标题： 基于视觉变换器和位置嵌入的泛北极永久冻土地貌与人类建筑基础设施特征检测 显示英文标题

标题： Pan-Arctic Permafrost Landform and Human-built Infrastructure Feature Detection with Vision Transformers and Location Embeddings

摘要： 利用亚米级卫星影像在全球北极范围内准确绘制永久冻土地貌、融沉扰动和人类建筑基础设施变得日益关键。 处理宠物字节规模的图像数据需要高性能计算和强大的特征检测模型。 虽然基于卷积神经网络（CNN）的深度学习方法广泛用于遥感（RS），类似于基于转换器的大语言模型的成功，视觉转换器（ViTs）通过注意力机制在捕捉长距离依赖关系和全局上下文方面具有优势。 ViTs支持通过自监督学习进行预训练——解决了北极特征检测中标记数据的常见限制，并在基准数据集上优于CNN。 北极也对模型泛化提出了挑战，尤其是当具有相同语义类别的特征表现出不同的光谱特性时。 为了解决这些北极特征检测问题，我们将地理空间位置嵌入集成到ViTs中，以提高跨区域的适应能力。 本研究探讨了：（1）预训练的ViTs作为高分辨率北极遥感任务特征提取器的适用性，以及（2）结合图像嵌入和位置嵌入的优势。 使用之前发布的北极特征检测数据集，我们在三项任务上评估我们的模型——检测冰楔多边形（IWP）、退化融沉滑坡（RTS）和人类建筑基础设施。 我们通过实证探索了多种配置来融合图像嵌入和位置嵌入。 结果显示，带有位置嵌入的ViTs在三项任务中的两项上优于之前的基于CNN的模型，包括RTS检测的F1分数从0.84提高到0.92，展示了具有空间意识的基于转换器的模型在北极遥感应用中的潜力。 显示英文摘要

摘要： Accurate mapping of permafrost landforms, thaw disturbances, and human-built infrastructure at pan-Arctic scale using sub-meter satellite imagery is increasingly critical. Handling petabyte-scale image data requires high-performance computing and robust feature detection models. While convolutional neural network (CNN)-based deep learning approaches are widely used for remote sensing (RS),similar to the success in transformer based large language models, Vision Transformers (ViTs) offer advantages in capturing long-range dependencies and global context via attention mechanisms. ViTs support pretraining via self-supervised learning-addressing the common limitation of labeled data in Arctic feature detection and outperform CNNs on benchmark datasets. Arctic also poses challenges for model generalization, especially when features with the same semantic class exhibit diverse spectral characteristics. To address these issues for Arctic feature detection, we integrate geospatial location embeddings into ViTs to improve adaptation across regions. This work investigates: (1) the suitability of pre-trained ViTs as feature extractors for high-resolution Arctic remote sensing tasks, and (2) the benefit of combining image and location embeddings. Using previously published datasets for Arctic feature detection, we evaluate our models on three tasks-detecting ice-wedge polygons (IWP), retrogressive thaw slumps (RTS), and human-built infrastructure. We empirically explore multiple configurations to fuse image embeddings and location embeddings. Results show that ViTs with location embeddings outperform prior CNN-based models on two of the three tasks including F1 score increase from 0.84 to 0.92 for RTS detection, demonstrating the potential of transformer-based models with spatial awareness for Arctic RS applications.