The paper introduces O$^2$-VG, a unified framework for oriented object visual grounding in remote sensing images, comprising three complementary models: O$^2$-VG-Trans, a cross‑modality transformer; O$^2$-VG-Uni, which predicts universal oriented proposals; and O$^2$-VG-VLM, an autoregressive vision‑language model that generates oriented bounding boxes. It also presents DIOR‑R‑SVG, a new dataset containing image, expression, and oriented box triplets for training and evaluation. The framework demonstrates superior performance across multiple benchmarks and is supported by publicly available code.
By Zeyu Ding, Yong Zhou, Jiaqi Zhao, Wen-Liang Du, Xixi Li, Hancheng Zhu, Rui Yao, Abdulmotaleb El Saddik
The paper introduces RSMEB, a unified benchmark for remote‑sensing multimodal retrieval that evaluates both cross‑modal and interleaved retrieval across 21 tasks under a single ranking protocol. It also presents VLM2GeoVec, an instruction‑conditioned single‑encoder model that embeds image, text, bounding‑box, and geo‑coordinate tokens into one sequence and achieves state‑of‑the‑art performance on region‑caption, referring‑expression, and semantic geo‑aware retrieval while remaining competitive on conventional tasks. The authors provide code, checkpoints, and data on GitHub to facilitate reproducibility.
By Emanuel S\'anchez Aimar, Gulnaz Zhambulova, Fahad Shahbaz Khan, Yonghao Xu, Michael Felsberg
arXiv:2505.15147v3 Announce Type: replace
Abstract: Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface. Semantic segmentation (SS) of RSIs enables the...
By Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang
The paper introduces NWPU-Refer, the largest bilingual dataset for referring remote sensing image segmentation, featuring 15,003 high‑resolution images from over 30 countries and 49,745 annotated targets. It also proposes the Multi‑scale Referring Segmentation Network (MRSNet), which uses an Intra‑scale Feature Interaction Module to refine fine‑grained features and a Hierarchical Feature Interaction Module to fuse cross‑scale information while preserving spatial structure. Experiments on NWPU‑Refer and RRSIS‑D show that MRSNet achieves strong, competitive performance, especially under strict IoU thresholds, demonstrating the effectiveness of both the new dataset and the model.
By Zhigang Yang, Huiguang Yao, Linmao Tian, Xuezhi Zhao, Qiang Li, Qi Wang
The paper introduces OVRSISBench, a unified benchmark for open‑vocabulary remote sensing image segmentation, and evaluates existing OVS/OVRSIS models, uncovering their shortcomings in remote sensing contexts. Leveraging insights from this evaluation, the authors propose RSKT‑Seg, a new framework featuring a Multi‑Directional Cost Map Aggregation module, an Efficient Cost Map Fusion transformer, and a Remote Sensing Knowledge Transfer module. Experiments on the benchmark demonstrate that RSKT‑Seg outperforms strong baselines by +3.8 mIoU and +5.9 mACC while achieving twice the inference speed.
By Bingyu Li, Haocheng Dong, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
arXiv:2605.07562v2 Announce Type: replace
Abstract: Remote sensing imagery spans ground sampling distances (GSDs) from centimeters to tens of meters, so both the visual evidence for a geographic conc...
By Song Zhang, Yanlong Chen, Yining Chen, Xiaowei Zhang, Yawei Li
Open-vocabulary remote sensing segmentation has recently emerged as a promising paradigm that enables pixel-level recognition of arbitrary categories specified by natural language, including classes unseen during training. However, geospatial domain shifts caused by heterogeneous regions, spatial resolutions, and acquisition platforms weaken visual-text matching and limit cross-dataset generalization.
Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.
By Dania Batool, Liliana Lo Presti, Marco La Cascia, Filippo Vella
OpenEarth-OVS44 is a new multimodal remote sensing dataset featuring 290,587 expert‑verified segments from 2,832 RGB images and 1,341 RGB‑SAR pairs at sub‑meter resolution, covering 40 cities in France, Japan, and the United States. It adopts a unified 44‑class taxonomy organized into nine base categories and offers three evaluation tracks: an RGB‑only fine‑grained benchmark, a multimodal track with synthetic cloudy conditions for weather‑robust evaluation, and a held‑out cross‑region track. Experiments show that training on OpenEarth-OVS44 improves fine‑grained segmentation performance, achieving up to 7.1 mIoU points on unseen categories and 27.6 mIoU points on seen categories compared to the strongest training‑free baseline.
By Yimin Wei, Hongruixuan Chen, Hao Zheng, Hao Liu, Jian Song, Yuting Zhu, Junshi Xia, Naoto Yokoya
arXiv:2606. 02092v1 Announce Type: cross Abstract: Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets.
By \"Umit Mert \c{C}a\u{g}lar, Alptekin Temizel
arXiv:2606. 00548v1 Announce Type: cross Abstract: Concentrated Animal Feeding Operations (CAFOs) play an important role in agricultural production but are also associated with environmental, public health, and disease surveillance concerns.
By Oishee Bintey Hoque, Nibir Chandra Mandal, Mandy L Wilson, Samarth Swarup, Madhav Marathe, Abhijin Adiga
The paper introduces Semantics-Aware Hierarchical Consensus (SAHC), a deep learning framework that incorporates hierarchical-level-specific classification heads and cross-level probability projectors to leverage predefined label hierarchies in remote sensing image classification. SAHC fuses direct and projected predictions into a geometric consensus distribution, enabling self-consistent training and optional hierarchy-aware inference. Experiments on two benchmark datasets demonstrate the method’s effectiveness in guiding network learning and its robustness across varying spectral and spatial resolutions.
By Giulio Weikmann, Gianmarco Perantoni, Lorenzo Bruzzone