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HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Data

Stella Girtsou, Konstantinos Alexis, Giorgos Giannopoulos, Harris Kontoes

Abstract

The increasing frequency and severity of climate related disasters have intensified the need for real time monitoring, early warning, and informed decision-making. Earth Observation (EO), powered by satellite data and Machine Learning (ML), offers powerful tools to meet these challenges. Foundation Models (FMs) have revolutionized EO ML by enabling general-purpose pretraining on large scale remote sensing datasets. However most existing models rely on high-resolution satellite imagery with low revisit rates limiting their suitability for fast-evolving phenomena and time critical emergency response. In this work, we present HighFM, a first cut approach towards a FM for high temporal resolution, multispectral EO data. Leveraging over 2 TB of SEVIRI imagery from the Meteosat Second Generation (MSG) platform, we adapt the SatMAE masked autoencoding framework to learn robust spatiotemporal representations. To support real time monitoring, we enhance the original architecture with fine grained temporal encodings to capture short term variability. The pretrained models are then finetuned on cloud masking and active fire detection tasks. We benchmark our SEVIRI pretrained Vision Transformers against traditional baselines and recent geospatial FMs, demonstrating consistent gains across both balanced accuracy and IoU metrics. Our results highlight the potential of temporally dense geostationary data for real-time EO, offering a scalable path toward foundation models for disaster detection and tracking.

HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Data

Abstract

The increasing frequency and severity of climate related disasters have intensified the need for real time monitoring, early warning, and informed decision-making. Earth Observation (EO), powered by satellite data and Machine Learning (ML), offers powerful tools to meet these challenges. Foundation Models (FMs) have revolutionized EO ML by enabling general-purpose pretraining on large scale remote sensing datasets. However most existing models rely on high-resolution satellite imagery with low revisit rates limiting their suitability for fast-evolving phenomena and time critical emergency response. In this work, we present HighFM, a first cut approach towards a FM for high temporal resolution, multispectral EO data. Leveraging over 2 TB of SEVIRI imagery from the Meteosat Second Generation (MSG) platform, we adapt the SatMAE masked autoencoding framework to learn robust spatiotemporal representations. To support real time monitoring, we enhance the original architecture with fine grained temporal encodings to capture short term variability. The pretrained models are then finetuned on cloud masking and active fire detection tasks. We benchmark our SEVIRI pretrained Vision Transformers against traditional baselines and recent geospatial FMs, demonstrating consistent gains across both balanced accuracy and IoU metrics. Our results highlight the potential of temporally dense geostationary data for real-time EO, offering a scalable path toward foundation models for disaster detection and tracking.

Paper Structure

This paper contains 9 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Methodology
  4. Datasets
  5. Pretraining with Adapted SatMAE
  6. Fine-tuning
  7. Experimental Setup
  8. Results
  9. Discussion

Figures (3)

  • Figure 1: High level flowchart of our methodology
  • Figure 2: Area of Interest
  • Figure 3: Qualitative results on test samples. Rows 1 and 2 show models fine-tuned on the cloud segmentation task, while rows 3 and 4 correspond to the fire detection task. Rows 1 and 3 present outputs from models trained with cross-entropy loss, whereas rows 2 and 4 show results from models trained with Dice loss. SEVIRI input images are visualized using a false-color fire composite, consistent with EUMETSAT’s operational fire RGB products. In the prediction columns, true positives are shown in green, false positives in red, and false negatives in blue.