S2Vec: Self-Supervised Geospatial Embeddings
This addresses the need for effective geospatial representations in AI applications, though it appears incremental by combining existing techniques like S2 cells and masked autoencoding.
The paper tackles the problem of learning scalable general-purpose representations of the built environment for geospatial AI by introducing S2Vec, a self-supervised framework that yields task-agnostic embeddings, showing competitive performance against state-of-the-art image-based embeddings on three large-scale socioeconomic prediction tasks.
Scalable general-purpose representations of the built environment are crucial for geospatial artificial intelligence applications. This paper introduces S2Vec, a novel self-supervised framework for learning such geospatial embeddings. S2Vec uses the S2 Geometry library to partition large areas into discrete S2 cells, rasterizes built environment feature vectors within cells as images, and applies masked autoencoding on these rasterized images to encode the feature vectors. This approach yields task-agnostic embeddings that capture local feature characteristics and broader spatial relationships. We evaluate S2Vec on three large-scale socioeconomic prediction tasks, showing its competitive performance against state-of-the-art image-based embeddings. We also explore the benefits of combining S2Vec embeddings with image-based embeddings downstream, showing that such multimodal fusion can often improve performance. Our results highlight how S2Vec can learn effective general-purpose geospatial representations and how it can complement other data modalities in geospatial artificial intelligence.