The paper introduces CASID, a large-scale climate‑aware satellite image dataset for land‑cover semantic segmentation, comprising 980 5,000×5,000‑pixel images from 30 Asian regions across four climate zones (temperate monsoon, subtropical monsoon, tropical monsoon, and tropical rainforest). It is the first dataset to explicitly address domain gaps caused by climatic diversity, providing detailed analyses of image features, textures, category distributions, spectral values, and object shapes for each climate domain. Extensive experiments on CASID benchmark state‑of‑the‑art segmentation and unsupervised domain adaptation methods, establishing a robust baseline for future research.
By Songlin Liu, Linwei Chen, Li Zhang, Jun Hu, Ying Fu
arXiv:2609.13332v1 Announce Type: new
Abstract: Automated landslide segmentation on Mars is one of the important tasks for understanding its surface processes, and all will aid in future space explor...
By Leo Thomas Ramos, Sidike Paheding, Abel A. Reyes-Angulo, Rajaneesh A., Sajinkumar K. S., Angel D. Sappa, Thomas Oommen
arXiv:2608.29609v1 Announce Type: new
Abstract: Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the...
By Ming-Han Lee, Chi-Yeh Chen
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
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
arXiv:2608. 15790v1 Announce Type: new Abstract: Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain.
By Steven Wallace, William D Harcourt, Richard Hann, Aiden Durrant, Somayajulu Sripada, Georgios Leontidis
arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.
By Atiq Ur Rehman, Joseph Michael Donovan
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.
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
LEVIRDet-159 is the largest remote sensing object detection dataset, featuring 159 categories, 2.56 million bounding boxes, and 700k fine‑grained annotations across a multi‑level taxonomy. It surpasses existing datasets by roughly 7× more images, 6× more object instances, and 4× more categories. Leveraging this dataset, the authors propose LEVIRDetNet, a scale‑hierarchy‑aware foundation model that predicts ground sampling distance, modulates queries accordingly, and employs a hierarchy‑aware detection head, achieving state‑of‑the‑art performance on nine external benchmarks without target‑domain training or fine‑tuning.
By Qinzhe Yang, Dongyu Wang, Haohan Niu, Jia Xu, Zhenwei Shi, Zhengxia Zou
FloodVLM-Pix is a new language‑guided pixel‑level flood understanding model introduced in the arXiv paper "FloodVLM-Pix: Language‑Guided Pixel‑Level Flood Understanding." The authors created the first large‑scale remote sensing flood multi‑modal benchmark, comprising 131,200 image patches and 443,100 textual descriptions of flood severity and evolution. FloodVLM‑Pix incorporates a flood prior module to mitigate attentional bias and a multi‑temporal flood pixel decoder with a general‑specific feature fusion mechanism to improve flood segmentation accuracy.
"whyItMatters":"The paper presents a benchmark and model that enhance both flood description quality and pixel‑level segmentation, addressing shortcomings of existing vision‑language models on flood‑related tasks."
By Chengyang Zhang, Zhihao Wang, Zhiyu Long, Xueqian Wang, Quanming Yao, Wei Wu, Gang Li
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