Power Plant Detection for Energy Estimation using GIS with Remote Sensing, CNN & Vision Transformers
This work addresses power plant monitoring for energy estimation applications, but it is incremental as it combines existing methods without introducing a new paradigm.
The researchers tackled power plant detection for energy estimation by proposing a hybrid model that pipelines GIS with remote sensing, CNN, and Vision Transformers, enhancing classification to assist in monitoring and sustainable energy planning.
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.