GeoState: Pretrained Geospatial Embeddings Are Forecastable Where the Land Changes

Geospatial foundation models are evaluated almost exclusively as static feature extractors. We ask whether their representations also behave as predictive states: can next year’s embedding of a location be forecast from its recent history? We study annual 64-dimensional AlphaEarth Foundations embeddings over five Indonesian regions. We forecast the residual $Z_{t+1}-Z_t$ with extrapolation, linear, MLP, GRU and Transformer forecasters under matched budgets and five seeds. We evaluate them under temporal and geographic holdouts with spatial-block bootstrap intervals. On the primary 2024 test, aggregate gains over persistence are modest and no architecture clearly wins. Stratifying by change explains why. Learned forecasters lose slightly to persistence at stable locations but gain substantially at dynamic ones, including when change is identified from information available at forecast time. The apparent geographic attenuation disappears once differences in dynamism are accounted for. A non-foundation control shows that aggregate error can mislead. PCA, autoencoder and quarterly representations of Sentinel-2 are easier to forecast overall, but their skill is not consistently concentrated at land-cover change and brings little downstream benefit. AlphaEarth instead shows a strong, model-consistent association between forecast skill and observed land-cover change. Its predictable component is state-conditional and anti-persistent, consistent with mean reversion, and it varies across years. Aggregate forecast error alone is therefore a poor test of whether a representation is a useful predictive state.