EarthPT: a time series foundation model for Earth Observation

Autor:	Smith, Michael J., Fleming, Luke, Geach, James E.
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Physics - Geophysics
Druh dokumentu:	Working Paper
Popis:	We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. We demonstrate that EarthPT is an effective forecaster that can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 -> 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar `Large Observation Models.' Comment: 7 pages, 4 figures, accepted to NeurIPS CCAI workshop at https://www.climatechange.ai/papers/neurips2023/2 . Code available at https://github.com/aspiaspace/EarthPT
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.07207 Zobrazit plný text záznamu View this record from Arxiv