Evaluating the Performance of Prithvi Foundation Model and Conditional Generative Adversarial Network for Cloud Gap Imputation in Multispectral Satellite Imagery

Core Concepts

Prithvi Foundation Model outperforms a Conditional Generative Adversarial Network in imputing missing pixels due to clouds in multispectral satellite imagery, demonstrating the advantages of geospatial foundation models.

Abstract

The paper compares the performance of a Vision Transformer-based Prithvi Foundation Model and a Conditional Generative Adversarial Network (CGAN) for imputing missing pixels due to clouds in multispectral satellite imagery.
The key highlights are:

The Prithvi Foundation Model, which was pretrained on 1TB of multispectral satellite imagery, outperforms the CGAN model in terms of both Mean Absolute Error (MAE) and Structural Similarity Index (SSIM) across various training dataset sizes.

The Prithvi model achieves an MAE of 0.03 on the validation dataset, which corresponds to an average error of 20% compared to the mean pixel reflectance. This performance is achieved with zero-shot inference, without any fine-tuning.

The CGAN model shows consistent improvement in performance with larger training datasets, but is still outperformed by the Prithvi model even when trained on the full dataset.

Visual inspection shows that the Prithvi model generates more realistic reflectance values compared to the CGAN, which tends to produce unrealistic values and noise, especially in areas with insufficient data.

The authors recommend using the configuration where only the middle scene in each chip is masked, as the Prithvi model performs better in this setting compared to masking all time steps.

The authors suggest that the cloud gap imputation capability of geospatial foundation models like Prithvi can be leveraged to augment training datasets for downstream applications that benefit from complete and multi-temporal satellite imagery.

Stats

The mean pixel reflectance values for all bands of the validation dataset were 0.151.
An MAE of 0.03 on the validation dataset corresponds to an average error of approximately 20 percent.

Quotes

"Prithvi, even without fine-tuning, outperforms the CGAN on cloud gap imputation with all subsets of data. This shows the strength of GFMs on gap-filling tasks, given that this mimics the training loop of models such as Prithvi."
"The gap-filled imagery can be used to augment time series data for training other downstream applications which benefit from complete coverage and multi-temporal scenes (like crop type segmentation or crop yield estimation)."

Key Insights Distilled From

Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model

by Denys Godwin... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19609.pdf

Deeper Inquiries

How can the cloud gap imputation performance of Prithvi be further improved, for example, by incorporating additional data sources such as Digital Elevation Models or land cover classifications?

To enhance the cloud gap imputation performance of Prithvi, integrating additional data sources such as Digital Elevation Models (DEMs) and land cover classifications can be beneficial. By incorporating DEMs, Prithvi can leverage elevation information to better understand the terrain and its impact on cloud cover. Elevation data can help in predicting cloud patterns and their effects on different regions, enabling more accurate imputation of cloud-covered areas in satellite imagery. Additionally, combining land cover classifications with satellite imagery can provide valuable insights into the types of surfaces or vegetation present, aiding in the contextual understanding of the imputed areas. This integration can help Prithvi generate more realistic and contextually accurate imputations by considering the relationship between land cover types and cloud cover dynamics.

What are the potential limitations or biases of using synthetic data generated by Prithvi to augment training datasets for downstream tasks, and how can these be addressed?

While using synthetic data generated by Prithvi for augmenting training datasets can be beneficial, there are potential limitations and biases to consider. One limitation is the risk of overfitting to the synthetic data, leading to reduced generalization performance on real-world data. Biases may arise if the synthetic data does not accurately represent the variability and complexity of real-world scenarios, impacting the model's ability to generalize effectively. To address these challenges, it is essential to validate the synthetic data against ground truth observations to ensure its fidelity. Additionally, incorporating diverse and representative real-world data during training can help mitigate biases and improve the model's robustness to unseen scenarios. Regular validation and testing on real-world datasets are crucial to assess the generalization capabilities of the model trained on synthetic data.

How can the cloud gap imputation capabilities of geospatial foundation models like Prithvi be leveraged to support real-world applications in areas such as agriculture, forestry, or urban planning?

The cloud gap imputation capabilities of geospatial foundation models like Prithvi offer significant potential for supporting real-world applications in various domains such as agriculture, forestry, and urban planning. In agriculture, Prithvi's ability to fill in missing data due to cloud cover can aid in monitoring crop health, predicting yields, and detecting anomalies in vegetation cover. For forestry applications, cloud gap imputation can assist in monitoring deforestation, assessing forest health, and identifying changes in tree cover over time. In urban planning, Prithvi's imputation capabilities can be utilized for analyzing land use changes, monitoring urban sprawl, and assessing environmental impacts in urban areas. By leveraging Prithvi's cloud gap imputation capabilities, stakeholders in these fields can make more informed decisions based on complete and accurate satellite imagery data, leading to improved resource management and decision-making processes.