DATA FLOWS
AI in Data Flows
How artificial intelligence is transforming sustainability evidence –
from satellite monitoring to automated verification.
In 30 Seconds
AI is revolutionising sustainability evidence across three pillars: how we measure and verify outcomes (MRV), how we trace products through supply chains (traceability), and how we extract and validate data from diverse sources (data quality).
MRV & Verification
Evidence for environmental outcomes.
- • Satellite imagery analysis
- • Biomass estimation
- • Deforestation detection
- • Ground-truth fusion
Traceability
Evidence for sourcing claims.
- • EUDR compliance verification
- • Deforestation-free mapping
- • Product origin tracking
- • Supplier risk screening
Data Quality
Evidence infrastructure.
- • LLM data extraction
- • Gap detection
- • Automated validation
- • Multi-source fusion
The transformation: What once required months of field surveys and manual verification can now be monitored continuously from space. But satellite data without ground-truth is just pixels – AI's real value is fusing multiple evidence streams.
Where This Fits
This page explores data infrastructure applications of AI in detail. It complements the broader AI in Sustainability overview by focusing specifically on evidence and verification use cases.
AI in Sustainability
Broad overview of AI across all sustainability domains – layers, flows, cross-cutting systems.
Landscape view
This Page
Data infrastructure AI – MRV, traceability, verification, data quality.
Evidence focus
Data Flows
The broader vertical – MRV systems, traceability, digital infrastructure.
Parent section
Symmetry note: This page mirrors AI in Sustainable Finance – Capital Flows has its AI deep-dive, and now Data Flows does too. Together they show how AI transforms both the money side and the evidence side of sustainability.
AI for MRV & Verification
Measurement, Reporting, Verification. AI transforms how we detect, quantify, and verify environmental outcomes at scale.
Satellite Imagery Analysis
The foundation of modern MRV. AI processes petabytes of satellite imagery to detect changes in forest cover, land use, and ecosystem condition – often within hours of acquisition.
Key Capabilities
- • Change detection for deforestation, degradation, fire
- • Land use classification at scale
- • Vegetation indices (NDVI, EVI) analysis
- • Cloud removal and gap-filling algorithms
- • Multi-spectral fusion from different sensors
Key Platforms
- Planet Labs – Daily global imagery, deforestation alerts
- Maxar – High-resolution optical for precise mapping
- EarthDaily – 22-band multispectral, carbon applications
- Sentinel (Copernicus) – Open data, radar capabilities
- GEDI – NASA lidar for forest structure
2025-26 developments: Space-based AI processing is emerging (ESA's ASCEND program), enabling real-time analysis without Earth-bound transmission. Hourly deforestation updates are becoming standard for high-risk regions.
Biomass & Carbon Estimation
Translating satellite observations into carbon stock estimates. The bridge between remote sensing and carbon credit quantification.
Estimation Methods
- • Lidar-based: Direct canopy height measurement
- • SAR (radar): Penetrates clouds, measures structure
- • Optical + ML: Spectral signatures to biomass models
- • Allometric models: Height/crown to carbon conversion
- • Temporal analysis: Growth rate estimation over time
Key Players
- Chloris Geospatial – Aboveground biomass mapping
- Pachama – Forest carbon AI, project verification
- Kanop – Forest analytics, EU carbon farming
- NCX – US forest carbon platform
- Climate TRACE – Open emissions monitoring (352M assets)
Accuracy challenge: Satellite-derived biomass estimates still carry 20-40% uncertainty in many ecosystems. Ground-truth calibration remains essential – AI helps optimise where to sample, but can't eliminate the need for field data.
Deforestation & Degradation Detection
Near-real-time forest loss alerts. The backbone of EUDR compliance and deforestation-free supply chain verification.
Detection Approaches
- • Alert systems: GLAD, RADD, Brazil DETER
- • Change classification: Loss vs. degradation vs. seasonal
- • Driver attribution: Agriculture, logging, fire, mining
- • Predictive risk: Where deforestation likely to occur
- • Regrowth tracking: Detecting forest recovery
Platform Examples
- Global Forest Watch – Open deforestation monitoring
- Trase – Supply chain + deforestation linkage
- MapBiomas – Brazil/Amazon annual mapping
- JRC TMF – EU tropical moist forest monitoring
- Orbital AI – Hourly deforestation updates
EUDR context: The EU Deforestation Regulation requires geolocation of sourcing plots and verification against the December 31, 2020 cutoff date. AI-powered historical land use analysis is essential for compliance – large operators face December 30, 2026 deadline.
Ground-Truth & Multi-Source Fusion
Combining satellite data with field measurements, IoT sensors, and community reports. The integration challenge that determines MRV credibility.
Fusion Approaches
- • Calibration: Adjusting models with field data
- • Validation: Independent accuracy assessment
- • Uncertainty quantification: Confidence intervals
- • Active learning: AI-guided sampling strategies
- • Crowdsourced data: Community-based monitoring
Data Sources
- • Forest inventory: Plot-level measurements
- • eDNA: Biodiversity from environmental samples
- • Acoustic sensors: Soundscape biodiversity
- • Camera traps: Wildlife AI identification
- • IoT sensors: Soil, water, microclimate
Key insight: The most credible MRV systems aren't purely satellite-based – they fuse multiple evidence streams. AI's role is integrating disparate data types into coherent, uncertainty-quantified estimates.
AI for Traceability
Supply chain visibility at scale. AI transforms how companies verify product origins and sourcing compliance.
EUDR Compliance Verification
The EU Deforestation Regulation creates unprecedented demand for AI-powered traceability. Seven commodities (cocoa, coffee, palm, soy, cattle, wood, rubber) must prove deforestation-free origins.
Compliance Requirements
- • Geolocation: GPS coordinates for sourcing plots
- • Cutoff date: No deforestation after Dec 31, 2020
- • Due diligence: Risk assessment and mitigation
- • Documentation: EU TRACES system registration
- • Country risk: Standard vs enhanced due diligence
AI Solutions
- Rainforest Alliance AI – Farm mapping, forest layer analysis
- Iceberg Data Lab – Historical deforestation, TRACES integration
- Satelligence – Real-time risk alerts
- Sourcemap – Supply chain mapping + satellite
- Earthworm – Starling satellite monitoring
Timeline update (Jan 2026): EU delayed enforcement by 12 months. Large operators face December 30, 2026 deadline; SMEs until June 30, 2027. Simplification review expected by April 2026 – but the direction is clear.
Deforestation-Free Verification
Beyond compliance – demonstrating genuine deforestation-free sourcing for corporate commitments and consumer trust.
Verification Elements
- • Historical analysis: Land use before/after cutoff
- • Ongoing monitoring: Continuous satellite alerts
- • Risk scoring: Supplier-level deforestation risk
- • Certification mapping: FSC, RSPO, Rainforest Alliance coverage
- • Agroforestry distinction: AI differentiating farms from forests
Platform Capabilities
- • Plot-level: Individual farm boundary verification
- • Landscape-level: Jurisdictional deforestation context
- • Supply chain: Tracing commodities to verified plots
- • Documentation: Audit-ready evidence packages
- • API integration: Connect to procurement systems
Product Origin & Chain of Custody
Tracking products from farm to final product. AI enables verification at scale across complex, multi-tier supply chains.
Tracking Methods
- • Segregation: Physical separation, identity preserved
- • Mass balance: Volume accounting through chain
- • Digital twins: Virtual product tracking
- • Isotope analysis: Geographic fingerprinting
- • Blockchain: Immutable transaction records
AI Applications
- • Anomaly detection: Identifying suspicious shipments
- • Pattern matching: Linking purchases to verified sources
- • Document analysis: Automated certificate verification
- • Risk prediction: Forecasting supply chain disruption
- • Supplier scoring: Aggregating compliance indicators
Supplier Risk Screening
Identifying high-risk suppliers before they become compliance problems. AI-powered due diligence at portfolio scale.
Risk Factors
- • Geographic: Proximity to protected areas, deforestation fronts
- • Historical: Past deforestation on or near supplier land
- • Certification: Presence/absence of credible standards
- • Governance: Country-level rule of law, enforcement
- • News/controversy: Media mentions, NGO reports
Screening Outputs
- • Risk scores: Tiered classification for prioritisation
- • Alert triggers: Real-time notification of changes
- • Due diligence reports: Automated assessment summaries
- • Mitigation recommendations: Suggested actions per supplier
- • Portfolio dashboards: Aggregate exposure views
AI for Data Quality & Infrastructure
The foundation of credible disclosure. AI transforms how sustainability data is extracted, validated, and integrated.
LLM-Powered Data Extraction
Large Language Models unlock sustainability data trapped in PDFs, reports, and unstructured documents. The bridge between narrative disclosure and structured data.
Extraction Capabilities
- • Scope 1-3 emissions: From sustainability reports
- • CSRD datapoints: Mapping narratives to ESRS requirements
- • Policy analysis: Extracting commitments and targets
- • Standard mapping: Cross-walking GRI, SASB, ISSB
- • Controversy monitoring: News and NGO report scanning
Implementation Patterns
- RAG architecture – Retrieval over document corpus
- Multi-agent systems – Specialised extractors (emissions, social, governance)
- Structured output – JSON/schema-conformant extraction
- Confidence scoring – Uncertainty quantification
- Human-in-loop – Expert validation for edge cases
2025-26 reality: Multi-agent ESG systems like ESGAgent achieve 84% accuracy on benchmark tasks, outperforming standalone LLMs. Production platforms (Ai-ESG, EcoRatings) report 30-90% efficiency gains for CSRD data collection.
Gap Detection & Quality Assessment
Identifying what's missing, inconsistent, or unreliable in sustainability data. Essential for audit-readiness and disclosure quality.
Detection Types
- • Completeness: Missing required disclosures
- • Consistency: Year-on-year anomalies
- • Comparability: Peer benchmarking outliers
- • Methodology: Calculation approach validation
- • Evidence: Claims without supporting data
Quality Metrics
- • Coverage score: % of required disclosures present
- • Confidence level: Data source reliability
- • Recency: Data freshness (reported vs current year)
- • Granularity: Level of detail (consolidated vs breakdown)
- • Audit trail: Traceability to source documents
Multi-Source Data Fusion
Combining data from satellites, IoT sensors, corporate disclosures, and third-party sources into coherent sustainability intelligence.
Fusion Challenges
- • Temporal alignment: Different reporting periods
- • Spatial matching: Asset location reconciliation
- • Methodology harmonisation: Different calculation approaches
- • Conflict resolution: When sources disagree
- • Uncertainty propagation: Combining confidence levels
Platform Examples
- Climate TRACE – 352M assets, open emissions database
- OS-Climate – Open source physical risk data
- NGFS scenarios – Climate scenario integration
- CDP – Disclosure aggregation platform
- SBTi Portal – Target tracking and validation
Interoperability trend: The EU Digital Product Passport and sustainability data spaces (e.g., Catena-X for automotive) are creating standardised data exchange. AI's role shifts from extraction to orchestration.
Automated Validation & QA
Real-time data quality assurance. Catching errors before they propagate through reports and disclosures.
Validation Rules
- • Range checks: Values within plausible bounds
- • Consistency rules: Related fields align
- • Trend analysis: Changes flagged if unusual
- • Cross-reference: Matches external databases
- • Unit conversion: Standardised to common metrics
Assurance Support
- • Pre-audit screening: Identify issues before review
- • Evidence packages: Automated documentation
- • Audit trail: Full data lineage
- • Exception reports: Prioritised issue lists
- • Benchmark context: Peer comparison for auditors
The Technology Landscape
AI tools for sustainability data span multiple categories. Here's the landscape by function:
Satellite & Remote Sensing
- Planet Labs – Daily global imagery
- Maxar – High-resolution optical
- EarthDaily – 22-band multispectral
- Sentinel (ESA) – Open data, SAR
- Umbra – SAR for forest monitoring
Established, commoditising
Forest & Carbon MRV
- Pachama – Forest carbon verification
- Sylvera – Carbon credit ratings
- Chloris Geospatial – Biomass mapping
- Kanop – EU carbon farming
- NCX – US forest carbon
Maturing rapidly
Deforestation Monitoring
- Global Forest Watch – Open platform
- Trase – Supply chain + deforestation
- MapBiomas – Brazil mapping
- Orbital AI – Hourly updates
- JRC TMF – EU tropical forests
Production-ready
EUDR & Traceability
- Rainforest Alliance – AI remote sensing
- Iceberg Data Lab – TRACES integration
- Satelligence – Real-time alerts
- Sourcemap – Supply chain mapping
- Earthworm (Starling) – Satellite monitoring
Hot market, deadline-driven
Emissions & Climate Data
- Climate TRACE – 352M asset inventory
- Copernicus CO2M – EU satellite mission
- GHGSat – Methane detection
- Carbon Mapper – Point source emissions
- OS-Climate – Open source tools
Rapidly expanding
LLM & ESG Extraction
- ESGAgent – Multi-agent ESG system
- Ai-ESG – 20+ specialised agents
- EcoRatings – Agentic ESG automation
- Clarity AI – LLM-enhanced analytics
- Custom RAG – Enterprise deployments
Emerging, high potential
Maturity Assessment
AI maturity varies significantly across data infrastructure applications. Some are production-ready; others remain experimental.
| Application | Maturity | Data Quality | Market Adoption |
|---|---|---|---|
| Deforestation detection | Production | High | Widespread |
| Land use classification | Production | High | Established |
| Biomass/carbon estimation | Maturing | Medium | Growing rapidly |
| EUDR compliance | Maturing | Medium-High | Deadline-driven surge |
| Supply chain traceability | Maturing | Medium | Commodity-specific |
| LLM data extraction | Emerging | Variable | Early enterprise |
| Multi-source fusion | Emerging | Low-Medium | Research & pilots |
| Biodiversity monitoring (eDNA/acoustic) | Emerging | Low | Early adopters |
Investment implication: Production-ready tools (deforestation, land use) deliver immediate compliance value. Emerging tools (LLM extraction, biodiversity monitoring) offer strategic differentiation for early adopters willing to invest in data quality validation.
Challenges & Limitations
AI in sustainability data is powerful but not without significant challenges. Understanding these is essential for effective deployment.
Ground-Truth Scarcity
Satellite AI is only as good as the field data that calibrates it. In many critical ecosystems, ground-truth data is sparse, outdated, or non-existent.
- • Tropical forest biomass: field plots cover <0.1% of area
- • Peatland carbon: highly variable, poorly mapped
- • Blue carbon (mangroves, seagrass): limited measurement infrastructure
- • Degraded lands: hard to distinguish from natural variation
Model Uncertainty & Transparency
AI-derived estimates often come without adequate uncertainty quantification. Users treat point estimates as precise when they may carry 20-50% error margins.
- • Confidence intervals rarely provided
- • Model assumptions often opaque
- • Training data biases not disclosed
- • Methodology comparisons difficult
Data Sovereignty & Access
Who owns the data? Who can access it? Critical questions as AI systems aggregate sensitive information about land, communities, and supply chains.
- • Indigenous land data: consent and benefit-sharing
- • Supplier data: competitive sensitivity
- • Satellite imagery: varying access rights
- • National data: sovereignty concerns
Gaming & Manipulation
As AI systems become gatekeepers for compliance and credit verification, incentives to game them increase. The same technology that detects fraud can be studied to evade it.
- • Deforestation displacement: clearing just outside monitored areas
- • Temporary compliance: timing activities to avoid detection
- • Document fabrication: AI-generated certificates
- • Selective disclosure: reporting only what AI doesn't catch
Infrastructure Gaps
AI requires connectivity, compute, and capacity that may not exist where sustainability data is most needed.
- • Rural connectivity: limited internet for real-time monitoring
- • Local capacity: expertise to interpret and act on AI outputs
- • Cost barriers: smallholders can't afford premium platforms
- • Language: tools often English-only
The Pandion View
AI is genuinely transforming sustainability data infrastructure. The scale and speed gains are real. But evidence quality – not just quantity – determines credibility.
Where AI Delivers Real Value
- • Scale: Monitoring millions of hectares that would be impossible manually
- • Speed: Near-real-time alerts for deforestation, fire, land use change
- • Coverage: Filling data gaps in hard-to-reach regions
- • Consistency: Standardised methodology across geographies
- • Cost: Reduced field survey requirements (though not eliminated)
Where Human Judgment Remains Essential
- • Ground-truthing: Field validation that AI can't replace
- • Context: Local knowledge about land use, tenure, community dynamics
- • Interpretation: What do anomalies actually mean?
- • Quality assurance: Verifying AI outputs before critical decisions
- • Stakeholder engagement: Building trust with communities and suppliers
Our Approach
As a hybrid professional combining sustainability expertise with AI capability, we help clients build credible evidence systems:
- • Tool selection: Which platforms fit your data needs and verification standards?
- • Integration: How do AI outputs connect to disclosure and decision processes?
- • Quality protocols: What validation ensures AI-derived data is credible?
- • Capability building: How do teams develop fluency in data infrastructure?
The organisations that build credibility will be those that combine AI scale with rigorous validation. Evidence without verification is just noise – that's the synthesis we help clients achieve.
Where To Go Next
AI in Sustainability
Broader view of AI across the entire sustainability domain
MRV Systems
The broader MRV infrastructure beyond AI applications
AI in Sustainable Finance
The companion page: AI transforming capital flows
Carbon Markets
Where MRV data meets credit verification
AI Capability
What Pandion offers: AI strategy, implementation, capability building