Scalable and Trustworthy Earth Observation Foundation Models

• URL: http://arxiv.org/abs/2607.07758v1
• Date: Wed, 08 Jul 2026 14:31:02 GMT
• Title: Scalable and Trustworthy Earth Observation Foundation Models
• Abstract summary: Foundation models (FMs) have transformed machine learning from isolated task-specific models to general-purpose models pretrained on broad data.<n>This chapter defines the FMs paradigm in remote sensing (RS), then synthesizes the current model landscape.<n>The chapter also incorporates recent benchmark evidence showing that no single geospatial foundation model is universally best.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models (FMs) have transformed machine learning from isolated task-specific model development toward general-purpose models pretrained on broad data and adapted to multiple downstream tasks. Earth observation (EO) is an important domain for this paradigm because satellite and airborne archives are large, high-revisit, and increasingly multimodal, while reliable field labels are often sparse. Remote sensing foundation models (RSFMs) cannot be transferred reliably/optimally without domain-specific adaptation. This is because EO data are governed by measurement physics and operational decision constraints. This chapter reviews the design principles arising from these domain-specific constraints. It first defines the FMs paradigm in remote sensing (RS), then synthesizes the current model landscape, pretraining objectives, architecture designs, downstream adaptation and trustworthiness requirements. The chapter also incorporates recent benchmark evidence showing that no single geospatial foundation model is universally best and that inconsistent evaluation remains a major issue to fair comparison and reliable deployment. In addition, two brief environmental monitoring case studies; physics-informed spectral targeted masking for harmful algal bloom prediction and reinforcement learning for adaptive environmental monitoring station selection to illustrate the FMs domain-guided principles in practice. This chapter posits that next-generation RSFMs should be evaluated not only by benchmark accuracy, but also by modality-aware transfer and physically plausible representations for trustworthy EO decisions.

Related papers

• IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring [59.59850511287819]
  We propose the industrial process monitoring foundation model (IPM-FM)<n>It first learns general-purpose representations from unlabeled industrial process data through self-supervised pretraining.<n>It adapts to specific monitoring tasks using a small amount of task-labeled data, and finally produces calibrated predictions.
  arXiv  Detail & Related papers  (2026-09-08T07:43:04Z)
• Wireless Foundation Models: State-of-the-Art and Open Challenges [8.033625328567284]
  Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data.<n>This survey provides a systematic analysis of WFMs for physical-layer applications.
  arXiv  Detail & Related papers  (2026-09-04T04:26:04Z)
• The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning [65.67266333751569]
  Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks.<n>We propose a self-supervised reinforcement learning framework that targets the internal reasoning process without requiring ground-truth annotations.<n>We show that this label-free consistency training approaches the accuracy of models trained with ground-truth supervision and achieves similar generalization across diverse tasks and data domains.
  arXiv  Detail & Related papers  (2026-06-10T10:50:06Z)
• Posterior Augmented Flow Matching [64.1559809786948]
  Posterior-Augmented Flow Matching (PAFM) is a generalization of flow matching (FM)<n>We show that PAFM improves over FM by up to 3.4 FID50K across different model scales.
  arXiv  Detail & Related papers  (2026-05-01T17:59:59Z)
• Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising [4.127374559128319]
  The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data.<n>This expansion makes complex noise suppression increasingly challenging, especially when signal fidelity must be preserved.<n>Pre-training seismic foundation models from scratch requires massive domain-specific data and substantial computation.<n>We propose an efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks through Fine-Tuning.
  arXiv  Detail & Related papers  (2026-04-30T15:28:51Z)
• PoseFM: Relative Camera Pose Estimation Through Flow Matching [0.0]
  We propose PoseFM, a framework to reformulate monocular frame-to-frame VO as a generative task using Flow Matching (FM)<n>By leveraging FM, we model camera motion as a distribution rather than a point estimate, learning to transform noise into realistic pose predictions via continuous-time ODEs.<n>In our evaluations, PoseFM achieves strong performance on TartanAir, KITTI and TUM-RGBD benchmarks, achieving the lowest absolute trajectory error (ATE) on some of the trajectories.
  arXiv  Detail & Related papers  (2026-04-24T08:36:53Z)
• Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting [52.53742727060265]
  Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets.<n>Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3Dwin UNet backbone of the pioneering model.<n>We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data.<n>Results demonstrate competitive or superior performance relative to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T11:40:39Z)
• The Seismic Wavefield Common Task Framework [4.588878997984687]
  Seismology faces fundamental challenges in state forecasting and reconstruction.<n>Recent machine learning efforts offer promise, but progress is obscured by a lack of proper characterization.<n>We introduce a Common Task Framework (CTF) for ML for seismic wavefields.
  arXiv  Detail & Related papers  (2025-12-22T23:04:03Z)
• VFMF: World Modeling by Forecasting Vision Foundation Model Features [67.09340259579761]
  We introduce a generative forecaster that performs autoregressive flow matching in vision foundation models feature space.<n>We show that this latent information more effectively than previously used PCA-based alternatives, both for forecasting and other applications.<n>With matched architecture and compute, our method produces sharper and more accurate predictions than regression across all modalities.
  arXiv  Detail & Related papers  (2025-12-12T02:10:05Z)
• GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI [52.13138825802668]
  GeoFMs are transforming Earth Observation, but evaluation lacks standardized protocols.<n> GEO-Bench-2 addresses this with a comprehensive framework spanning classification, segmentation, regression, object detection, and instance segmentation.<n>Code, data, and leaderboard for GEO-Bench-2 are publicly released under a permissive license.
  arXiv  Detail & Related papers  (2025-11-19T17:45:02Z)
• The View From Space: Navigating Instrumentation Differences with EOFMs [0.0]
  Earth Observation Foundation Models (EOFMs) have exploded in prevalence as tools for processing the massive volumes of remotely sensed and other earth observation data, and for delivering impact on the many essential earth monitoring tasks.<n>An emerging trend posits using the outputs of pre-trained models as 'embeddings' which summarize high dimensional data to be used for generic tasks such as similarity search and content-specific queries.<n>Most EOFM models are trained only on single modalities of data and then applied or benchmarked by matching bands across different modalities.<n>It is not clear from existing work what impact diverse sensor architectures have on the internal representations
  arXiv  Detail & Related papers  (2025-10-01T00:53:45Z)
• On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study [7.0723456126359245]
  This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications. A case study is presented featuring a vehicle classification application using acoustic and seismic sensing.
  arXiv  Detail & Related papers  (2024-04-03T05:04:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.