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20242026
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cs.CV2026

Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment

Ziyao Wang, Maonan Wang, Yucheng He +5

Cloud removal (CR) is essential for optical remote sensing, serving as a prerequisite for reliable downstream interpretation, such as semantic segmentation and change detection. Ho…

cs.CV2026

MPerS: Dynamic MLLM MixExperts Perception-Guided Remote Sensing Scene Segmentation

Ziyi Wang, Xianping Ma, Ziyao Wang +2

The multimodal fusion of images and scene captions has been extensively explored and applied in various fields. However, when dealing with complex remote sensing (RS) scenes, exist…

cs.CV2026

Open-Vocabulary Semantic Segmentation Network Integrating Object-Level Label and Scene-Level Semantic Features for Multimodal Remote Sensing Images

Jinkun Dai, Yuanxin Ye, Peng Tang +4

Semantic segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and precision earth observation. C…

cs.CV202557 cited

A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation

Xianping Ma, Xiaokang Zhang, Man-On Pun +1

Multimodal remote sensing data, acquired from diverse sensors, offer a comprehensive and integrated perspective of the Earth's surface. Leveraging multimodal fusion techniques, sem…

cs.CV2025

EuroMineNet: A Multitemporal Sentinel-2 Benchmark for Spatiotemporal Mining Footprint Analysis in the European Union (2015-2024)

Weikang Yu, Vincent Nwazelibe, Xianping Ma +4

Mining activities are essential for industrial and economic development, but remain a leading source of environmental degradation, contributing to deforestation, soil erosion, and…

cs.CV2025

Auto-Prompting SAM for Weakly Supervised Landslide Extraction

Jian Wang, Xiaokang Zhang, Xianping Ma +2

Weakly supervised landslide extraction aims to identify landslide regions from remote sensing data using models trained with weak labels, particularly image-level labels. However,…