# VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation

arXiv: https://arxiv.org/abs/2606.21961 · HTML: https://mlsanity.com/paper/2606.21961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Placed in

- World models / [Latent predictive world models](https://mlsanity.com/wm/latent-predictive-world-models/)

## Builds on (most cited first, 17 of 17)

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- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
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- [EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task](https://mlsanity.com/paper/2104.10066/) (2021)
- [Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs](https://mlsanity.com/paper/2210.13648/) (2022)
- [Earth System Data Cubes: Avenues for advancing Earth system research](https://mlsanity.com/paper/2408.02348/) (2024)
- [Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates](https://mlsanity.com/paper/2602.17683/) (2026)
- [On-Demand Earth System Data Cubes](https://mlsanity.com/paper/2404.13105/) (2024)
- [RS-WorldModel: a Unified Model for Remote Sensing Understanding and Future Sense Forecasting](https://mlsanity.com/paper/2603.14941/) (2026)

## Abstract (arXiv)

Vegetation monitoring under climate stress requires answering not only how it will evolve given the expected weather, but how it would respond to alternative meteorological conditions. Forecasting models return the expected vegetation state for the observed weather and cannot answer these scenario-conditioned questions, because future weather is fixed to the recorded trajectory. We present VegSim, a geospatial world model for scenario-conditioned vegetation simulation. VegSim infers a latent vegetation state from sparse satellite-derived NDVI histories, past meteorological covariates, and static spatial context, propagates it forward under future weather forcing through recurrent latent dynamics, and decodes predictive NDVI quantiles at each lead time. Because future forcing enters as a controllable input, the same trained model supports probabilistic forecasting under observed weather and conditional simulation under user-defined meteorological forcing, without supervision on scenario responses. We evaluate VegSim on GreenEarthNet across in-distribution data and spatial, temporal, and joint spatio-temporal shifts, where it achieves strong point and probabilistic accuracy against time series and Earth Observation forecasting baselines while using a compact architecture. We then simulate vegetation responses across Europe under two contrasting meteorological scenarios, obtaining a weak but broadly positive response under winter wet warming and a widespread negative response under summer drought. The code is available at https://github.com/arco-group/vegsim.