Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171918
Title: Mining big spatial data
Authors: Ng, Zhi Kai
Keywords: Engineering::Computer science and engineering::Data::Data structures
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Ng, Z. K. (2023). Mining big spatial data. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171918
Project: SCSE22-0725 
Abstract: The purpose of representing road network is to provide a comprehensive view of the real-world road connections and features for analysis purposes. However, limitations in data unavailability hinder the creation of road network representations to precisely align with actual road layouts. This study investigates the usage of images to construct a road network representation and subsequently improving the performance of several downstream tasks. Three distinct image encoder architectures are used to obtain the image embeddings. Furthermore, several enhancement techniques are applied to further boost the performance of the images. My findings showcase the performance comparison between images, enhanced images, and baseline methods. Specifically, images showed superior performance when compared with baseline methods. Moreover, enhancements contribute slightly to improving image performance. The study highlights the usefulness of images in constructing road network representations. By capturing the visual information from images, the study introduced a novel approach in representing road networks compared to traditional methods.
URI: https://hdl.handle.net/10356/171918
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Amended_Final_Report_SCSE22-0725.pdf
  Restricted Access
Undergraduate project report1.85 MBAdobe PDFView/Open

Page view(s)

72
Updated on Jul 24, 2024

Download(s)

4
Updated on Jul 24, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.