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Training neural networks for informal road extraction
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613454277279744 |
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| access_status_str | Open Access |
| author2 | Maribe, Gaonyalelwe |
| author_browse | Maribe, Gaonyalelwe |
| author_facet | Maribe, Gaonyalelwe |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/89454 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:24.202Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/89454 Training neural networks for informal road extraction Maribe, Gaonyalelwe u17099481@tuks.co.za Fabris-Rotelli, Inger Nicolette Wannenburg, Abraham Johannes UCTD Data set bias Neural networks Informal road extraction Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria 2022. Roads found in informal settlements arise out of convenience are often not recorded or maintained by authorities. This may cause issues with service delivery, sustainable development and crisis mitigation, including COVID-19. Therefore, the aim of extracting informal roads from remote sensing images is of importance. Existing techniques aimed at the extraction of formal roads are not completely suitable for the problem due to the complex physical and spectral properties that informal roads pose. The only existing approaches for informal roads, namely [62, 82], do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities due to the way they learn through training. They require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data sets. A problem that has been shown to come up when working with computer vision data sets is data set bias. Data set bias adds to the already existing problem of machine learning algorithms called overfitting. This paper implements a neural network developed for formal roads to extract informal roads from three data sets digitised by this research group to investigate the presence of data set bias. Three different geological areas from South Africa are digitised. We implement the GAN-UNet model that obtained the highest F1-score in a 2020 review paper [1] on the state-of-the-art deep learning models used to extract formal roads. We present quantitative and qualitative results that concludes the presence of data set bias. We then present further work that can be done to create a robust training data set for the development of an automatic informal road extraction model. - Data Science Africa 2021 Project (PI: Inger Fabris-Rotelli) - Centre for Artificial Intelligence Research - CoE-MaSS grant (2022 grant: ref #2022-018-MAC-Road) Statistics MSc (Advanced Data Analytics) Unrestricted 2023-02-13T13:15:40Z 2023-02-13T13:15:40Z 2023-04 2022-11 Mini Dissertation * A2023 https://repository.up.ac.za/handle/2263/89454 10.25403/UPresearchdata.21522360 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Data set bias Neural networks Informal road extraction Training neural networks for informal road extraction |
| title | Training neural networks for informal road extraction |
| title_full | Training neural networks for informal road extraction |
| title_fullStr | Training neural networks for informal road extraction |
| title_full_unstemmed | Training neural networks for informal road extraction |
| title_short | Training neural networks for informal road extraction |
| title_sort | training neural networks for informal road extraction |
| topic | UCTD Data set bias Neural networks Informal road extraction |
| url | https://repository.up.ac.za/handle/2263/89454 |