Neuroplasticity-Inspired Foundation Models for Earth Observation

The advent of foundation models has already begun reshaping this landscape, offering promising avenues for interpreting large-scale EO datasets across varied environments. However, the siloed nature of existing approaches—where separate models are trained for optical, radar, hyperspectral, or multispectral data—has resulted in fragmentation and inefficiency. Traditional models are often rigid, requiring significant computational and human effort to adapt to new data sources, tasks, or sensor modalities.

To overcome these limitations, we spotlight a ground-breaking development that aligns directly with ThinkingEarth’s mission: the Dynamic One-For-All (DOFA) model, a neural plasticity-inspired foundation model designed to unify multimodal EO analysis.

DOFA: Inspired by the Brain, Built for the Planet

DOFA draws inspiration from neuroplasticity, the brain’s remarkable ability to rewire itself in response to new stimuli. Just as the human brain adapts to changing environments by altering synaptic pathways, DOFA dynamically adapts its internal architecture to process diverse EO inputs—including data it has never seen before.

At its core, DOFA employs a hypernetwork that uses wavelength as a unifying representation across different sensors. This hypernetwork dynamically generates network weights conditioned on the central wavelength of each spectral band, effectively tailoring the model’s parameters to the modality of the input. This flexible approach allows DOFA to function as a single, generalised model across optical, SAR, multispectral, hyperspectral, and aerial imagery—all while maintaining or exceeding task-specific performance.

What makes DOFA transformative for platforms like ThinkingEarth is its ability to:

• Generalise across sensors: DOFA has demonstrated strong performance on 14 EO tasks spanning 5 distinct sensors, including sensors not encountered during training.

• Reduce model complexity: A shared vision backbone eliminates the need for separate encoders per modality, streamlining deployment across operational pipelines.

• Adapt in real-time: Like the brain’s synaptic remodelling, DOFA can dynamically adjust to new modalities with minimal fine-tuning, enabling faster adaptation for use cases such as wildfire monitoring, urban development tracking, and biodiversity assessment.

The model is trained using a masked image modelling strategy and enhanced via distillation from large-scale visual encoders (e.g., ImageNet-pretrained models), ensuring robust performance even with limited labelled data.

DOFA’s architecture is uniquely aligned with ThinkingEarth’s goals of delivering scalable, data-agnostic geospatial intelligence. In our efforts to support energy communities, climate resilience, and land-use planning, the need to harmonise disparate EO sources is paramount. DOFA allows us to move away from fragmented workflows toward a single adaptive model capable of:

• Monitoring solar potential in cities by combining optical and aerial data

• Mapping crop stress by fusing radar and hyperspectral time-series

• Enhancing early warning systems with rapid sensor-agnostic analysis

• Reducing computational load and human supervision during deployment

By integrating DOFA or DOFA-inspired architectures, ThinkingEarth can deliver real-time insights across sectors, regardless of the input modality. This represents a critical step forward in democratising access to high-quality EO analytics—particularly for regions or institutions that may lack specialised processing capabilities.

Looking Forward

The development of multimodal foundation models like DOFA marks a paradigm shift for EO, transitioning from sensor-specific pipelines to adaptive, general-purpose intelligence. For ThinkingEarth, this is more than a technical innovation—it is a strategic enabler for global sustainability and resilience.

As we continue to experiment with and deploy such models, we believe the future of EO lies not in isolated breakthroughs, but in neuroplastic, unifying systems that learn, adapt, and grow—just like the Earth they are built to understand.