Shuo Ni1,3, Di Wang2,3, He Chen1, Haonan Guo2,3 †, Ning Zhang1.4 †, Jing Zhang2 †.
1 Beijing Institute of Technology, 2 Wuhan University, 3 Zhongguancun Academy, 4 Hong Kong Polytechnic University.
† Corresponding author
Update | Abstract | Datasets | Models | Usage | Statement
2025.11.25
- The paper is post on arXiv! (arXiv 2511,23332)
Instruction-driven segmentation in remote sensing generates masks from guidance, offering great potential for accessible and generalizable applications. However, existing methods suffer from fragmented task formulations and limited instruction data, hindering effective understanding and generalization. To address these issues, we introduce GeoSeg-1M, the first million-scale dataset for remote sensing instruction-driven segmentation, constructed via an automatic mask filtering and instruction generation pipeline that synthesizes referring, interactive, and reasoning segmentation instructions from multiple public datasets. GeoSeg-1M contains 590K images, 117 categories, and 1.1M image–mask–instruction triplets. Building upon this foundation, we further curate GeoSeg-Bench, a challenging benchmark designed to evaluate contextual understanding and reasoning capabilities across diverse instruction-driven tasks and complex geospatial scenes. Furthermore, we present UniGeoSeg, a unified framework that serves as a strong baseline, incorporating task-aware text enhancement, latent knowledge memory, and a progressive training strategy to facilitate multi-task learning. Extensive experiments demonstrate the state-of-the-art performance of UniGeoSeg across GeoSeg-Bench and diverse public benchmarks, while exhibiting strong zero-shot generalization.
Figure 1. Examples from GeoSeg-1M.
Figure 2. The diagram of UniGeoSeg.
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If you find GeoZero helpful, please give a ⭐ and cite it as follows:
@misc{ni2025unigeosegunifiedopenworldsegmentation,
title={UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes},
author={Shuo Ni and Di Wang and He Chen and Haonan Guo and Ning Zhang and Jing Zhang},
year={2025},
eprint={2511.23332},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.23332},
}
For any other questions please contact Shuo Ni at bit.edu.cn or 126.com.
This project is based on PSALM, SegEarth-R1, Thanks for their wonderful work!