Consistent Flood Mapping and Forecasting with ThinkingEarth

As climate change intensifies extreme rainfall and accelerates sea-level rise, the need for accurate, reliable, and scalable flood mapping and forecasting has never been more urgent.

Despite major advances in Earth Observation (EO) and machine learning, long-term forecasting of flood dynamics remains a major challenge. Conventional models often overlook the physical laws that govern water movement and rarely quantify uncertainties, making it difficult for decision-makers to rely on predictions during high-risk events.
ThinkingEarth addresses these limitations by integrating deep learning for flood analysis and multi-modal EO data into a unified platform for flood intelligence.

Multi-Source Data Integration for Robust Flood Detection

Flood mapping requires data that is both frequent and resilient to atmospheric conditions. ThinkingEarth integrates multiple satellite modalities—optical, multispectral, and synthetic aperture radar (SAR)—to provide stable observations even during extreme weather or heavy cloud cover.

• Sentinel-1 SAR and Sentinel-2 for optical context:
Drawing on approaches used in datasets like Kuro Siwo, ThinkingEarth enhances contrastive learning and masked-image modelling across Sentinel-1, -2, and -3 data. This enables accurate separation of floodwater from permanent water bodies while minimising false positives.

• Expanding global coverage and resolution:
Leveraging global spatiotemporal datasets, ThinkingEarth improves flood extent mapping in underrepresented regions, ensuring inclusiveness and global representativeness.
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Self-Supervised Learning to Reduce Annotation Burden

High-quality flood datasets often require extensive manual interpretation, as seen in collections like CAU-Flood. ThinkingEarth reduces this bottleneck by applying self-supervised graph learning:

• automatically refining flood masks,
• enhancing consistency across scenes and sensors,
• preserving high accuracy with minimal manual input.

This dramatically accelerates dataset development and system scalability.
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Deep Learning for Spatiotemporal Flood Analysis

Floods evolve quickly, making it essential to capture patterns across both space and time. ThinkingEarth uses deep neural architectures capable of learning:

• local spatial features (water texture, land cover differences),
• temporal evolution across consecutive satellite passes,
• wider environmental context (soil moisture, topography, precipitation).

This enables ThinkingEarth to support use cases ranging from post-disaster mapping to long-term flood susceptibility modelling and climate resilience assessments.
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Near-Real-Time Insights for Emergency Response

By leveraging near-real-time EO streams, ThinkingEarth provides rapid situational awareness during active flood events. When combined with non-EO datasets—such as hydrological models, river gauge data, or climate indicators—the platform can:

• generate operational flood extent maps,
• track inundation progression,
• support emergency teams with actionable intelligence.

This unified view bridges the gap between satellite observation, physical modelling, and on-the-ground decision-making.

A Physically Grounded, Data-Rich Future for Flood Monitoring

ThinkingEarth’s integration of AI, multi-source satellite data, and large-scale self-supervised learning offers a transformative approach to flood mapping. It enables:

• higher accuracy under clouded or complex conditions,
• global scalability with reduced annotation effort,
• reliable uncertainty-aware forecasting,
• and timely insights during disaster events.

By merging advanced modelling with diverse and global EO datasets, ThinkingEarth is shaping a new generation of flood intelligence aimed at safeguarding communities, infrastructure, and ecosystems.