用地理信息指导深度学习，提升电站分类准确率。
Geoinformatics-Guided Machine Learning for Power Plant Classification
- 融合地理空间掩码的CNN与ViT双模型架构
- 相比纯深度学习方法，分类准确率显著提升
- 适合智能城市与环境计算领域的研究者参考
本文提出一种知识引导机器学习（KGML）新框架，结合卷积神经网络（CNN）与视觉变换器（ViT）及地理信息系统（GIS），利用GIS生成的空间掩码（SM）引入地理信息知识，以提升从真实卫星图像中对多种类型电站进行分类的性能。实验表明，该方法显著优于仅使用CNN或ViT的基线模型，凸显了地理信息引导在能源管理中的关键作用。该工作推动了KGML的发展，对智慧城市与环境计算领域具有广泛影响。
原文摘要 · Abstract (English)
This paper proposes an approach in the area of Knowledge-Guided Machine Learning (KGML) via a novel integrated framework comprising CNN (Convolutional Neural Networks) and ViT (Vision Transformers) along with GIS (Geographic Information Systems) to enhance power plant classification in the context of energy management. Knowledge from geoinformatics derived through Spatial Masks (SM) in GIS is infused into an architecture of CNN and ViT, in this proposed KGML approach. It is found to provide much better performance compared to the baseline of CNN and ViT only in the classification of multiple types of power plants from real satellite imagery, hence emphasizing the vital role of the geoinformatics-guided approach. This work makes a contribution to the main theme of KGML that can be beneficial in many AI systems today. It makes broader impacts on AI in Smart Cities, and Environmental Computing.
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