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Solving visual pollution with deep learning: A new nexus in environmental management.

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  • 1Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh.

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This study introduces automated visual pollutant classification using deep learning to identify issues like billboards and litter. The developed model achieves high accuracy, paving the way for environmental management applications.

Keywords:
Convolutional neural networkDeep learningEnvironmental managementImage recognitionPollutant classificationVisual pollution

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Visual pollution is an emerging environmental concern requiring formalization and assessment.
  • Existing environmental pollution research needs to incorporate visual pollution dimensions.
  • Automated classification is necessary for effective visual pollution management.

Purpose of the Study:

  • To establish automated visual pollutant classification using deep learning.
  • To develop a model for identifying visual pollutants in urban environments.
  • To create new metrics for urban environmental management.

Main Methods:

  • A convolutional neural network (deep learning model) was trained and tested for image recognition.
  • Four categories of visual pollutants were considered: billboards, wires, towers, and litter.
  • Data augmentation and an 80:20 train-test split were employed.

Main Results:

  • The deep learning model achieved 95% training accuracy and 85% validation accuracy.
  • Model accuracy is influenced by dataset size.
  • The study demonstrates the feasibility of automated visual pollutant classification.

Conclusions:

  • Automated visual pollutant classification is achievable with deep learning.
  • The model has practical applications in environmental management using drones and CCTV.
  • A 'visual pollution score/index' can be generated for urban areas.