Multi-modal learning for geospatial vegetation forecasting (2303.16198v2)

Abstract: The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbon accounting. To leverage the vast availability of satellite imagery for this task, various works have applied deep neural networks for predicting multispectral images in photorealistic quality. However, the important area of vegetation dynamics has not been thoroughly explored. Our study breaks new ground by introducing GreenEarthNet, the first dataset specifically designed for high-resolution vegetation forecasting, and Contextformer, a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe. Our multi-modal transformer model Contextformer leverages spatial context through a vision backbone and predicts the temporal dynamics on local context patches incorporating meteorological time series in a parameter-efficient manner. The GreenEarthNet dataset features a learned cloud mask and an appropriate evaluation scheme for vegetation modeling. It also maintains compatibility with the existing satellite imagery forecasting dataset EarthNet2021, enabling cross-dataset model comparisons. Our extensive qualitative and quantitative analyses reveal that our methods outperform a broad range of baseline techniques. This includes surpassing previous state-of-the-art models on EarthNet2021, as well as adapted models from time series forecasting and video prediction. To the best of our knowledge, this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle, thereby paving the way for predicting vegetation health and behaviour in response to climate variability and extremes.

• The paper presents a novel multi-modal framework using the GreenEarthNet dataset and Contextformer model for precise vegetation forecasting.
• It integrates a Pyramid Vision Transformer with a temporal transformer to effectively combine spatial context and time dynamics with improved cloud masking.
• The proposed approach achieved an R² of 0.62 and reduced RMSE to 0.14 over a 100-day horizon, outperforming legacy models in environmental predictions.

Multi-Modal Learning for Geospatial Vegetation Forecasting

The paper presented in this paper addresses a critical aspect of environmental modeling by introducing a novel framework for geospatial vegetation forecasting using multi-modal data. The researchers propose GreenEarthNet, a dataset tailored for high-resolution vegetation forecasting, and the Contextformer, a state-of-the-art multi-modal transformer model for predicting vegetation greenness across Europe.

The innovative edge of this research lies in its methodological advancements and dataset development. GreenEarthNet represents a significant improvement over the previous EarthNet2021 dataset, addressing issues such as faulty cloud masking and lack of a coherent evaluation scheme. This ensures a more robust platform for vegetation modeling that aligns with the complex ecological dynamics involved. Contextformer, the multi-modal transformer model introduced, leverages a Pyramid Vision Transformer backbone to integrate spatial context efficiently, while employing a temporal transformer for understanding temporal dynamics.

Key Performance Highlights:

• GreenEarthNet Dataset: Provides high-resolution data, including Sentinel 2 satellite imagery, and features enhanced cloud masking techniques for increased accuracy. The dataset is structured to allow compatibility with EarthNet2021, facilitating cross-dataset evaluation.
• Model Performance: Contextformer exhibits superior performance against both legacy machine learning models and state-of-the-art approaches, achieving an $R^2$ of 0.62 and a notable reduction in RMSE to 0.14 over a 100-day prediction horizon. The model significantly surpasses the climatology baseline, indicating its efficacy in capturing vegetation dynamics.
• Comparison with EarthNet Models: Models trained on EarthNet2021, such as ConvLSTM and Earthformer, do not perform as well, highlighting the improvements introduced by GreenEarthNet and the new modeling strategies.

The theoretical implication of this research lies in its contribution to advancing multi-modal learning architectures for remote sensing applications. The Contextformer model emphasizes the utility of combining spatial and temporal data for predictive accuracy, signaling new avenues in AI applications for ecological and climate-driven phenomena.

Practically, this approach holds substantial potential for diverse applications including agricultural management, forestry operations, and environmental monitoring. The ability to forecast vegetation conditions with such precision is invaluable for proactive agricultural planning, deforestation monitoring, and assessing ecosystem responses to climate variability.

Future research could benefit from scaling the GreenEarthNet dataset to a global scope and experimenting with additional modalities, such as hyperspectral data, to further improve model capabilities. Integrating more sophisticated atmospheric and climatic predictors might refine the understanding of fine-scale vegetation responses to environmental stresses.

In conclusion, the paper makes a substantial contribution to geospatial modeling by enriching the methodological toolkit and data resources available for vegetation forecasting. It paves the way for more precise and informed decision-making in climate-sensitive sectors, enhancing the predictive prowess of machine learning models in environmental sciences.