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

• Spatial Patterns and Dynamics of Agricultural Land Use: A Geographical Analysis
  Anjali Harshavardhan Thorat · Zenodo (CERN European Organ... · Nov 30, 2026

  This study examines the spatial patterns and dynamics of agricultural land use through a geographical perspective, emphasizing the relationship between land use, land cover, and socio-economic as well as environmental factors. The research …

• Spatial Patterns and Dynamics of Agricultural Land Use: A Geographical Analysis
  Anjali Harshavardhan Thorat · Zenodo (CERN European Organ... · Nov 30, 2026

  This study examines the spatial patterns and dynamics of agricultural land use through a geographical perspective, emphasizing the relationship between land use, land cover, and socio-economic as well as environmental factors. The research …

• Climate-Based Modeling of Invasive Species Impact on Urban Green Infrastructure Using CLIMEX and Remote Sensing Data
  A. Suresh kumar, C. Rajan · Journal of Smart Infrastruc... · Nov 6, 2026

  The pace at which invasive alien species are established and spread is becoming a threat, especially to urban ecosystems, due, in part, to the variability of climates, urban growth, and fragmentation of habitats. These biological invasions …

• A Unified Benchmark for Aerial-Enhanced Autonomous Driving: The KITScenes Aerial Dataset
  Daniel Lengerer, Richard Schwarzkopf, Jonas Merkert, Fabian Immel et al. · arXiv · Oct 29, 2026

  Aerial imagery provides complementary spatial context for autonomous driving by exposing road structure beyond the immediate perception range of onboard sensors. Yet map construction from aerial imagery, aerial-assisted online HD mapping, a…

• Soil Moisture Estimation in Precision Agriculture: A Knowledge Distillation Approach
  Mohammed Rakib, Vrinda Nagula, Arunkumar Bagavathi · arXiv · Oct 29, 2026

  Accurate soil moisture estimation is critical for precision agriculture, influencing irrigation, fertilization, and harvesting strategies. Traditional methods primarily rely on meteorological and soil property data, while recent approaches …

• NeurEO: dissecting Earth observation embeddings with computational neuroscience
  Thijs L van der Plas, Jacob JW Bakermans, Ioannis N Athanasiadis · arXiv · Oct 29, 2026

  Earth embeddings are compressed representations of rich Earth observation data that enable downstream models to tackle a wide range of complex geospatial tasks. Yet, this increase in performance comes at the cost of decreased interpretabili…

• SynthFaces: Balanced Large Scale Human Dataset
  Sergey Karpukhin, Denis Bobkov, Kirill Morozov, Aibek Alanov · arXiv · Oct 29, 2026

  The pursuit of fair and equitable AI in human-centric tasks is critically hindered by the inherent biases and privacy risks of real-world datasets, which offer imbalanced representations of identity and often contain sensitive personal info…

• Quantum-Inspired Convolutional Networks for High-Resolution Pseudo-Label Generation from Multimodal Earth Observation Data
  Abhishek Singh, Giovanni Cuozzo, Alexander Jacob, Claudia Notarnicola · arXiv · Oct 29, 2026

  Accurate land-cover mapping from multimodal Earth observation (EO) data remains a challenge in regions where high-resolution annotations are scarce or costly to obtain. This work introduces a quantum convolutional encoder-decoder network fo…

• GroundControl: Satellite-Conditioned Multi-View Diffusion for Reconstruction-Free Novel View Synthesis
  Akshay Krishnan, Matias Turkulainen, Jamie Wynn, Zawar Qureshi et al. · arXiv · Oct 27, 2026

  Multi-view or video diffusion models have recently emerged as state-of-the-art approaches for novel-view synthesis from sparse image collections. While they can produce high quality images, they tend to hallucinate unrealistic spatial layou…

• Learning to Look Beyond Screens: Transferable Visual Observation from GUI Grounding
  Ruilin Yao, Xinyu Qiu, Tianyu Zou, Yi Rong et al. · arXiv · Oct 27, 2026

  Visual perception is evolving from passive recognition toward active, task-oriented observation. We investigate GUI grounding as a scalable source of spatial supervision for learning observation behaviors that transfer beyond interfaces. We…

• Quantized Edge Inference of a Self-Supervised SAR-Optical Encoder on Commodity x86 Hardware
  Christian Sanchez-Leon, Dongming Chen · arXiv · Oct 27, 2026

  Self-supervised vision transformers are increasingly used as reusable encoders for Earth observation, but their inference cost on the hardware available in the field is rarely characterized. We deploy a pretrained SAR-optical masked-autoenc…

• Evaluating AlphaEarth Embeddings for Crop Classification: Separability, Temporal Dynamics, and Cross-Regional Transferability
  Priyanka Goyal, Maitreyee Sar, Adway Mitra, Manjira Sinha · arXiv · Oct 27, 2026

  Earth Observation (EO)-based agricultural monitoring is shifting from pixel-level spectral analysis toward representation-driven modeling, yet systematic evaluation of learned representations across spatial, temporal, and domain-generalizat…

• Efficient Remote Sensing Image-Text Retrieval via Interaction-Free Recall and Text-Guided Reranking
  Xi Chen, Xu Chen, Xiamgyang Jia, Wei Wang et al. · arXiv · Oct 27, 2026

  Remote sensing (RS) image-text retrieval plays a critical role in understanding massive RS imagery. However, the dense multi-object distribution and complex backgrounds in RS imagery make it difficult to simultaneously achieve fine-grained …

• VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains
  Richard Fechner, Peer Neubert · arXiv · Oct 26, 2026

  Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-of-the-art VPR and SLAM approaches excel in urban and feature-rich natural environments, the…

• Hyperspectral Unmixing using a Structured Linear Mixing Model with Additive Spectral Variability
  Salah Eddine Brezini, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra Benhalouche et al. · arXiv · Oct 26, 2026

  Linear hyperspectral unmixing aims at retrieving pure material spectra present in an observed scene and their associated abundance fractions in each pixel. In most practical situations, hyperspectral images are affected by spectral variabil…

• OBB-PBD: Extending Parallel Box Decoding to Oriented Bounding Boxes for Referring Expression Grounding in Aerial Imagery
  Chang Woo Song, Yeong Jun Koh · arXiv · Oct 26, 2026

  Referring expression grounding in overhead imagery requires oriented bounding boxes rather than horizontal ones, since aerial objects appear at arbitrary orientations. Most grounding models emit box coordinates sequentially; Parallel Box De…

• Shared-Factor-Conditioned Diffusion for Low-Rank Tensor Recovery
  Zhengyun Cheng, Guanwen Zhang, Wei Zhou · arXiv · Oct 26, 2026

  Tensor recovery spans heterogeneous data structures and observation processes, calling for a representation that can adapt across modalities and tasks. We propose DiffTR, an unsupervised framework that unifies tensor completion, denoising, …

• Accuracy and efficiency of compact foundation models for surface-water mapping during floods
  Parastoo Farajpoor, Mohammadreza Narimani · arXiv · Oct 26, 2026

  Pretrained Earth observation models may reduce the effort needed to map surface water, but their value depends on the alternative against which they are compared. We evaluated Prithvi-EO-2.0-Tiny using the 446 hand-labeled Sentinel-2 chips …

• CoDeRNet: Selective Cross-Task Routing under Heterogeneous Supervision for Change Detection and Captioning
  Eunki Cho, Hyeon Bae Kim, Seong Tae Kim · arXiv · Oct 26, 2026

  Change Detection & Captioning (CDC) aims to jointly localize changed regions and describe their semantic content from bi-temporal remote sensing images. While recent approaches have demonstrated the effectiveness of unified modeling, th…

• LoCA: Enabling Multi-Modality in Geospatial Location Encoders
  Panagiotis Koromilas, Eleanna Vali, Diogenis Antonopoulos, Eleni Saka et al. · arXiv · Oct 23, 2026

  Location encoders map geographic coordinates to high-dimensional descriptors through contrastive alignment with paired Earth Observation imagery. We prove that this construction is structurally limited: any Lipschitz location encoder produc…

• SCDM-HTD: Spectral-Wise Conditional Diffusion Model for Hyperspectral Target Detection
  Dunbin Shen, Wenfeng Kong, Jianjun Liu, Xiaorui Ma et al. · arXiv · Oct 23, 2026

  Hyperspectral target detection (HTD) aims to locate and recognize objects at a fine-grained scale, playing a pivotal role in remote sensing. Despite significant progress, current HTD methods primarily focus on feature comparison while negle…

• Reuse, Don't Retrain: Efficient, Modality-Versatile Geospatial Foundation Models through Feature Distillation
  Mario Hamon, Pierre Adorni, Minh-Tan Pham · arXiv · Oct 23, 2026

  Geospatial foundation models (GeoFMs) are costly to pretrain, yet model families often repeat this process across backbone scales and sensing modalities. We investigate feature distillation as a more efficient way to reuse existing represen…

• GeoLEON-CLIP: Graph-Anchored Multimodal Contrastive Learning for Geographic Region Representation
  Radoslaw Malek, Szymon Soltysiak, Aleksandra Kawala-Sterniuk, Piotr Szymanski · arXiv · Oct 23, 2026

  Geographic regions are multimodal: satellite imagery captures visual morphology, text conveys human-interpretable semantics, and map structures describe urban context. Existing geospatial contrastive methods typically align image-text or im…

• OlmoEarth v1.3: Embeddings
  Gabriel Tseng, Favyen Bastani, Patrick Alan Johnson, Yawen Zhang et al. · arXiv · Oct 23, 2026

  We make some OlmoEarth embeddings. To do this, we train a new model: OlmoEarth v1.3. OlmoEarth v1.3 is designed to condense multimodal, multitemporal remote sensing data into a single digestible 64 or 128-dimensional vector. OlmoEarth v1.3 …

• DS-BiCA-Net: Pair-Preserving Cross-Temporal Modeling for Remote Sensing Change Captioning
  Licheng Zhang, Naveed Akhtar, Siew-Kei Lam · arXiv · Oct 23, 2026

  Remote sensing image change captioning (RSICC) describes in natural language the semantic changes between two images of the same scene captured at different times. Previous methods commonly merge the pair during repeated processing or combi…

• Data-Driven Vision-Language Models for Remote Sensing: A Survey
  Yue Zhou, Shujun Zhao, Xue Yang, Ruigang Li et al. · arXiv · Oct 22, 2026

  During the deep learning era, innovations in remote sensing (RS) vision models primarily focused on optimizing network architectures for specific tasks and conducting end-to-end training. However, with the advent of the Vision-Language Mode…

• Multiclass Boundary Refinement with Neural Cellular Automata for Remote Sensing Land Cover Segmentation
  Pascoassis Souza Santos Meira, Gilson Antonio Giraldi, Rafael Duarte Coelho dos Santos · arXiv · Oct 21, 2026

  Semantic segmentation of remote sensing imagery often places class boundaries a few pixels away from where they truly lie, and this position error is concentrated in the thin band measured by Boundary IoU (BIoU). We treat boundary refinemen…

• Existence of Minimizers for the Gaussian Mumford-Shah Model via Covariance Floors on the Value Distribution
  Faian Conrad · arXiv · Oct 21, 2026

  The Gaussian Mumford-Shah model segments an image into regions by minimizing a continuous multivariate Gaussian negative log-likelihood term plus a perimeter penalty. It is used for scalar- and vector-valued images, in particular hyperspect…

• Compositionality with Primitives: A Unified View from Euclidean to Hyperbolic Vision-Language Models
  Puru Vaish, Meelad Dashti, Christoph Brune, Pascal Mettes et al. · arXiv · Oct 21, 2026

  Compositional zero-shot learning (CZSL) aims to recognize novel combinations of previously observed objects and attributes. Prior work showed that large-scale vision-language models encode decomposable semantic structures that can be recomp…

• Key Findings for Small UAS for Post-Disaster Damage Assessment from the CURSE'26 Exercise
  Thomas Manzini, Stephen Johnson, Priyankari Perali, David Merrick et al. · arXiv · Oct 21, 2026

  The CLARKE building and road damage assessment AI classifier was incorporated into the Catastrophic Unmanned Remote Sensing Exercise (CURSE) 2026, which trained 23 small uncrewed aerial systems (sUAS) teams from state and local agencies and…