connect regional centers of excellence, fund knowledge exchanges, and maintain registries of validated models and datasets. Current cooperation is ad hoc; structured networks would accelerate capability development where commercial vendors ignore market opportunities.

1. Group on Earth Observations (GEO) and GEOSS GEO already coordinates satellite data sharing and interoperability across 100+ countries. The Dialogue should integrate AI governance into GEO's work: add model evaluation standards to GEOSS data-sharing principles, fund AI capacity-building through GEO's training programs, and use GEO's regional networks to deploy validated geospatial models. GEO has infrastructure; the Dialogue adds AI-specific governance. 2. Copernicus and ESA open data programs Europe's Sentinel satellites provide free global coverage. The Dialogue should expand on this by funding compute infrastructure to process these archives in the Global South and supporting development of open-weight foundation models trained on Copernicus data. Copernicus solved the data access problem; compute access remains the bottleneck. 3. Digital Earth Africa and similar regional initiatives This partnership provides cloud-based satellite analysis infrastructure for African nations. The Dialogue should replicate this model in South Asia, Southeast Asia, and Latin America—funded compute platforms, curated datasets, pre-trained models, and technical training. Connect these regional hubs into a global network for knowledge exchange. 4. WMO AI for weather and climate initiatives The World Meteorological Organization is developing AI standards for numerical weather prediction. The Dialogue should coordinate with WMO to extend these standards to satellite-based climate monitoring, ensure interoperability between weather AI and Earth observation AI, and avoid duplicating evaluation frameworks. Added value the Dialogue brings: Cross-domain integration: Existing initiatives are sector-specific (weather, agriculture, disaster response). The Dialogue can harmonize governance across applications. Binding commitments: GEO and similar bodies issue guidelines; the Dialogue can secure funded pledges with accountability metrics. Dual-use mediation: No existing Earth observation forum addresses military applications. The Dialogue fills this gap.

Technical practitioners lead working groups Governance discussions dominated by policy generalists produce unimplementable frameworks. Assign geospatial AI practitioners, remote sensing scientists, and infrastructure engineers to lead thematic working groups on model validation, capacity-building, and dual-use risks. Their output: technical specifications governments can adopt, not aspirational principles.Regional blocs submit validation requirements Countries group by shared geographic/climatic conditions (monsoon Asia, Sahel, Small Island Developing States) to define model performance standards relevant to their environments. A South Asian bloc specifies cloud-penetrating radar requirements; an African bloc defines arid-region training data minimums. This produces actionable interoperability frameworks instead of one-size-fits-all standards.Open-source communities demonstrate alternatives Reserve sessions for teams building open geospatial AI infrastructure (Radiant Earth, Development Seed, university labs) to showcase working systems. Demonstrations beat white papers—show a fine-tuned foundation model detecting informal settlements or a compute-efficient pipeline for processing satellite archives on limited hardware.Private sector presents under constraints Commercial satellite and AI providers participate but cannot dominate. Require disclosure: training data sources, geographic performance metrics, dual-use safeguards. No marketing presentations—only technical specifications and known failure modes.Structured format: problem → solution → commitment Three-part sessions: (1) Technical practitioners present a specific governance gap with data (e.g., flood models failing in deltaic regions), (2) Working groups propose solutions with implementation details (validation protocols, compute subsidies, dataset curation), (3) States and multilaterals make binding commitments with funding amounts and delivery timelines. Record commitments publicly and track progress.Avoid: panels, keynotes, broad consultations No multi-stakeholder panels discussing "AI for good." No ministerial keynotes. No open comment periods that generate thousands of pages nobody reads. Small working groups with technical mandates produce governance infrastructure. Large forums produce declarations.

Technical practitioners lead working groups Governance discussions dominated by policy generalists produce unimplementable frameworks. Assign geospatial AI practitioners, remote sensing scientists, and infrastructure engineers to lead thematic working groups on model validation, capacity-building, and dual-use risks. Their output: technical specifications governments can adopt, not aspirational principles. Regional blocs submit validation requirements Countries group by shared geographic/climatic conditions (monsoon Asia, Sahel, Small Island Developing States) to define model performance standards relevant to their environments. A South Asian bloc specifies cloud-penetrating radar requirements; an African bloc defines arid-region training data minimums. This produces actionable interoperability frameworks instead of one-size-fits-all standards. Open-source communities demonstrate alternatives Reserve sessions for teams building open geospatial AI infrastructure (Radiant Earth, Development Seed, university labs) to showcase working systems. Demonstrations beat white papers—show a fine-tuned foundation model detecting informal settlements or a compute-efficient pipeline for processing satellite archives on limited hardware. Private sector presents under constraints Commercial satellite and AI providers participate but cannot dominate. Require disclosure: training data sources, geographic performance metrics, dual-use safeguards. No marketing presentations—only technical specifications and known failure modes. Structured format: problem → solution → commitment Three-part sessions: (1) Technical practitioners present a specific governance gap with data (e.g., flood models failing in deltaic regions), (2) Working groups propose solutions with implementation details (validation protocols, compute subsidies, dataset curation), (3) States and multilaterals make binding commitments with funding amounts and delivery timelines. Record commitments publicly and track progress. Avoid: panels, keynotes, broad consultations No multi-stakeholder panels discussing "AI for good." No ministerial keynotes. No open comment periods that generate thousands of pages nobody reads. Small working groups with technical mandates produce governance infrastructure. Large forums produce declarations. Question 16: Underrepresented Voices (300 words max) Geographic information practitioners in the Global South National mapping agencies, meteorological departments, agricultural extension services, and disaster management authorities use geospatial AI daily but rarely participate in governance discussions. They understand model failures intimately—where predictions break, which populations get overlooked, what ground-truth data is missing. Include them through: regional technical convenings with travel funding, asynchronous input mechanisms (recorded technical briefings, written submissions with translation support), and reserved seats in working groups. Indigenous communities and local land managers AI-driven land use classification, forest monitoring, and resource mapping directly affect Indigenous territories, yet these communities are excluded from model design and governance. Their knowledge identifies failure modes invisible to satellite analysts—seasonal migration patterns, customary land boundaries, sacred sites. Include through: community liaisons embedded in technical working groups, protocols requiring free prior informed consent for AI deployment on Indigenous lands, and funding for community-led validation of geospatial AI outputs. Small state technical personnel SIDS and least developed countries send diplomats to AI governance forums, not the engineers running their climate monitoring systems. These practitioners know compute constraints, dataset gaps, and dependency risks firsthand. Include through: practitioner-specific tracks parallel to ministerial sessions, remote participation

Live model evaluation challenges Teams bring geospatial AI models and test them on held-out datasets from underrepresented geographies (Sahel croplands, Himalayan terrain, Pacific atolls). Performance metrics displayed publicly. Failures drive governance discussions—if all flood models break in flat deltaic regions, that becomes a priority working group. Makes abstract governance concrete and exposes vendor claims to scrutiny. Red-teaming sessions for dual-use risks Practitioners demonstrate how benign geospatial AI capabilities enable harmful applications: turning building detection into military targeting, converting crop monitoring into smuggling route identification, using displacement tracking for surveillance. Then design guardrails in real-time. Adversarial thinking prevents governance gaps. Asynchronous technical annotation of draft standards Post draft governance frameworks (model validation protocols, disclosure requirements) on collaborative platforms. Practitioners worldwide annotate specific clauses with implementation feasibility, cost estimates, and failure scenarios. Example: "This compute requirement excludes 80% of national meteorological services—alternative: tiered standards by resource availability." Surfaces practical barriers before adoption. Regional validation dataset sprints Fund distributed teams to curate ground-truth datasets for underrepresented geographies during the Dialogue. Bangladesh team labels monsoon flood extents, Sahel team maps pastoral land use, Pacific team annotates cyclone damage. Datasets released publicly, demonstrating capacity-building in action. Produces governance-enabling infrastructure, not just documents. Mandatory cost-benefit analysis for proposed standards Every governance proposal must include: implementation cost estimates by country income level, required compute and expertise, timeline to operationalise, and populations excluded if not implemented. Prevents high-income countries from imposing standards low-capacity states cannot meet. Avoid: hackathons (produce prototypes, not governance), VR experiences (gimmicks), art installations (commentary, not solutions), consensus-building retreats (slow and lowest-common-denominator). Prioritise formats that produce working infrastructure and enforceable commitments.

Singapore AI Verify - Practical validation framework Provides testable standards for AI systems with open-source toolkits. Companies run evaluations on their models against defined metrics (fairness, robustness, transparency) and receive verifiable reports. Applied to geospatial AI: forces disclosure of geographic performance variation and training data coverage. Strength: moves beyond principles to measurable compliance. Weakness: voluntary adoption limits impact. EU AI Act evaluation protocols - Geographic performance mandates High-risk AI systems must document performance across demographic and geographic segments. For geospatial AI, this means reporting model accuracy by climate zone, terrain type, and data availability. Prevents deploying temperate-trained models in tropical regions without disclosure. Strength: enforceable requirements with penalties. Weakness: compliance costs exclude small providers. Radiant Earth MLHub - Open training dataset registry Curates georeferenced training datasets with quality metadata, licensing clarity, and geographic coverage documentation. Enables researchers in low-capacity countries to build models without proprietary data access. Includes African cropland maps, disaster damage labels, and informal settlement annotations-datasets commercial providers ignore. Strength: reduces barriers to entry. Weakness: limited compute infrastructure support for actually using the data. NOAA/NASA compute subsidies for climate research Provides cloud credits to researchers and agencies for processing satellite archives. Applied broadly: enables meteorological departments in Pakistan, Kenya, and Philippines to develop locally validated flood and drought models instead of relying on global products. Strength: addresses capacity gap directly. Weakness: bureaucratic application processes and short funding cycles. Sentinel Hub - Transparent satellite data processing Commercial platform with clear API documentation, disclosed preprocessing steps, and reproducible analysis pipelines. Users know exactly how raw satellite data becomes analysis-ready imagery. Contrast with proprietary providers selling "vegetation health indices" with undisclosed algorithms. Strength: enables independent validation. Weakness: still requires technical expertise to use. What works: Measurable standards, funded compute access, open datasets, transparent processing. What fails: Voluntary principles, unfunded mandates, closed ecosystems.