Date of Award


Degree Type

Masters Thesis

Degree Name

Master of Science (MS)


Computing Sciences and Computer Engineering

Committee Chair

Chaoyang Zhang

Committee Chair School

Computing Sciences and Computer Engineering

Committee Member 2

Yuanyuan Zhang

Committee Member 2 School


Committee Member 3

Zhaoxian Zhou

Committee Member 3 School

Computing Sciences and Computer Engineering


Lack of sidewalk pavement can contribute to pedestrian fatalities and injuries in the USA. Although many researchers have conducted research on sidewalk detection, there are not many publicly available datasets that we would work on for sidewalk classification. In this study, I conducted classification tasks using pretrained CNN models including VGG16 and ResNet50. I extended these models by adding custom layers at the top of pretrained layers, employing various techniques to improve the classification accuracy.

The dataset comprises 4,731 images of sidewalk based on occlusion levels, including images where the sidewalk is visible from overhead and instances where sidewalk is occluded by tree canopies, buildings, or vehicles etc. These images were collected manually using ArcGIS Pro from Allegheny County, PA USA.

Different preprocessing techniques were applied to the dataset, such as image resizing, image extraction from bounding boxes, and image mapping to actual class label. Due to the class imbalance nature of the dataset, an augmentation technique was employed to augment the minority class and reduce the imbalanced. The data was partitioned into a training (80%) and test (20%) sets. The models were trained with and without augmentation and their performances were evaluated using metrics including precision, recall, F1- score, accuracy, and area under receiver operating curve. The results showed VGG16 outperforms ResNet50 in sidewalk classification and employing data augmentation technique to the minority class proves beneficial when dealing with imbalanced data in image classification tasks.

Available for download on Thursday, July 31, 2025