融合遥感与深度学习，实现电力设施实时检测与能源估算
Power Plant Detection for Energy Estimation using GIS with Remote Sensing, CNN & Vision Transformers
- 结合GIS、CNN与视觉变换器，构建多源数据协同分析框架
- 通过遥感影像实现电力设施精准识别，支持实时能源估算
- 适合能源规划、环境监测领域研究人员参考
本研究提出一种混合模型，用于电力设施检测以辅助能源估算。该方法将具备遥感功能的地理信息系统（GIS）与卷积神经网络（CNN）及视觉变换器（ViT）相结合。通过GIS平台实现多类型数据在统一地图上的实时分析，利用CNN提取特征，借助ViT捕捉长距离依赖关系。实验表明，该混合方法显著提升了分类性能，有助于电力设施的监测与运营管控，为未来能源估算与可持续能源规划提供支持。结果展示了机器学习方法与领域特定技术融合在性能提升上的有效性。
原文摘要 · Abstract (English)
In this research, we propose a hybrid model for power plant detection to assist energy estimation applications, by pipelining GIS (Geographical Information Systems) having Remote Sensing capabilities with CNN (Convolutional Neural Networks) and ViT (Vision Transformers). Our proposed approach enables real-time analysis with multiple data types on a common map via the GIS, entails feature-extraction abilities due to the CNN, and captures long-range dependencies through the ViT. This hybrid approach is found to enhance classification, thus helping in the monitoring and operational management of power plants; hence assisting energy estimation and sustainable energy planning in the future. It exemplifies adequate deployment of machine learning methods in conjunction with domain-specific approaches to enhance performance.
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