Visual Pollution Dhaka Dataset

Welcome to the Visual Pollution Dhaka dataset! This dataset provides a collection of images depicting visual pollution in the city of Dhaka. The dataset is labeled and annotated, making it suitable for various computer vision and deep learning projects. The images in this dataset capture instances of visual pollution, such as billboards, advertisements, graffiti, and other visually intrusive elements in urban environments.

📊 Dataset Overview

• Total Images: 4500+
• Classes: 12
• Image Resolution: 440*560
• Annotation Format: YOLO/PascelVoc (e.g., bounding boxes, segmentation masks)

🚀 Potential Applications

The Visual Pollution Dhaka dataset can serve as a valuable resource for deep learning projects in the field of computer vision. Here are some potential applications where this dataset can be utilized:

1. Visual Pollution Detection: Train deep learning models to automatically detect and classify instances of visual pollution in urban environments. This can help in identifying areas with high visual pollution levels and assist city planners and policymakers in taking appropriate actions.

2. Visual Pollution Segmentation: Utilize the annotated segmentation masks to develop models that can accurately segment and separate visual pollution elements from the surrounding environment. This can be useful for generating cleaner and visually appealing images or for conducting detailed analyses of the extent and impact of visual pollution.

3. Visual Pollution Monitoring: Develop algorithms to analyze the distribution and temporal changes in visual pollution elements in different areas of Dhaka. This can aid in monitoring the effectiveness of pollution control measures and identifying areas that require immediate attention.

4. Urban Planning and Design: Use the dataset to analyze the relationship between visual pollution and various urban design factors, such as building heights, street layouts, and architectural styles. Deep learning models can assist in identifying design interventions that can mitigate visual pollution and improve the overall aesthetic quality of urban spaces.

💡 Usage Suggestions

To make the most out of the Visual Pollution Dhaka dataset for your deep learning project, consider the following suggestions:

1. Data Preprocessing: Perform necessary preprocessing steps such as resizing, normalization, and augmentation to ensure consistency and enhance the quality of the dataset.

2. Model Selection: Choose suitable deep learning architectures such as Convolutional Neural Networks (CNNs) or Fully Convolutional Networks (FCNs) that are well-suited for the specific task at hand, such as classification or segmentation.

3. Transfer Learning: Leverage the power of transfer learning by utilizing pre-trained models trained on large-scale datasets like ImageNet. Fine-tuning these models on the Visual Pollution Dhaka dataset can significantly improve performance, especially with limited data.

4. Model Evaluation: Employ appropriate evaluation metrics such as accuracy, precision, recall, and Intersection over Union (IoU) to assess the performance of your deep learning models.

5. Model Interpretation: Utilize visualization techniques such as Grad-CAM, saliency maps, or activation maximization to gain insights into how your deep learning model makes predictions and identify important visual cues for visual pollution detection or segmentation.

📁 Dataset Structure

The dataset is organized into different folders, each representing a class or category of visual pollution elements. The images are accompanied by annotation files (e.g., bounding box coordinates, segmentation masks) in a format specified in the dataset description.

📄 Dataset Citation

If you use the Visual Pollution Dhaka dataset in your research or projects, please consider citing it:

[Dataset Citation]

📋 Contributing

Contributions to the Visual Pollution Dhaka dataset are welcome! If you would like to contribute to this dataset or report any issues, please follow these steps:

1. Fork the repository.
2. Create a new branch for your additions or modifications.
3. Make the necessary changes.
4. Commit and push your changes.
5. Submit a pull request.

✉️ Contact

For any inquiries or suggestions regarding the Visual Pollution Dhaka dataset, please reach out to:

• Name: Md Riasat Khan
• Email: riasat.khan@northsouth.edu
• Affiliation: Associate Professor, NSU
• PhD: New Mexico, USA

Project Developer:

• Name: Md Fahim Shahoriar Titu
• Email: fahimshahoriar66@gmail.com
• Facebook: fahim.shahoriar.t2
• LinkedIn: Fahim Shahoriar
• GitHub: TituShahoriar