发表机构
Ben-Gurion University of the Negev; The Hebrew University of Jerusalem; Ben Gurion Institute for the Study of Israel & Zionism; Ben-Gurion Israel Research Institute（内盖夫本-古里安大学; 耶路撒冷希伯来大学; 本-古里安以色列与犹太复国主义研究所; 本-古里安以色列研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究评估了SAM3在遥感影像中小规模光伏分割中不同提示策略的效果，发现混合提示性能最优，仅需少量标注样本即可实现高效分割，为无电网地区光伏测绘提供了可行方案。
AI 中文摘要
时空光伏数据对于理解无电网地区的光伏普及过程至关重要，但这类数据大多仍无法获取。遥感影像的自动分割提供了一种有前景的解决方案；然而， residential光伏系统因其尺寸小、分布稀疏，导致严重的目标-背景不平衡，仍是具有挑战性的分割目标。视觉-语言基础模型（FMs）通过基于提示的语义和空间引导提供了一种数据高效的范式，但不同提示类型的相对贡献仍不清楚。我们针对遥感影像中的小规模光伏分割，系统评估了SAM3模型，在不同监督水平、训练策略、空间分辨率和成像条件下，比较了文本提示、几何提示和混合提示。研究站点为来自一个大型无电网农村地区的多时相航空影像，研究结果在另外三个数据集上得到了验证。提示策略成为决定模型行为的主导因素。文本提示始终产生最低的性能，且对监督和成像条件表现出最大的敏感性。相比之下，空间引导显著提高了分割精度和鲁棒性。混合提示实现了最高的精度和稳定性，表明语义和空间引导提供了互补信息。仅用几百个带标注样本就实现了大部分性能提升，显示出强大的数据效率。迁移学习的整体影响有限，仅在监督有限的情况下，文本提示观察到适度的改进。总体而言，我们的研究结果确定提示策略是SAM3模型适配性、鲁棒性和泛化性的关键决定因素，凸显了可提示基础模型在数据受限的无电网地区实现可扩展光伏测绘的潜力。
英文摘要
Spatio-temporal PV data are essential for understanding adoption processes in off-grid regions, yet such data remain largely unavailable. Automated segmentation of remote sensing (RS) imagery offers a promising solution; yet, residential PV systems remain challenging targets because of their small size and sparse distribution, resulting in severe target-background imbalance. Vision-language foundation models (FMs) provide a data-efficient paradigm through prompt-based semantic and spatial guidance, but the relative contribution of different prompt types remains unclear. We systematically evaluate SAM3 for small-scale PV segmentation in RS imagery by comparing textual, geometric, and hybrid prompting, under varying supervision levels, training strategies, spatial resolutions, and imaging conditions. Multi-temporal aerial imagery from a large off-grid rural region serves as a study site, with findings validated across three additional datasets. Prompting strategy emerged as the dominant factor governing model behavior. Textual prompting consistently produced the lowest performance and showed the greatest sensitivity to supervision and imaging conditions. In contrast, spatial guidance substantially improved both segmentation accuracy and robustness. Hybrid prompting achieved the highest accuracy and stability, indicating that semantic and spatial guidance provide complementary information. Most performance gains were achieved with only a few hundred annotated samples, demonstrating strong data efficiency. Transfer learning had limited overall impact, with only modest improvements observed for textual prompting under limited supervision. Overall, our findings establish prompting strategy as a key determinant of SAM3 adaptation, robustness, and generalization, highlighting the potential of promptable FMs for scalable PV mapping in data-constrained off-grid regions.
