Enterprise AI Analysis

LEPA: A Novel Architecture for Equivariant Embeddings

To overcome the limitations of traditional interpolation, we introduce the Learned Equivariance-Predicting Architecture (LEPA). Unlike methods that attempt to average embedding vectors, LEPA conditions a specialized predictor on explicit geometric augmentation parameters (such as rotation, scaling, and translation) to directly predict the transformed embedding. This approach is inspired by Image World Models and adapts the Joint-Embedding Predictive Architecture (I-JEPA) framework. During training, LEPA's predictor learns to not only 'inpaint' missing blocks (as in I-JEPA) but also to effectively 'reverse' or apply geometric transformations within the embedding space itself. This process ensures that the encoder learns an approximately equivariant representation, allowing for precise geometric adjustments without the need for computationally intensive re-encoding of the original satellite imagery. We also incorporate novel centered positional encodings to better reflect changed patch positions under transformation.

Quantifiable Improvements in Equivariance and Representation Quality

Our comprehensive evaluation demonstrates the significant advantages of LEPA:

• Superior Geometric Equivariance: Standard interpolation methods for patch embeddings yield a Mean Reciprocal Rank (MRR) below 0.2. LEPA, however, dramatically increases the MRR to over 0.8, proving its ability to accurately adjust embeddings geometrically. Further fine-tuning of the predictor pushes this even higher.
• Robustness: Experiments confirm that traditional interpolation and downsampling methods fundamentally break down when applied to patch embeddings, highlighting the necessity of a learned approach.
• Competitive Representation Quality: Despite its focus on equivariance, our I-JEPA models (trained on ImageNet-1k or HLS data) perform competitively on the PANGAEA benchmark for semantic segmentation, even with architectural modifications like centered positional encodings.
• Architectural Insights: We found that adding a CLS token can improve semantic segmentation for ImageNet models but not consistently for HLS data, suggesting dataset-specific effects on global information aggregation. Modified positional encodings, while intuitive, had no measurable impact on embedding quality in our I-JEPA models.

Enabling Scalable Earth Observation Solutions

LEPA represents a crucial advancement for enterprise Earth observation applications. By allowing for direct geometric manipulation of precomputed embeddings, it unlocks several key benefits:

• Operational Efficiency: Eliminates the need for costly and time-consuming re-encoding of satellite data when aligning with diverse user-defined areas of interest, significantly accelerating analysis workflows.
• Enhanced Data Utility: Maximizes the value of geospatial foundation models by making their powerful embeddings flexible and adaptable to real-world geometric variations, rotations, and scales without loss of fidelity.
• Scalability: Supports large-scale Earth observation initiatives by reducing computational bottlenecks and data transfer requirements associated with handling raw imagery.
• Future Directions: Further research will explore additional foundation models for equivariance, optimize predictor complexity for inference cost reduction, enhance inductive biases with advanced positional encodings (e.g., ALiBi, ROPE), and investigate the impact of classification-oriented vs. general-purpose RGB datasets on embedding noise.