Latest Remote Sensing & Geospatial Research Papers

The newest Remote Sensing & Geospatial papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Remote Sensing & Geospatial so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• SemDINO: A DINOv3-Driven Network for Cross-Temporal Semantic Alignment in Change Detection
  Xinyu Tong, Meihua Zhou, Jinxiao Sun, Yingjie Tang et al. · arXiv · Jun 8, 2026

  Semantic change detection (SCD) aims to simultaneously locate land-cover changes and identify semantic categories before and after transition. However, existing methods suffer from insufficient cross-temporal alignment, weak multi-scale rep…

• Beyond Backscatter: InSAR coherence from detected SAR images
  Francescopaolo Sica, Andrea Pulella, Michael Schmitt · arXiv · Jun 5, 2026

  In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistration. A Residual U-Net is trained using coherence maps derived from precisely coregistere…

• In-Context Multiple Instance Learning
  Alexander Möllers, Marvin Sextro, Julius Hense, Gabriel Dernbach et al. · arXiv · Jun 4, 2026

  Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery. Nevertheless, existi…

• EgoExoMem: Cross-View Memory Reasoning over Synchronized Egocentric and Exocentric Videos
  Ruiping Liu, Junwei Zheng, Yufan Chen, Di Wen et al. · arXiv · May 18, 2026

  Egocentric memory is widely used in embodied intelligence, but it may be insufficient for comprehensive spatial-temporal reasoning. Inspired by human recall from both field and observer perspectives, we introduce EgoExoMem, the first benchm…

• Better Together: Evaluating the Complementarity of Earth Embedding Models
  Thijs L van der Plas, Jacob JW Bakermans, Vishal Nedungadi, Gabrielė Tijūnaitytė et al. · arXiv · May 18, 2026

  Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically evaluated in isolation, comparing the downstream task performance across different Earth e…

• Seeing Across Skies and Streets: Feedforward 3D Reconstruction from Satellite, Drone, and Ground Images
  Qiwei Wang, Zhongyao Tuo, Xianghui Ze, Yujiao Shi · arXiv · May 8, 2026

  Cross-view localization classically asks: where does this ground image lie on the satellite tile? Existing methods are typically limited to 3-DoF estimates -- an $(x,y)$ position and a yaw angle -- because nadir satellite imagery provides n…

• A unified Benchmark for Multi-Frame Image Restoration under Severe Refractive Warping
  Maxim V. Shugaev, Md Reshad Ul Hoque, Bridget Kennedy, Joseph T. Riley et al. · arXiv · May 6, 2026

  Video sequence capturing through refractive dynamic media, such as a turbulent air or water surface, often suffer from severe geometric distortions and temporal instability. While recent advances address mild atmospheric turbulence, no exis…

• Label-Efficient School Detection from Aerial Imagery via Weakly Supervised Pretraining and Fine-Tuning
  Zakarya Elmimouni, Fares Fourati, Mohamed-Slim Alouini · arXiv · May 5, 2026

  Accurate school detection is essential for supporting education initiatives, including infrastructure planning and expanding internet connectivity to underserved areas. However, many regions around the world face challenges due to outdated,…

• Quantifying the human visual exposome with vision language models
  Christian Rominger, Andreas R. Schwerdtfeger, Malay Gaherwar Singh, Dimitri Khudyakow et al. · arXiv · May 5, 2026

  The visual environment is a fundamental yet unquantified determinant of mental health. While the concept of the environmental exposome is well established, current methods rely on coarse geospatial proxies or biased self reports, failing to…

• Unified Map Prior Encoder for Mapping and Planning
  Zongzheng Zhang, Sizhe Zou, Guantian Zheng, Zhenxin Zhu et al. · arXiv · May 4, 2026

  Online mapping and end-to-end (E2E) planning in autonomous driving remain largely sensor-centric, leaving rich map priors, including HD/SD vector maps, rasterized SD maps, and satellite imagery, underused because of heterogeneity, pose drif…

• Seeing Realism from Simulation: Efficient Video Transfer for Vision-Language-Action Data Augmentation
  Chenyu Hui, Xiaodi Huang, Siyu Xu, Yunke Wang et al. · arXiv · May 4, 2026

  Vision-language-action (VLA) models typically rely on large-scale real-world videos, whereas simulated data, despite being inexpensive and highly parallelizable to collect, often suffers from a substantial visual domain gap and limited envi…

• Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data
  Zahid Hassan Tushar, Sanjay Purushotham · arXiv · May 1, 2026

  Aerosol Optical Depth (AOD) retrieval is essential for Earth observation, supporting applications from air quality monitoring to climate studies. Conventional physics-based AOD retrieval methods formulate the problem as a pixel-wise inversi…

• MemOVCD: Training-Free Open-Vocabulary Change Detection via Cross-Temporal Memory Reasoning and Global-Local Adaptive Rectification
  Zuzheng Kuang, Honghao Chang, Boqiang Liang, Haoqian Wang et al. · arXiv · Apr 29, 2026

  Open-vocabulary change detection aims to identify semantic changes in bi-temporal remote sensing images without predefined categories. Recent methods combine foundation models such as SAM, DINO and CLIP, but typically process each timestamp…

• Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles
  Minh-Khoa Le-Phan, Minh-Hoang Le, Trong-Le Do, Minh-Triet Tran · arXiv · Apr 28, 2026

  Current deepfake detection models achieve state-of-the-art performance on pristine academic datasets but suffer severe spatial attention drift under real-world compound degradations, such as blurring and severe lossy compression. To address…

• Quantum-Inspired Robust and Scalable SAR Object Classification
  Maximilian Scharf, Marco Trenti, Felix Bock, Padraig Davidson et al. · arXiv · Apr 28, 2026

  SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these models on edge devices, such as drones and military aircraft,…

• SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery
  Safouane El Ghazouali, Nicola Venturi, Michael Rueegsegger, Umberto Michelucci · arXiv · Apr 23, 2026

  Recent advances in deep learning for remote sensing rely heavily on large annotated datasets, yet acquiring high-quality ground truth for geometric, radiometric, and multi-domain tasks remains costly and often infeasible. In particular, the…

• EF-YOLO: Detecting Small Targets in Early-Stage Agricultural Fires via UAV-Based Remote Sensing
  Jun Tao, Zhihan Wang, Jianqiu Wu, Yuan Li et al. · Remote Sensing · Apr 9, 2026

  Early detection of agricultural fires with Unmanned Aerial Vehicles (UAVs) is important for environmental safety, yet it remains difficult because ignition cues are extremely small, smoke patterns vary widely, and farmland scenes often cont…

• Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection
  Xinbin Yuan, Zhaohui Zheng, Yuxuan Li, Xialei Liu et al. · Proceedings of the AAAI Con... · Mar 14, 2026

  In this paper, we show that current approaches using large square kernels or transformer-based global modeling aggregate contextual information uniformly across spatial dimensions, leading to feature dilution and localization errors for elo…

• Tree species–specific forest canopy cover loss in Germany (2018–2024): A national-scale remote sensing assessment
  Marco Wegler, Frank Thonfeld, Sarah Asam, Patrick Kacic et al. · Forest Ecology and Management · Mar 12, 2026

  Effective forest management and climate adaptation require a detailed understanding of tree species-specific disturbance dynamics. In recent years forest disturbances in Central Europe have intensified, driven by rising temperatures and rec…

• Integrating Computer Vision and GIS for Large-Scale Morphological Mapping and Driving Force Analysis of Vernacular Courtyard Dwellings
  Lihua Liang, Xiaodong Li, Shutong Liu, Zhenhao Guo et al. · Buildings · Mar 11, 2026

  This study develops and applies an integrated methodology that combines deep learning-based computer vision and spatial statistics to automate the large-scale identification and analysis of morphological features in vernacular courtyard dwe…

• FATSNet: transformer-based skip network with frequency attention for remote sensing image super-resolution
  Yan Huo, Shuang Gang, Xiao Xiao, Bo Fu et al. · Complex & Intelligent Systems · Mar 5, 2026

  The growing scope of image degradation encountered in remote sensing detection has sparked significant interest in the application of deep learning methods. To address the challenges posed by high-frequency signal loss and structural distor…

• ICL Characterization of Geo-Foundation Models
  Mosab Hawarey · OpenAlex · Feb 28, 2026

  Geo-foundation models (GeoFMs) have emerged as powerful tools for remote sensing, yet there exists no theoretical framework characterizing which downstream tasks admit efficient few-shot adaptation. We address this gap by applying the in-co…

• Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation
  Feng Gao, Miao Fu, Jingchao Cao, Junyu Dong et al. · IEEE Transactions on Geoscience and Remote Sensing · Apr 3, 2025

  Semantic segmentation of high-resolution remote sensing images plays a crucial role in land-use monitoring and urban planning. Recent remarkable progress in deep learning-based methods makes it possible to generate satisfactory segmentation…

• XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?
  Fengxiang Wang, Hongzhen Wang, Mingshuo Chen, Di Wang et al. · Computer Vision and Pattern Recognition · Mar 31, 2025

  The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations, and indicate future research directions. However, this is challe…

• Adaptive Rectangular Convolution for Remote Sensing Pansharpening
  Xueyang Wang, Zhixin Zheng, Jiandong Shao, Yule Duan et al. · Computer Vision and Pattern Recognition · Mar 1, 2025

  Recent advancements in convolutional neural network (CNN)-based techniques for remote sensing pansharpening have markedly enhanced image quality. However, conventional convolutional modules in these methods have two critical drawbacks. Firs…

• A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection
  Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang et al. · Remote Sensing · Feb 28, 2025

  In recent years, numerous advanced lightweight models have been proposed for salient object detection (SOD) in optical remote sensing images (ORSI). However, most methods still face challenges such as performance limitations and imbalances …

• DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Remote Sensing Change Detection
  W. G. C. Bandara, Nithin Gopalakrishnan Nair, Vishal M. Patel · IEEE Workshop/Winter Conference on Applications of Computer Vision · Feb 26, 2025

  Remote sensing change detection is crucial for understanding the dynamics of our planet's surface, facilitating the monitoring of environmental changes, evaluating human impact, predicting future trends, and supporting decision-making. In t…

• Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
  Gabriel Tseng, A. Fuller, Marlena Reil, Henry Herzog et al. · International Conference on Machine Learning · Feb 13, 2025

  We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, elevation, weather, pseudo-labels, and more - across space and time. These inputs are useful for div…

• Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey
  Elias Dritsas, M. Trigka · Remote Sensing · Feb 6, 2025

  The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelli…

• A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches
  Tania Islam, E. B. Zeleke, Mahmud Afroz, A. Melesse · Remote Sensing · Feb 3, 2025

  Climate change has led to an increase in global temperature and frequent intense precipitation, resulting in a rise in severe and intense urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface…