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arXiv · 2503.12191

Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches

Ying Zang·Yuncan Gao·Jiangi Zhang·Yuangi Hu·Runlong Cao·Lanyun Zhu·Qi Zhu·Deyi Ji·Renjun Xu·Tianrun Chen
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Abstract

This work advances zero-shot interactive segmentation for remote sensing imagery through three key contributions. First, we propose a novel sketch-based prompting method, enabling users to intuitively outline objects, surpassing traditional point or box prompts. Second, we introduce LTL-Sensing, the first dataset pairing human sketches with remote sensing imagery, setting a benchmark for future research. Third, we present LTL-Net, a model featuring a multi-input prompting transport module tailored for freehand sketches. Extensive experiments show our approach significantly improves segmentation accuracy and robustness over state-of-the-art methods like SAM, fostering more intuitive human-AI collaboration in remote sensing analysis and enhancing its applications.

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Ying Zang, Yuncan Gao, Jiangi Zhang, Yuangi Hu, Runlong Cao, Lanyun Zhu, Qi Zhu, Deyi Ji, Renjun Xu, Tianrun Chen. 2025-03-15. Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches. https://arxiv.org/abs/2503.12191

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