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.
","abstract_html":"<p>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.</p> <p>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.</p> <p>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.</p>","abstract_has_math":false,"creators":["Khanal, Subeksha"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Chaoyang Zhang","Yuanyuan Zhang","Zhaoxian Zhou"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-01T07:00:00Z","date_published":"2024-05-01T07:00:00Z","updated_at":"2026-07-24T05:45:40Z","subjects":["Deep Learning","Convolutional Neural Networks","Classification","Satellite Image","Sidewalk Detection","Data Augmentation","Artificial Intelligence and Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/1041","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chaoyang Zhang","Yuanyuan Zhang","Zhaoxian Zhou"]},{"key":"dc:creator","label":"Author","values":["Khanal, Subeksha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-31T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep Learning","Convolutional Neural Networks","Classification","Satellite Image","Sidewalk Detection","Data Augmentation","Artificial Intelligence and Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/1041"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Satellite Image Analysis and Sidewalk Classification using Deep Learning Models"]}]}],"canonical_facts":{"dc:contributor":["Chaoyang Zhang","Yuanyuan Zhang","Zhaoxian Zhou"],"dc:creator":["Khanal, Subeksha"],"dc:date.available":["2025-07-31T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://aquila.usm.edu/masters_theses/1041"],"dc:subject":["Deep Learning","Convolutional Neural Networks","Classification","Satellite Image","Sidewalk Detection","Data Augmentation","Artificial Intelligence and Robotics"],"dc:title":["Satellite Image Analysis and Sidewalk Classification using Deep Learning Models"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:45:40Z"}