AI-Based Classification of Mosquito-Prone and Non-Prone Urban Environments Using Image Data in Bangladesh

This dataset contains approximately 5,000 ground-level images collected from more than 25 urban locations across Dhaka, Jessore, and Magura in Bangladesh to support AI-based mosquito breeding risk classification. The images represent diverse urban environments captured under varied weather, lighting, and seasonal conditions during pre-monsoon and early-monsoon periods. The dataset is organized into two folders corresponding to the risk classes: Prone, Non prone where each folder contains images of that category. All images were manually labeled by three team members independently, with a consensus review achieving Cohen's kappa of 0.87, indicating very high inter-annotator agreement. Images were captured using consumer-grade smartphones and preprocessed to a standardized 640 × 480-pixel resolution in 4:3 aspect ratio. The dataset is intended for image classification tasks to train, validate, and evaluate models that can automatically detect and classify urban environments by mosquito breeding risk level. Categories for this data: Prone (visible stagnant water, garbage accumulation, clogged drains, partially wet surfaces with potential breeding conditions), Non prone (clean dry roads and well-maintained open spaces)