A Multicomponent Collaborative Fossil Fuel Power Plants Detection Framework Based on Geographic Analysis in Wide Areas

Abstract

Fossil fuel power plants (FFPPs) are major sources of carbon dioxide emissions in the power industry. Accurately locating these plants is essential for monitoring emissions, studying atmospheric pollution, and optimizing power supply structures. However, obtaining comprehensive geographic location data for FFPPs is challenging due to data availability and collection constraints. Therefore, we propose a wide-area FFPP detection framework that enhances detection efficiency through geographic constraints and improves detection accuracy using a multicomponent collaborative strategy. First, a geographic constraint method was developed, leveraging multisource geographic data to extract candidate FFPP regions based on their spatial characteristics. Next, we constructed a comprehensive FFPP dataset, including plants and their components, and trained two separate object detection models for FFPPs and their components. Subsequently, the FFPP model was used to perform coarse detection, followed by the refined detection of primary features (chimneys, square chimneys, and cooling towers) and auxiliary features (substations and storage tanks). After detecting these objects, the density-based spatial clustering of applications with noise clustering algorithm was applied to retain clusters with specific component combinations, yielding the final detection results. In the approximately 660 000-km² study area (Jiangsu Province, São Paulo, and Maharashtra), the proposed framework effectively minimized invalid regions by 94.8%, 91.12%, and 97.1%, respectively. Validation using high-resolution Google Earth images recalled 225 known FFPPs with a 91.46% recall rate and identified 167 previously unrecorded FFPPs. These results demonstrate the framework’s reliability for efficient and automated FFPP detection, representing a novel integration of multisource geographic analysis, deep-learning-based object detection, and wide-area FFPP recognition.