Transferring Physical Priors into Remote Sensing Segmentation via Large Language Models

• URL: http://arxiv.org/abs/2603.27504v1
• Date: Sun, 29 Mar 2026 03:55:11 GMT
• Title: Transferring Physical Priors into Remote Sensing Segmentation via Large Language Models
• Abstract summary: We introduce a novel paradigm for integrating domain-specific physical priors into segmentation models.<n>We develop PriorSeg, a physics-aware residual refinement model trained with a joint visual-physical strategy.
• Score: 7.859330723946042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation of remote sensing imagery is fundamental to Earth observation. Achieving accurate results requires integrating not only optical images but also physical variables such as the Digital Elevation Model (DEM), Synthetic Aperture Radar (SAR) and Normalized Difference Vegetation Index (NDVI). Recent foundation models (FMs) leverage pre-training to exploit these variables but still depend on spatially aligned data and costly retraining when involving new sensors. To overcome these limitations, we introduce a novel paradigm for integrating domain-specific physical priors into segmentation models. We first construct a Physical-Centric Knowledge Graph (PCKG) by prompting large language models to extract physical priors from 1,763 vocabularies, and use it to build a heterogeneous, spatial-aligned dataset, Phy-Sky-SA. Building on this foundation, we develop PriorSeg, a physics-aware residual refinement model trained with a joint visual-physical strategy that incorporates a novel physics-consistency loss. Experiments on heterogeneous settings demonstrate that PriorSeg improves segmentation accuracy and physical plausibility without retraining the FMs. Ablation studies verify the effectiveness of the Phy-Sky-SA dataset, the PCKG, and the physics-consistency loss.

Related papers

• Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study [1.8964402635820148]
  This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide.<n>We present a comprehensive assessment of pretraining methodologies, covering five different contrastive and non-contrastive self-supervised learning objectives for ECG foundation models.<n>We investigate their scaling behavior with pretraining dataset sizes up to 11M input samples, exclusively from publicly available sources.
  arXiv  Detail & Related papers  (2026-05-12T15:10:15Z)
• Fake It Right: Injecting Anatomical Logic into Synthetic Supervised Pre-training for Medical Segmentation [21.75204301463342]
  Vision Transformers (ViTs) excel in 3D medical segmentation but require massive datasets.<n>Formula-Driven Supervised Learning (F) offers a privacy-preserving alternative by pre-training on synthetic mathematical primitives.<n>We propose an annotated-Informed Synthetic Anatomy Supervised Pre-training framework unifying F's infinite scalability with anatomical realism.
  arXiv  Detail & Related papers  (2026-03-01T08:15:18Z)
• PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction [0.16206783799607727]
  We introduce PhysE-Inv, a novel framework that integrates a sophisticated sequential architecture, an LSTM-Decoder with Multi-head Attention and physics-guided contrastive learning, with physics-guided inference.<n> PhysE-Inv significantly improves prediction performance, reducing error by 20% while demonstrating superior physical consistency and resilience to data sparsity compared to empirical methods.<n>This approach pioneers a path for noise-tolerant, interpretable inverse modeling, with wide applicability in geospatial and cryospheric domains.
  arXiv  Detail & Related papers  (2026-01-23T00:43:51Z)
• SourceNet: Interpretable Sim-to-Real Inference on Variable-Geometry Sensor Arrays for Earthquake Source Inversion [7.936803576241796]
  SourceNet is a Transformer-based framework that treats the sensor array as a flexible set to model arbitrary geometries.<n>By pre-training on 100,000 synthetic events and fine-tuning on 2,000 real world events, SourceNet achieves state-of-the-art precision on held-out real data.
  arXiv  Detail & Related papers  (2026-01-09T21:42:28Z)
• SPHERE: Semantic-PHysical Engaged REpresentation for 3D Semantic Scene Completion [52.959716866316604]
  Camera-based 3D Semantic Scene Completion (SSC) is a critical task in autonomous driving systems.<n>We propose the Semantic-PHysical Engaged REpresentation (SPHERE) for camera-based SSC.<n>SPHERE integrates voxel and Gaussian representations for joint exploitation of semantic and physical information.
  arXiv  Detail & Related papers  (2025-09-14T09:07:41Z)
• PhySense: Sensor Placement Optimization for Accurate Physics Sensing [53.58729034895666]
  PhySense is a framework that learns to jointly reconstruct physical fields and to optimize sensor placements.<n>It achieves state-of-the-art physics sensing accuracy and discovers informative sensor placements previously unconsidered.
  arXiv  Detail & Related papers  (2025-05-19T14:59:11Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection [4.592579302639643]
  Single-Domain Generalized Object Detection(S-DGOD) aims to train on a single source domain for robust performance across a variety of unseen target domains by taking advantage of an object detector.<n>Existing S-DGOD approaches often rely on data augmentation strategies, including a composition of visual transformations, to enhance the detector's generalization ability.<n>We propose PhysAug, a novel physical model-based non-ideal imaging condition data augmentation method, to enhance the adaptability of the S-DGOD tasks.
  arXiv  Detail & Related papers  (2024-12-16T14:18:01Z)
• PhyRecon: Physically Plausible Neural Scene Reconstruction [81.73129450090684]
  We introduce PHYRECON, the first approach to leverage both differentiable rendering and differentiable physics simulation to learn implicit surface representations. Central to this design is an efficient transformation between SDF-based implicit representations and explicit surface points. Our results also exhibit superior physical stability in physical simulators, with at least a 40% improvement across all datasets.
  arXiv  Detail & Related papers  (2024-04-25T15:06:58Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)
• PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer [55.936527926778695]
  Recent deep learning approaches focus on mining subtle r clues using convolutional neural networks with limited-temporal receptive fields. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture.
  arXiv  Detail & Related papers  (2021-11-23T18:57:11Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)

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.