Power Plant Detection for Energy Estimation using GIS with Remote Sensing, CNN & Vision Transformers

TL;DR

This paper introduces a hybrid GIS-based model combining CNNs and Vision Transformers for real-time power plant detection, improving classification accuracy for energy estimation and sustainable planning.

Contribution

It presents a novel hybrid approach integrating GIS, CNN, and ViT for power plant detection, enabling enhanced real-time analysis and long-range dependency capture.

Findings

Improved classification accuracy in power plant detection.

Effective real-time analysis of multiple data types.

Enhanced monitoring for energy estimation and planning.

Abstract

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.