Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensing

• URL: http://arxiv.org/abs/2601.17288v1
• Date: Sat, 24 Jan 2026 03:55:21 GMT
• Title: Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensing
• Abstract summary: We propose a lightweight architecture that introduces a topology-aware feature rectification framework.<n>F Fluxamba achieves a real-time inference speed of over 24 FPS with only 3.4M parameters and 6.3G FLOPs.
• Score: 6.815807403335458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The precise segmentation of geological linear features, spanning from planetary lineaments to terrestrial fractures, demands capturing long-range dependencies across complex anisotropic topologies. Although State Space Models (SSMs) offer near-linear computational complexity, their dependence on rigid, axis-aligned scanning trajectories induces a fundamental topological mismatch with curvilinear targets, resulting in fragmented context and feature erosion. To bridge this gap, we propose Fluxamba, a lightweight architecture that introduces a topology-aware feature rectification framework. Central to our design is the Structural Flux Block (SFB), which orchestrates an anisotropic information flux by integrating an Anisotropic Structural Gate (ASG) with a Prior-Modulated Flow (PMF). This mechanism decouples feature orientation from spatial location, dynamically gating context aggregation along the target's intrinsic geometry rather than rigid paths. Furthermore, to mitigate serialization-induced noise in low-contrast environments, we incorporate a Hierarchical Spatial Regulator (HSR) for multi-scale semantic alignment and a High-Fidelity Focus Unit (HFFU) to explicitly maximize the signal-to-noise ratio of faint features. Extensive experiments on diverse geological benchmarks (LROC-Lineament, LineaMapper, and GeoCrack) demonstrate that Fluxamba establishes a new state-of-the-art. Notably, on the challenging LROC-Lineament dataset, it achieves an F1-score of 89.22% and mIoU of 89.87%. Achieving a real-time inference speed of over 24 FPS with only 3.4M parameters and 6.3G FLOPs, Fluxamba reduces computational costs by up to two orders of magnitude compared to heavy-weight baselines, thereby establishing a new Pareto frontier between segmentation fidelity and onboard deployment feasibility.

Related papers

• Widest-Path Reachability Fields for Connectivity-Preserving Slender Structure Segmentation [3.8331203995580565]
  We propose Widest-Path Reachability Fields (WPRF), a differentiable Max-Min reachability objective that redirects gradient flow to connectivity bottlenecks.<n>WPRF implements a differentiable Max-Min objective via dynamic programming on a domain-restricted graph.<n> Experiments across nine architectures and six datasets validate the bottleneck-focused gradient routing mechanism.
  arXiv  Detail & Related papers  (2026-07-08T08:04:06Z)
• Topological Flow Matching [56.16372813551555]
  Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance.<n>We introduce topological flow matching, a topology-aware generalization of flow matching.<n>We demonstrate its effectiveness on diverse structured datasets, including brain fMRIs, ocean currents, seismic events, and traffic flows.
  arXiv  Detail & Related papers  (2026-06-14T16:29:33Z)
• FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
  FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2026-05-27T08:55:54Z)
• GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval [54.741349848771144]
  GeoMamba is a geometry-driven framework for optical-SAR fine-grained retrieval.<n>GFI module enhances cross-modal feature interaction and incorporates structural priors.<n>GeoMamba outperforms existing methods, achieving 63.3% mAP and 77.0% Rank-1 accuracy in all-to-all retrieval setting.
  arXiv  Detail & Related papers  (2026-05-19T12:08:09Z)
• Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks [7.796460319257142]
  Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and structures.<n>Most HSI-LiDAR fusion methods rely on CNNs or geometrics with fixed activation functions and linear weights.<n>We propose the Implicit Frequency-Geometry Fusion Network (IFGNet), which adaptively capture highly nonlinear relationships between hyperspectral and LiDAR features.
  arXiv  Detail & Related papers  (2026-05-14T01:12:12Z)
• A Mean Curvature Approach to Boundary Detection: Geometric Insights for Unsupervised Learning [52.452902154360565]
  We introduce Mean Curvature Boundary Points (MCBP), a novel geometric framework grounded in Geometric Machine Learning.<n>MCBP explicitly models the intrinsic curvature of the data manifold and computes pointwise mean curvature without requiring principled manifold parametrization.<n>Experiments on synthetic and real-world datasets demonstrate that MCBP consistently improves clustering performance.
  arXiv  Detail & Related papers  (2026-05-05T20:19:09Z)
• Star-Fusion: A Multi-modal Transformer Architecture for Discrete Celestial Orientation via Spherical Topology [0.0]
  We present Star-Fusion, a multi-modal architecture that reformulates orientation estimation as a discrete topological classification task.<n>The proposed architecture employs a SwinV2-Tiny transformer backbone for photometric feature extraction, a tripartite convolutional heatmap branch for spatial grounding, and a coordinate-based for geometric anchoring.<n> Experimental evaluations on a synthetic Hipparcos-derived dataset demonstrate that Star-Fusion achieves a Top-1 accuracy of 93.4% and a Top-3 accuracy of 97.8%.
  arXiv  Detail & Related papers  (2026-04-29T12:01:41Z)
• Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs [6.490344405865677]
  FGOS-Net is a framework based on frequency allowbreak-geometric disentanglement.<n>It consistently outperforms strong baselines across four challenging benchmarks.<n>It achieves 91.3% mIoU and 97.1% clDice on DeepCrack while running at 80 FPS with only 7.87 GFLOPs.
  arXiv  Detail & Related papers  (2026-03-30T14:39:04Z)
• Cross-view geo-localization, Image retrieval, Multiscale geometric modeling, Frequency domain enhancement [1.6686955491488273]
  Cross-view geo-localization (CVGL) aims to establish spatial correspondences between images captured from significantly different viewpoints.<n>CVGL remains challenging due to severe geometric asymmetry, texture inconsistency across imaging domains, and the progressive degradation of discriminative local information.<n>This paper proposes the Spatial and Frequency Domain Enhancement Network (SFDE), which leverages complementary representations from spatial and frequency domains.
  arXiv  Detail & Related papers  (2026-03-03T08:25:35Z)
• DSFC-Net: A Dual-Encoder Spatial and Frequency Co-Awareness Network for Rural Road Extraction [32.51260718935461]
  We propose DSFC-Net, a dual-encoder framework that fuses spatial and frequency-domain information.<n>CFIA module explicitly decouples high- and low-frequency information via a Laplacian Pyramid strategy.<n>Experiments on the WHU-RuR+, DeepGlobe, and Massachusetts datasets validate the superiority of DSFC-Net over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-02-01T15:23:42Z)
• Dynamic Topology Awareness: Breaking the Granularity Rigidity in Vision-Language Navigation [22.876516699004814]
  Vision-Language Navigation in Continuous Environments (VLN-CE) presents a core challenge: grounding high-level linguistic instructions into precise, safe, and long-horizon spatial actions.<n>Explicit topological maps have proven to be a vital solution for providing robust spatial memory in such tasks.<n>Existing topological planning methods suffer from a "Granularity Rigidity" problem.<n>We propose DGNav, a framework for Dynamic Topological Navigation, introducing a context-aware mechanism to modulate map density and connectivity on-the-fly.
  arXiv  Detail & Related papers  (2026-01-29T14:06:23Z)
• PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition [49.955269674859004]
  This paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to align model capacity with signal complexity.<n>Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement.<n>A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency.
  arXiv  Detail & Related papers  (2026-01-19T07:57:52Z)
• MS-ISSM: Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity [65.85858856481131]
  unstructured and irregular nature of point clouds poses a significant challenge for objective quality assessment (PCQA)<n>We propose the Multi-scale Implicit Structural Similarity Measurement (MS-ISSM)
  arXiv  Detail & Related papers  (2026-01-03T14:58:52Z)
• DisentangleFormer: Spatial-Channel Decoupling for Multi-Channel Vision [10.378378296066305]
  Vision Transformers face a fundamental limitation: standard self-attention jointly processes spatial and channel dimensions.<n>We propose DisentangleFormer, an architecture that achieves robust multi-channel vision representation through principled spatial-channel decoupling.<n>Our design integrates three core components: (1) Parallel Disentanglement: Independently processes spatial-token and channel-token streams, enabling decorrelated feature learning across spatial and spectral dimensions, (2) Squeezed Token Enhancer: An adaptive calibration module that dynamically fuses spatial and channel streams, and (3) Multi-Scale FFN: complementing global attention with multi-scale local context.
  arXiv  Detail & Related papers  (2025-12-03T23:03:56Z)
• SVRecon: Sparse Voxel Rasterization for Surface Reconstruction [60.92372415355283]
  We extend the recently proposed sparse voxelization paradigm to the task of high-fidelity surface reconstruction by integrating SVRecon.<n>Our method achieves strong reconstruction accuracy while having consistently speedy convergence.
  arXiv  Detail & Related papers  (2025-11-21T16:32:01Z)
• Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution [68.54692184478462]
  Mamba-based methods have shown great potential in optimizing both computational cost and performance of light field image super-resolution.<n>We propose a Subspace Simple Scanning (Sub-SS) strategy, based on which we design the Subspace Simple Mamba Block (SSMB) to achieve more efficient and precise feature extraction.<n>We also propose a dual-stage modeling strategy to address the limitation of state space in preserving spatial-angular and disparity information.
  arXiv  Detail & Related papers  (2025-09-05T05:50:38Z)
• FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
  FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
  arXiv  Detail & Related papers  (2025-05-23T00:07:59Z)
• Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data [14.104497777255137]
  We introduce Low-rank Efficient Spatial-Spectral Vision Transformer with three key innovations.<n>We pretrain LESS ViT using a Hyperspectral Masked Autoencoder framework with integrated positional and channel masking strategies.<n> Experimental results demonstrate that our proposed method achieves competitive performance against state-of-the-art multi-modal geospatial foundation models.
  arXiv  Detail & Related papers  (2025-03-17T05:42:19Z)
• Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity [3.792470553976718]
  This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS)<n>Topology-GS addresses compromised pixel-level structural integrity due to incomplete initial geometric coverage.<n>Experiments on three novel-view benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics.
  arXiv  Detail & Related papers  (2024-12-21T13:25:03Z)
• FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Optical Flow Estimation [72.41370576242116]
  We propose a semi-supervised Feature Pyramidal Correlation and Residual Reconstruction Network (FPCR-Net) for optical flow estimation from frame pairs. It consists of two main modules: pyramid correlation mapping and residual reconstruction. Experiment results show that the proposed scheme achieves the state-of-the-art performance, with improvement by 0.80, 1.15 and 0.10 in terms of average end-point error (AEE) against competing baseline methods.
  arXiv  Detail & Related papers  (2020-01-17T07:13:51Z)

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.