Abstract
Remote sensing datasets offer significant promise for tackling key classification tasks such as land-use categorization, object presence detection, and rural/urban classification. However, many existing studies tend to focus on narrow tasks or datasets, which limits their ability to generalize across various remote sensing classification challenges. To overcome this, we propose a novel model, SpatialNet-ViT, leveraging the power of Vision Transformers (ViTs) and Multi-Task Learning (MTL). This integrated approach combines spatial awareness with contextual understanding, improving both classification accuracy and scalability. Additionally, techniques like data augmentation, transfer learning, and multi-task learning are employed to enhance model robustness and its ability to generalize across diverse datasets
Abstract (translated)
遥感数据集在处理诸如土地利用分类、目标存在检测和城乡分类等关键任务方面具有巨大潜力。然而，许多现有的研究往往集中于狭窄的任务或特定的数据集上，这限制了它们应对不同遥感分类挑战的能力。为了解决这一问题，我们提出了一种新的模型SpatialNet-ViT，该模型结合了视觉变换器（Vision Transformers, ViT）和多任务学习（Multi-Task Learning, MTL）的优势。这种集成方法将空间感知与上下文理解相结合，提高了分类准确性和可扩展性。此外，通过应用数据增强、迁移学习和多任务学习等技术，增强了模型的鲁棒性和跨多种数据集泛化的能力。
URL
https://arxiv.org/abs/2506.22501
