RS-Paper-Hub — All Papers

RS-Paper-Hub — All Papers https://rspaper.top/output/feed.xml 2026-10-06T07:05:51Z Latest remote sensing papers (last 7 days) — 21 entries RS-Paper-Hub https://rspaper.top Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory http://arxiv.org/abs/2610.06476v1 2026-10-05T00:00:00Z 2026-10-05T00:00:00Z Arthur Spitta Andrew O. Finley Christoph Gollob Ralf Kraßnitzer Tim Ritter Andreas Tockner Arne Nothdurft

Forest managers require timely stand-level information for operational planning. Model-based estimation combines sparse field samples with remotely sensed auxiliary data to estimate growing stock volume (GSV) for small-area units. This is particularly relevant in mixed-species and structurally heterogeneous forests, where timely structural information can support management decisions under climate change and associated disturbance pressures. Uncrewed aerial vehicle laser scanning (ULS) provides flexible access to high-resolution LiDAR data, but its benefits over conventional airborne laser scanning (ALS) for model-based inference remain insufficiently understood. We compared public ALS and new ULS data using Bayesian change-of-support models to estimate GSV in a mixed-species forest in north-eastern Germany. We assessed the effects of distributional LiDAR metrics and spatial random effects. ULS models consistently outperformed ALS models, with cross-validated root mean squared prediction errors (RMSPEs) of 68.5 m^3/ha and 79.5 m^3/ha , respectively, a 13.8 % reduction in prediction error. Distributional metrics benefited ULS models more than ALS models, reducing RMSPE by up to 10.1 %, whereas spatial effects yielded minor improvements at greater computational cost. ULS models also exhibited lower stand-level predictive uncertainty for latent stand-mean GSV. This advantage may partly reflect closer temporal alignment with field data and finer-scale predictor information. These findings suggest that timely, information-rich LiDAR data may be more beneficial for stand-level GSV estimation than increasingly complex spatial model structures. ULS is promising where timely acquisition and high-resolution canopy characterization are operationally feasible. Controlled comparisons with temporally matched ALS and ULS data are needed to distinguish platform effects from temporal mismatch.

Publication: 42 pages, 9 figures Category: Method Multi-Task Partially Supervised Learning for Super-Resolution and Semantic Segmentation on Earth Observation data http://arxiv.org/abs/2610.06389v1 2026-10-05T00:00:00Z 2026-10-05T00:00:00Z Hoàng-Ân Lê Minh-Tan Pham Solange Lemai-Chenevier Daniel Greslou

Super-resolution and semantic segmentation are known to benefit one another, especially in the Earth observation context. However, learning both tasks in a joint model often requires both task annotations, which is impractical and expensive. In this paper, we study the multi-task partially supervised learning paradigm for both tasks, where each example is assumed to have only a single-task annotation. To that end, we examine two multi-task architectural variations, the sequential and shared variants, and then propose a hybrid variant and a re-projection loss to benefit from the shared representation and enforce image quality of super-resolution when training with semantic segmentation. Experiments show favorable results compared to the SOTA sequential variant. Source code will be published at https://github.com/lhoangan/munera.

Code: <a href="https://github.com/lhoangan/munera">https://github.com/lhoangan/munera</a> Category: Method Tasks: SEG;SR EORestore-Agent: Fidelity-Guided Agentic Restoration of Remote Sensing Images with Composite Degradations http://arxiv.org/abs/2610.06196v1 2026-10-05T00:00:00Z 2026-10-05T00:00:00Z Heli Qi Zeqi Zhou Jingjun Yi Kunyi Liu Ziyang Lihe Junjue Wang Osamu Yoshie Naoto Yokoya

Remote sensing images often carry composite degradations, in which haze, cloud, noise, blur, low light, and low resolution coexist. Restoring them requires deciding which tool to apply, in what order, and when to stop, yet no clean reference is available at inference time to verify these decisions. All-in-one models trained on single degradations converge to a narrow PSNR band as degradations accumulate. To formulate real-world remote sensing restoration as a traceable trajectory, we present EORestore-Agent, which replaces this unmeasurable objective with reference-free, verifiable per-step decisions. A fine-tuned vision-language model reports all residual degradation types, whose tool pools are scored together, so the restoration order emerges from step-wise selection. A relative quality scorer, trained with full-reference supervision on synthetic degradation chains, predicts the changes in PSNR, SSIM, and LPIPS from the current image to each candidate. A step is accepted only when no predicted change is negative and the predicted PSNR gain is positive. Otherwise, the agent keeps the current image. On a synthetic Landsat-8 benchmark with six degradation types, EORestore-Agent improves PSNR by 2.3 to 3.2 dB over the strongest retrained all-in-one baseline on composites of two to six degradations, whereas zero-shot natural-image agents fall below the degraded input in PSNR in 17 of 18 settings. Replacing the learned scorer with no-reference quality differences costs 1.1 to 4.6 dB. The remaining harmful steps are small and cluster near the acceptance threshold. Sentinel-2 examples illustrate transfer to real atmospheric degradation without retraining.

Publication: 20 pages, 6 figures Category: Method FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models http://arxiv.org/abs/2610.05790v1 2026-10-05T00:00:00Z 2026-10-05T00:00:00Z Md Aminur Hossain Omkumar Vaghasiya Rajeev Ranjan Dwivedi Vinod Kurmi Biplab Banerjee

Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecological regions. We introduce FairRSFM, a biome-aware benchmark for evaluating ecological group robustness in RSFMs. FairRSFM maps georeferenced samples from 14 terrestrial biome classes into six ecologically meaningful macro-groups and evaluates models under a unified frozen-backbone evaluation protocol. The benchmark covers four downstream datasets: m-EuroSAT, m-BigEarthNet, m-SA-Crop-Type, and MMEarth20K with Dynamic World label maps. Using Prithvi-EO-2.0, SatMAE, and DOFA across three random seeds, we show that aggregate performance consistently masks biome-dependent disparities across architectures and tasks. For example, Prithvi-EO-2.0 reaches 90.98% overall macro-F1 on m-EuroSAT but a mean worst-group score of only 83.72%, while m-SA-Crop-Type drops from 27.30% overall mIoU to 18.47% in the Xeric and Mineralogical group. We further evaluate Biome-Orthogonal Linear Probing (BOLP), Dynamic Biome Reweighting (DBR), and GroupDRO as complementary mitigation baselines. Their effectiveness is model- and task-dependent; for example, BOLP improves Prithvi-EO-2.0 worst-group F1@opt on m-BigEarthNet from 46.12% to 50.27% without updating the RSFM backbone. FairRSFM provides a reusable protocol for diagnosing and mitigating ecological robustness gaps in remote sensing foundation models. Code and datasets are available at: https://github.com/aminurhossain/FairRSFM.

Code: <a href="https://github.com/aminurhossain/FairRSFM">https://github.com/aminurhossain/FairRSFM</a> Publication: 13 Category: Dataset T-JEPA: A Temporal Joint-Embedding Predictive Architecture for Learning Better Remote Sensing Representations http://arxiv.org/abs/2610.05731v1 2026-10-05T00:00:00Z 2026-10-05T00:00:00Z Bowen Peng Li Liu Yongxiang Liu Weijie Li Jie Zhou Zhen Liu

Earth observation (EO) data provide rich temporal supervision, yet existing remote sensing foundation models mainly exploit sequential observations through imposing predefined pairwise relations or aggregating holistic reconstruction context. We seek to further exploit the sparse and nonuniform temporal sampling inherent in EO sequences as supervisory signals. To this end, we propose T-JEPA, a temporal joint-embedding predictive architecture that learns time-gap-conditioned latent transitions. A shared single-frame encoder processes each observation, while a temporal predictor estimates the complete target latent field from a masked source latent representation and the actual elapsed time. Across multiple temporal intervals, these predictive constraints organize observed states into structured latent trajectories. Asymmetric metadata injection mitigates shortcut learning, and direct supervision across multiple temporal scales proves more effective than recursively rolling out intermediate states. In parallel, masked pixel reconstruction provides complementary supervision for preserving spatial details. Under matched pre-training data and throughput, T-JEPA achieves leading transfer performance on both static and temporal tasks. Analyses further reveal that T-JEPA learns representations with time-gap-dependent transition predictability and coherent latent dynamics, while maintaining strong cross-period consistency, representation diversity, and semantic discriminability.

Category: Method Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health http://arxiv.org/abs/2610.05699v1 2026-10-05T00:00:00Z 2026-10-05T00:00:00Z Arbaaz Muslim Aviv Slobodkin Katherine Wheeler-Martin Eric Zhou John Brittain Martin Frasch Hamsa Subramaniam Jacob Bien Hamed Sadeghi Sarah Conderino Stone Jiang Ben Spoer Daniel Neill Mimi Sun Joydeep Paul Yun Liu Tomer Shekel Amy Chung-Yu Chou Anna Carter Charles Elliott Aaron Bell Reuven Sayag Avinatan Hassidim Niv Efron Yossi Matias Kasumi Widner Eduardo Lopez Ortiz José Alberto Díaz-Quiñonez Moritz U. G. Kraemer John Brownstein Isaias Fernandes Co Etien Luc Koua Monica Bharel Benjamin Rader Lorna Thorpe Marc Gourevitch Shravya Shetty Gautam Prasad

The efficacy of traditional disease prediction is limited by spatial gaps and temporal lags, which impact the timing and targets of resource deployments. Outbreaks escalate undetected, chronic disease burdens are quantified years later, and at-risk populations in data-sparse regions remain unaddressed. Planetary geospatial foundation models complement existing epidemiological workflows to provide operational improvements, encoding multimodal search, mobility, and environmental signals into generalizable place representations. As illustrations of this complementarity, we present independent global health case studies of Google Earth AI's Population Dynamics Foundation Model (PDFM) -- a foundation model for geospatial inference -- across four domains (vaccine-preventable, communicable, noncommunicable, maternal mental health), five tasks (spatial extrapolation, interpolation/nowcasting, probabilistic forecasting, prospective forecasting, risk stratification), and four countries (USA, Canada, Mexico, and the Democratic Republic of the Congo). Across these case studies, PDFM addresses critical surveillance gaps across domains: improving US-Canada border MMR vaccination coverage predictions by capturing cross-border behavioral spillovers domestic models miss; nowcasting cardiovascular disease to accelerate data availability; enhancing short-term municipal Mexican dengue forecasts for timely outbreak vector control; improving forecasts of cholera hotspots; and adding a transferable signal to individual-level postpartum-depression risk prediction in US states the model had never seen, while not replacing individual socioeconomic data or closing demographic screening gaps. Together, these results showcase capabilities of geospatial foundation models for public health surveillance.

Publication: 41 pages, 7 figures Category: Method A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation http://arxiv.org/abs/2610.05625v1 2026-10-04T00:00:00Z 2026-10-04T00:00:00Z Hind Yousif Alhammadi Isam Mashhour Al Jawarneh

This paper presents the workflow for building an automated ArcGIS Pro tool using ArcPy to extract the Land Surface Temperature (LST) from Landsat 7, 8 and 9 datasets. The tool eliminates the need for manual band selection and repetitive raster computations by automating the multi-step workflow of radiometric calibration, NDVI-based emissivity correction, and thermal conversion. In addition to supporting batch and single-scene processing, the tool has an optional Large Language Model (LLM) for statistical result interpretation and reporting. Depending on batch size, the tool reduced the processing time from around 11-58 minutes when done manually to around 4-11 minutes using the tool. We tested the tool with data from Ras Al Khaimah (RAK) in the UAE, and the LST obtained for Ras Al Khaimah ranged from approximately 25C to 50C, demonstrating an accurate LST mapping compatible with the weather conditions of RAK. In summary, our tool reduces human errors and improves processing accuracy and efficiency for thermal and environmental remote sensing applications, in addition to providing an interactive LLM-based interface for result interpretation.

Category: Method LEON: Location Embeddings from OSM Neighborhoods via Hexagonal Graph Masked Autoencoders http://arxiv.org/abs/2610.05497v1 2026-10-04T00:00:00Z 2026-10-04T00:00:00Z Szymon Soltysiak Radoslaw Malek Jedrzej Kusnierz Milosz Chojecki Piotr Szymanski Aleksandra Kawala-Sterniuk

Geographic information systems increasingly rely on sophisticated spatial representation learning techniques to extract meaningful patterns from complex geospatial data. This paper introduces LEON, a novel self-supervised framework that adapts Graph Masked Autoencoders (GraphMAE) for geospatial region representation learning. Our method leverages the inherent spatial structure of geographic data by constructing hexagonal grid graphs using H3 indexing and applying masked autoencoding techniques to learn robust spatial embeddings from OpenStreetMap (OSM) amenity distribution patterns. We evaluate LEON on multiple real-world datasets including EuroSAT satellite imagery classification and various geographic prediction tasks (housing prices, crime prediction, and urban analytics). Experimental results demonstrate that LEON achieves significant improvements in spatial understanding, with up to 1.87% accuracy improvement on EuroSAT classification and consistent performance gains across geographic prediction benchmarks. The learned embeddings exhibit highly structured and distinct properties, making them particularly suitable for downstream spatial analysis tasks. Our findings suggest that self-supervised learning provides an effective paradigm for geospatial region representation learning using widely available crowdsourced data.

Publication: Advances in Computational Collective Intelligence (ICCCI 2026) Category: Method Tasks: CLS RSure-Agent: Reliable Use of Tool Observations for Remote Sensing Agents http://arxiv.org/abs/2610.04836v1 2026-10-04T00:00:00Z 2026-10-04T00:00:00Z Fuyuan Liu Nayu Liu Wenhao Yu Peijin Wang Yingchao Feng Fanglong Yao Liang Wan Wei Feng

Remote sensing agents rely on perception, measurement, and raster analysis tools to solve Earth observation tasks. We refer to their judgments and quantitative results about ground objects as tool observations. However, these observations are subject to substantial uncertainty and may be incorrect even when the tools execute successfully. When agents accept incorrect observations, the errors can propagate through subsequent reasoning and cause task failure. We analyze 1,229 execution trajectories across three remote sensing agent benchmarks. On each benchmark, at least 88.1% of tasks depend on tool observations. Among these tasks, at least 22.7% contain incorrect observations despite successful tool execution. These errors propagate to the final answer in at least 82.0% of affected tasks on each benchmark. To address this problem, we propose RSure-Agent, a framework for verifying tool observations and limiting error propagation. We introduce a verifiable observation protocol that requires tools to return process evidence for the agent to verify their observations. We also construct a task-tool reliability prior from offline task feedback. The prior summarizes each tool configuration's past performance across task types and provides a task-specific reference for verification. Using process evidence and this prior, RSure-Agent decides whether to accept an observation, request additional evidence, or reject it. We evaluate RSure-Agent on EarthBench, ThinkGeo, TerraLogic, and CHOICE-420. Across the three agent benchmarks, RSure-Agent reduces the error propagation rate by 21.3 to 25.9 percentage points relative to the base configuration with verification and the prior disabled. On CHOICE-420, it improves overall accuracy over direct answering by 5.71 percentage points on average across 11 backbone models. On EarthBench, it reduces the tool-call ratio by 25.9% relative to Earth-Agent.

Publication: The demo is available at https://github.com/airs101/RSure-Agent (code will be released for further research) Category: Method A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images http://arxiv.org/abs/2610.04300v1 2026-10-03T00:00:00Z 2026-10-03T00:00:00Z Jianzheng Wang Huan Ni Xiaonan Niu Danfeng Hong Haiyan Guan

The semantic information of objects in remote sensing images is typically invariant to geometric transformations from the dihedral group D4. However, SAM3-based open-vocabulary semantic segmentation (OVSS) methods often exhibit inconsistent responses to different geometric transformations. To exploit this property and improve the stability of OVSS for remote sensing images, we propose a feature-adaptive manifold repair method based on dihedral-group geometric transformations. First, we introduce multi-scale harmonic-guided D4 view selection (MH-D4VS) to select complementary candidate views from a set of geometrically transformed views. Next, we propose original-view-anchored adaptive manifold repair (OAMR), which uses the original view as an anchor and reliable cross-view information to selectively repair locally unreliable visual features. Finally, we develop pixel decoder test-time adaptation (PD-TTA) for SAM3, which fine-tunes only the parameters of the GroupNorm layers online during inference, thereby enhancing the model's ability to adapt to sample-level distribution shifts. Experimental results show that the proposed method achieves an average mIoU of 55.6% across eight remote sensing semantic segmentation benchmarks and delivers consistent performance improvements under different SAM3-based inference frameworks.

Category: Method Tasks: SEG On-Board Anomaly Detection for Efficient Marine Environmental Monitoring http://arxiv.org/abs/2610.03649v1 2026-10-02T00:00:00Z 2026-10-02T00:00:00Z Thomas Goudemant Clotilde Szywala Benjamin Francesconi Michelle Aubrun Yves Bobichon Marjorie Bellizzi Adrien Girard

Marine ecosystems are impacted by various threats such as oil spills, algal blooms, and sediment floods, which disrupt habitats, wildlife, and human activities. Advances in satellite imagery and Artificial Intelligence (AI) have enhanced our capabilities for early detection and mitigation of such hazards. In this paper, we propose a marine event detection pipeline for Earth observation satellites equipped with multi- or hyperspectral sensors. Our approach includes a self-supervised neural network encoder that compresses satellite images into a reduced latent space, enabling efficient onboard processing. A machine learning anomaly detection model identifies deviations from normal sea patterns to detect environmental anomalies. We compare its performance against traditional algorithms such as Isolation Forest, One-Class Support Vector Machine and Local Outlier Factors. Our lightweight, resource-efficient pipeline is optimized for deployment on satellites with limited computational resources, ranging from embedded CPUs to AI hardware accelerators. By prioritizing the transmission of critical information, our solution enhances system responsiveness and optimizes satellite communication bandwidth. Demonstrated through current integration across multiple missions, including European Space Agency's (ESA) Phisat-2 mission and Microsoft/Thales Alenia Space IMAGIN-e mission, our pipeline aims to improve marine environmental monitoring by providing timely alerts and efficient data reduction.

Publication: 8 pages, 3 figures Category: Method A Two-Stage Cascade for Near-Real-Time Forest Anomaly Detection from Sentinel-1 SAR Time Series http://arxiv.org/abs/2610.02763v1 2026-10-02T00:00:00Z 2026-10-02T00:00:00Z Pann Thinzar Seint Subas Chhatkuli Bryan Atwood

Tropical forest monitoring is essential for global climate stability and biodiversity preservation. To address the urgent need for rapid, reliable detection of forest loss which is essential for timely intervention against illegal logging, supply chain transparency, land-use governance and carbon market standards, we introduce a two-stage statistics-encoder cascade for near-real-time anomaly detection using Sentinel-1 time series. Our system is designed to overcome two fundamental challenges in remote sensing: the cloud-cover limitations that restrict optical monitoring and seasonal backscatter variation that causes SAR systems to mistake natural moisture changes for forest loss. The architecture integrates two distinct analytical engines to ensure high-fidelity detection: (1) an adaptive, robust-statistics z-score test on co-registered Sentinel-1 VH backscatter, same-season historical baseline and (2) a learned confirmation gate based on the latent-space structural similarity (SSIM) of a convolutional autoencoder trained on stable-forest patches. A candidate disturbance is confirmed as an alert only when both stages agree, and is assigned a confidence score and a Low/Medium/High risk tier from its repeat-occurrence history. The system produces per-alert auditable confidence scores and area-in-hectares estimates directly compatible with Monitoring, Reporting and Verification (MRV) workflows, sustainable forestry management, operational field checks and environmental risk assessments. Beyond its primary application, the model's flexibility allows for critical environmental applications ranging from selective logging to large-scale agricultural encroachment mapping, flood mapping and so on.

Category: Method Why Convolution Still Matters: Evaluating Inductive Biases in Cryospheric Image Classification http://arxiv.org/abs/2610.04073v1 2026-10-02T00:00:00Z 2026-10-02T00:00:00Z Chhaya Kulkarni Emam Hossain

Recent advances in attention-based deep learning have motivated their adoption for remote sensing image classification; however, their benefits for cryospheric imagery, where surface states are dominated by fine-grained textures and class imbalance, remain unclear. In this work, we revisit a benchmark Greenland Ice Sheet image dataset, previously shown to favor convolutional neural networks (CNNs), to examine whether modern attention-based and hybrid architectures improve class-wise reliability. We conduct a controlled comparison between a classical CNN (AlexNet), a modern CNN (ConvNeXt-Tiny), a pure attention-based model (Swin-Tiny), and a hybrid convolution-attention model (CoAtNet-0) under identical training and evaluation protocols. Results show that AlexNet achieves the highest accuracy and the strongest balanced performance as measured by macro-averaged F1, while ConvNeXt-Tiny exhibits the highest macro-averaged AUC, indicating strong class separability but less consistent final decision quality. Class-wise analysis reveals that hybrid architectures improve recall for rare and structurally distinct surface classes, whereas convolutional models remain more reliable for texture-dominated categories. These findings highlight the importance of aligning architectural inductive bias with cryospheric data characteristics and suggest that increased model complexity does not necessarily translate to improved reliability for ice-sheet surface classification.

Publication: IEEE IGARSS 2026 Category: Method Tasks: CLS Harnessing Domain Specialists in Multimodal Mixture-of-Experts for Efficient Adaptation http://arxiv.org/abs/2610.02123v1 2026-10-01T00:00:00Z 2026-10-01T00:00:00Z Damiano Marsili Raphi Kang Aditya Mehta Pietro Perona Georgia Gkioxari

Mixture-of-Experts (MoE) architectures scale model capacity through sparse computation, routing each token through only a small subset of experts. In this work, we explore whether this sparsity gives rise to emergent intrinsic organization in multimodal MoEs. We find that experts develop strong semantic specialization across modalities and domains despite not being explicitly trained for modularity. Building on this structure, we introduce ExpertLens, a data-free method that identifies domain-specialized experts directly from pretrained model weights by decoding router weights into semantically meaningful vocabulary tokens. We leverage this specialization for efficient multimodal adaptation by selectively fine-tuning experts relevant to a target domain. Across math, medical, and remote sensing tasks, ExpertLens matches or surpasses full fine-tuning while updating only 21.7 - 47.0% of model parameters and achieving a 4.0x average training speedup, and outperforms LoRA in both adaptation performance and training efficiency. These results show that sparsity introduced for efficiency can give rise to semantic modularity that is directly useful for efficient adaptation.

Publication: Project page: https://glab-caltech.github.io/expertlens/ Category: Method A two-stage approach to satellite constellation optimization: classical and QUBO formulations http://arxiv.org/abs/2610.01885v1 2026-10-01T00:00:00Z 2026-10-01T00:00:00Z Carlo Novara

The design of satellite constellations for Earth observation requires balancing spatial coverage, revisit time, cost, and operational complexity. This paper considers the problem of designing the orbits of a given number of Low Earth Orbit (LEO) or Very Low Earth Orbit (VLEO) satellites to maximize the spatial and temporal resolution achieved over a prescribed set of ground targets. This kind of problem is inherently nonconvex and possibly combinatorial, making its solution computationally demanding for large constellations and target sets. To address this challenge, we propose a two-stage optimization strategy that separates spatial-coverage design from temporal-resolution optimization, thereby reducing the complexity of the overall problem. Two variants of the method are developed. The first employs continuous decision variables during the spatial-optimization stage, whereas the second discretizes these variables and reformulates the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The latter formulation enables the use of efficient classical QUBO solvers and is directly compatible with quantum-annealing hardware. The proposed framework provides a scalable approach to the design of heterogeneous LEO and VLEO Earth-observation constellations and establishes a pathway for exploiting emerging quantum-optimization technologies in satellite mission design. Preliminary simulation results are presented to demonstrate the effectiveness of the strategy.

Publication: 15 pages, 1 figure Category: Method From Pixels to Policy: A Multi-Agent System for Intervention and Geo-Spatial Decision Support http://arxiv.org/abs/2610.01870v1 2026-10-01T00:00:00Z 2026-10-01T00:00:00Z Hosam Elgendy Utkarsh Mall

Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventions remains costly, time-consuming, and often impractical. Existing geospatial vision methods largely focus on monitoring urban indicators from aerial and street-view imagery, rather than proposing interventions and estimating their effects on such indicators. Moving beyond recognition, we introduce the problem of discovering interventions that improve target indicators for a given aerial or street-view image. We argue that a black-box indicator model, combined with a generative editing model, can serve as an implicit digital twin for testing intervention hypotheses. We present VIDA-Geo , a multi-agent system that explores this intervention space by coordinating segmentation, diffusion-based inpainting, and indicator scoring models to produce interventions that are both perceptually realistic and aligned with real-world policies. We evaluate our system on 8 indicators across aerial and street-view imagery, measuring changes in factors such as perceived safety and greenery. Our approach outperforms existing baselines in many cases, achieving up to 2X higher perceptual quality and policy alignment scores. Finally, our model provides users with multiple candidate interventions, supporting an expert city-planner-in-the-loop workflow.

Category: Method CoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement http://arxiv.org/abs/2610.01710v1 2026-10-01T00:00:00Z 2026-10-01T00:00:00Z Dongwei Sun Yujie Zhang Bowen Yao Pei Liu Jing Yao Xiangyong Cao

Visual grounding localizes an object described by language with a bounding box. Most multimodal grounding models compress target identification, spatial reasoning, and boundary estimation into one terminal prediction. Free-form rationales make reasoning linguistically explicit but do not necessarily expose measurable, editable spatial states. Intermediate localization errors are therefore difficult to diagnose and correct, allowing incorrect region choices and imprecise boundaries to persist in the final box. We introduce CoEvolve, a construct-to-edit framework that separates grounding into explicit state construction and state editing. Region-Evolution Reinforcement (RER) organizes grounding analysis into a progressive semantic--spatial trajectory, with each reasoning step committing to an explicit candidate region. Bidirectional Denoising Refiner (BDR) treats the reasoning text as fixed semantic context and refines the trajectory's coordinate fields through bidirectional same-position reconstruction. Geometry- and behavior-level objectives provide target geometry and edit-preference signals for consolidating reliable candidates, preserving accurate inputs, or correcting toward annotations. Evaluations cover natural-image and remote-sensing grounding. With a 9B backbone, CoEvolve rivals models up to 241B parameters in grounding accuracy. Under controlled corruption, a single BDR pass improves mean box overlap by over 27 percentage points, demonstrating strong recovery from substantial localization errors. State-source comparisons further support the complementarity of explicit state construction and source-matched editing. The project is at https://sundongwei.github.io/CoEvolve_Project/.

Category: Method Tasks: VG A PyTorch Library for Hyperspectral Image Models: Technical Report http://arxiv.org/abs/2609.39871v2 2026-09-30T00:00:00Z 2026-09-30T00:00:00Z Tanishq Rachamalla Aryan Das Srishti Kaushik Swalpa Kumar Roy

Hyperspectral remote sensing has advanced across diverse deep learning paradigms, including spectral spatial CNNs, Vision Transformers, Mamba, graph neural networks, Kolmogorov Arnold networks, and self supervised masked autoencoding. Yet progress remains hindered by fragmented repositories, incompatible tensor conventions, and non standardized evaluation. Hyperspectral Image Models addresses these challenges through a modular framework unifying 55 representative models across six paradigms with a common registry, automatic 4D/5D tensor adaptation, and standardized constructors. It integrates 24 benchmark scenes from Airborne, Spaceborne, UAV, and Mars CRISM sensors, with caching, label remapping, PCA, explicit band selection or raw spectra, optional spatial max pooling, and arbitrary PxP patch extraction. To prevent inflated accuracy from overlapping windows, it supports class balanced random partitioning and spatially disjoint regional blocking with Chebyshev guard bands that eliminate train test pixel overlap. Experiments use a single config.yaml with deterministic seeds and complete provenance, generating LaTeX benchmark tables and classification maps. Across 1,320 model scene evaluations and 6,600 seeded runs, scene difficulty dominates architecture, with mean accuracy ranging from 96.40% on Botswana to 56.70% on Houston 2018, versus a 15 point spread across paradigm means. No paradigm universally dominates, while sub 1 M parameter models can match architectures two orders of magnitude larger. Code is publicly available at https://github.com/Tanishq251/Hyperspectral-Image-Models.

Code: <a href="https://github.com/Tanishq251/Hyperspectral-Image-Models">https://github.com/Tanishq251/Hyperspectral-Image-Models</a> Publication: Documentation and benchmark library for hyperspectral image models Category: Method Tasks: CLS COBICount: Separating Object and Background Responses for Remote Sensing Object Counting Without Training on Target Data http://arxiv.org/abs/2609.39366v1 2026-09-30T00:00:00Z 2026-09-30T00:00:00Z Junjing Zheng Zhiyi Zhou Ningrui Yang Hongying Meng

Remote sensing object counting estimates how many buildings, vehicles, or ships appear in overhead images. Most supervised counters predict a density map, whose sum gives the object count, and assume similar categories, sizes, and backgrounds. Applying them across regions, sensors, or categories often requires target data or further training, which may be costly or unavailable. We study source-only counting. Training for the counting task and model selection use one group of images that shares an object category and similar imaging conditions, with one point marking each object. Target images and information remain unavailable until the model is fixed. This reduces data preparation but makes transfer harder. A model trained on one source may place high density values, called responses, on real objects and repeated background structures. Road edges, parking grids, roof boundaries, and water boundaries may then be counted as objects, creating candidate origin ambiguity. COBICount separates response generation, acceptance, and background suppression. Candidate Evidence (CE) generates possible responses. Candidate Acceptance (CA) keeps compact responses centered on objects. Bias Isolation (BI) reduces responses associated with repeated background structures. Their outputs form the final density map. Trained on RSOC Building and evaluated directly on DOTA Large Vehicle, Small Vehicle, and Ship, COBICount achieves the lowest mean absolute error (MAE) averaged over the target domains among the compared methods, 174.132. It uses 5.07 million parameters and 17.41 billion floating point operations for a 512x512 input. COBICount improves transfer without target data or training for each target. The code will be available at: https://github.com/yixuxi22/COBICount.

Code: <a href="https://github.com/yixuxi22/COBICount">https://github.com/yixuxi22/COBICount</a> Publication: 19 pages, 7 figures Category: Method RW-Flow: One-Step Generation on Compact Manifolds via Wasserstein Gradient Flows http://arxiv.org/abs/2609.39271v2 2026-09-30T00:00:00Z 2026-09-30T00:00:00Z Ualibyek Nurgulan Seungwoo Yoo Prin Phunyaphibarn Minhyuk Sung

Manifold-valued data, and consequently the distributions they induce, are prevalent across many domains, ranging from the locations of geospatial events, such as earthquakes, to biomolecular torsion angles that encode information about three-dimensional structure. While diffusion and flow-based generative models have been successfully extended to compact manifolds, sampling typically requires tens or hundreds of sequential network evaluations. We introduce RW-Flow, a theoretically grounded framework for learning one-step generative models on compact manifolds via Wasserstein gradient flows. The main challenge is identifiability: driving the velocity field to zero should guarantee that the model distribution matches the target distribution. We establish a necessary and sufficient condition for identifiability on compact, connected Riemannian manifolds. We specifically show that, for a symmetric, Lipschitz-continuous cost function, the velocity field induced by the Sinkhorn divergence is identifiable if and only if the associated Gibbs kernel is nondegenerate. This characterization provides a general principle for designing identifiable costs on compact manifolds. It also reveals that the squared geodesic distance, the natural manifold analogue of the squared Euclidean distance, does not always guarantee identifiability. Across benchmarks involving geospatial events, protein side chain torsion angles, RNA backbone torsion angles, and general manifolds discretized as triangular meshes, RW-Flow outperforms existing one-step methods in nearly all settings under fair comparison conditions.

Publication: 27 pages, ICLR, 2 figures, 11 tables Category: Method FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification http://arxiv.org/abs/2610.00693v1 2026-09-30T00:00:00Z 2026-09-30T00:00:00Z Barış Büyüktaş Begüm Demir

Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentralized RS image archives without requiring direct access to local data. However, FL performance significantly degrades when the data distributions between clients are heterogeneous, which often occurs due to geographical differences, seasonal changes, and varying image acquisition and atmospheric conditions. To address this challenge, in this letter, we propose a novel personalized FL framework (denoted as FedMAD) for RS image classification problems. The proposed framework separates globally shared representation parameters from client-specific adaptation parameters to preserve client-specific features while maintaining globally transferable representations. This is achieved by integrating lightweight modulation modules and local batch normalization layers into the backbone network. Although globally shared parameters are collaboratively optimized between clients, client-specific parameters remain local to preserve domain-specific feature characteristics. In addition, FedMAD introduces a modulation-aware directional aggregation strategy that dynamically adjusts the importance of aggregation for each client according to the alignment of local modulation updates. This allows the global optimization process to suppress conflicting client updates originating from heterogeneous data distributions while enhancing the contribution of clients with consistent adaptation behaviors. The experimental results obtained on the BigEarthNet-S2 and EuroSAT datasets demonstrate the effectiveness of FedMAD compared to state-of-the-art FL algorithms under heterogeneous RS data distributions. The code of the proposed framework will be publicly available at https://git.tu-berlin.de/rsim/fedmad.

Category: Method Tasks: CLS