DISCnet Presentation — Research Internship at Satellite Applications Catapult: Remote Sensing and Deep Learning for Polluting Cement Plant Detection (2022)

This presentation summarised my research internship at the Satellite Applications Catapult, undertaken as part of my DISCnet scholarship. I presented the development of deep learning methods for detecting polluting cement plants using multi-temporal Landsat-8 satellite imagery.

Event: DISCnet Doctoral Training Consortium
Date: 04 May 2022

YouTube recording

The presentation covered:

• What makes cement plants distinguishable in satellite imagery.
• Construction of a global database of cement production assets.
• Creation of 1 km2 Landsat-8 chip grids across multiple months.
• Use of thermal infrared (Bands 10 and 11) and short-wave infrared (Bands 6 and 7).
• Deep learning architectures: VGG13, ResNet, EfficientNet.
• Transfer learning and class weighting for imbalanced datasets.
• Multi-run evaluation across stratified splits.
• Results demonstrating the feasibility of automated cement plant detection.

The talk also reflected on the benefits of the research placement:

• Collaboration with industry and academia.
• Communicating technical work to non-experts.
• Applying deep learning to diverse remote sensing datasets.
• Building a research portfolio and producing publishable work.