The Missing Piece: Standardising for AI-ready Earth Observation Datasets

Cesar Aybar, Julio Contreras, Chen Ma, Oscar J. Pellicer-Valero, Gonzalo Mateo-García, Luis Gómez-Chova, Gustau Camps-Valls, Nils Lehmann, Mikolaj Czerkawski, David Montero, Miguel D. Mahecha, Ingrid Aybar

Keywords: deep-learning, specification, data model, GDAL, Apache Parquet

TL;DR: We introduce TACO: a comprehensive specification that defines a formal data model, a cloud-optimized on-disk layout, and an API for creating and accessing AI-ready EO datasets.

Abstract: Geospatial communities have long relied on standardised formats for raster and vector data, enabling interoperability, stable tool development, and long-term data preservation. In contrast, Artificial Intelligence (AI)-ready datasets, particularly those derived from Earth Observation (EO), lack equivalent conventions. As a result, data providers often adopt ad hoc file structures, loosely defined formats, and inconsistent semantic encodings. This fragmentation hinders interoperability, complicates reuse, and undermines reproducibility. We argue that the lack of a standard format represents a structural bottleneck to scalable scientific progress, especially in the era of foundation models, where diverse datasets must be combined for effective training and performance evaluation in downstream tasks. To address this, we introduce TACO: a comprehensive specification that defines a formal data model, a cloud-optimized on-disk layout, and an API for creating and accessing AI-ready EO datasets.

Submission Number: 29