Shrub Detection in High-Resolution Imagery: A Comparative Study of Two Deep Learning Approaches
- James, Katherine M F, Bradshaw, Karen L
- Authors: James, Katherine M F , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440326 , vital:73766 , ISBN 9783030955021 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: A common task in high-resolution remotely-sensed aerial imagery is the detection of particular target plant species for various ecological and agricultural applications. Although traditionally object-based image analysis approaches have been the most popular method for this task, deep learning approaches such as image patch-based convolutional neural networks (CNNs) have been seen to outperform these older approaches. To a lesser extent, fully convolutional networks (FCNs) that allow for semantic segmentation of images, have also begun to be used in the broader literature. This study investigates patch-based CNNs and FCN-based segmentation for shrub detection, targeting a particular invasive shrub genus. The results show that while a patch-based CNN demonstrates strong performance on ideal image patches, the FCN outperforms this approach on real-world proposed image patches with a 52% higher object-level precision and comparable recall. This indicates that FCN-based segmentation approaches are a promising alternative to patch-based approaches, with the added advantage of not requiring any hand-tuning of a patch proposal algorithm.
- Full Text:
- Date Issued: 2022
- Authors: James, Katherine M F , Bradshaw, Karen L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440326 , vital:73766 , ISBN 9783030955021 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: A common task in high-resolution remotely-sensed aerial imagery is the detection of particular target plant species for various ecological and agricultural applications. Although traditionally object-based image analysis approaches have been the most popular method for this task, deep learning approaches such as image patch-based convolutional neural networks (CNNs) have been seen to outperform these older approaches. To a lesser extent, fully convolutional networks (FCNs) that allow for semantic segmentation of images, have also begun to be used in the broader literature. This study investigates patch-based CNNs and FCN-based segmentation for shrub detection, targeting a particular invasive shrub genus. The results show that while a patch-based CNN demonstrates strong performance on ideal image patches, the FCN outperforms this approach on real-world proposed image patches with a 52% higher object-level precision and comparable recall. This indicates that FCN-based segmentation approaches are a promising alternative to patch-based approaches, with the added advantage of not requiring any hand-tuning of a patch proposal algorithm.
- Full Text:
- Date Issued: 2022
Mapping Computational Thinking Skills to the South African Secondary School Mathematics Curriculum
- Bradshaw, Karen L, Milne, Shannon
- Authors: Bradshaw, Karen L , Milne, Shannon
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440285 , vital:73763 , ISBN 9783030950033 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: Computational thinking (CT) is gaining recognition as an important skill for learners in both Computer Science (CS) and several other disciplines, including mathematics. In addition, researchers have shown that there is a direct correlation between poor mathematical skills and the high attrition rate of CS undergraduates. This research investigates the use of nine core CT skills in the South African Grades 10–12 Mathematics curriculum by mapping these skills to the objectives given in each of the topics in the curriculum. The artefact developed shows that all the identified CT skills are used in the curriculum. With the use of this mapping, future research on interventions to develop these skills through mathematics at secondary school, should produce school leavers with better mathematical and problem solving abilities, which in turn, might contribute to better success rates in CS university courses.
- Full Text:
- Date Issued: 2021
- Authors: Bradshaw, Karen L , Milne, Shannon
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440285 , vital:73763 , ISBN 9783030950033 , https://doi.org/10.1007/978-3-030-95502-1_41
- Description: Computational thinking (CT) is gaining recognition as an important skill for learners in both Computer Science (CS) and several other disciplines, including mathematics. In addition, researchers have shown that there is a direct correlation between poor mathematical skills and the high attrition rate of CS undergraduates. This research investigates the use of nine core CT skills in the South African Grades 10–12 Mathematics curriculum by mapping these skills to the objectives given in each of the topics in the curriculum. The artefact developed shows that all the identified CT skills are used in the curriculum. With the use of this mapping, future research on interventions to develop these skills through mathematics at secondary school, should produce school leavers with better mathematical and problem solving abilities, which in turn, might contribute to better success rates in CS university courses.
- Full Text:
- Date Issued: 2021
A Critical Evaluation of Validation Practices in the Forensic Acquisition of Digital Evidence in South Africa
- Jordaan, Jason, Bradshaw, Karen L
- Authors: Jordaan, Jason , Bradshaw, Karen L
- Date: 2020
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440174 , vital:73754 , ISBN 9783030660390 , https://doi.org/10.1007/978-3-030-66039-0_9
- Description: Accepted digital forensics practice requires the tools used in the forensic acquisition of digital evidence to be validated, meaning that the tools perform as intended. In terms of Sect. 15 of the Electronic Communications and Transactions Act 25 of 2002 in South Africa, validation would contribute to the reliability of the digital evidence. A sample of digital forensic practitioners from South Africa was studied to determine to what extent they make use of validated forensic tools during the acquisition process, and how these tools are proven to be validated. The research identified significant concerns, with no validation done, or no proof of validation done, bringing into question the reliability of the digital evidence in court. It is concerning that the justice system itself is not picking this up, meaning that potentially unreliable digital evidence is used in court.
- Full Text:
- Date Issued: 2020
- Authors: Jordaan, Jason , Bradshaw, Karen L
- Date: 2020
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440174 , vital:73754 , ISBN 9783030660390 , https://doi.org/10.1007/978-3-030-66039-0_9
- Description: Accepted digital forensics practice requires the tools used in the forensic acquisition of digital evidence to be validated, meaning that the tools perform as intended. In terms of Sect. 15 of the Electronic Communications and Transactions Act 25 of 2002 in South Africa, validation would contribute to the reliability of the digital evidence. A sample of digital forensic practitioners from South Africa was studied to determine to what extent they make use of validated forensic tools during the acquisition process, and how these tools are proven to be validated. The research identified significant concerns, with no validation done, or no proof of validation done, bringing into question the reliability of the digital evidence in court. It is concerning that the justice system itself is not picking this up, meaning that potentially unreliable digital evidence is used in court.
- Full Text:
- Date Issued: 2020
Detecting Similarity in Multi-procedure Student Programs Using only Static Code Structure
- Bradshaw, Karen L, Chindeka, Vongai
- Authors: Bradshaw, Karen L , Chindeka, Vongai
- Date: 2020
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440260 , vital:73761 , ISBN 9783030356286 , https://doi.org/10.1007/978-3-030-35629-3_14
- Description: Plagiarism is prevalent in most undergraduate programming courses, including those where more advanced programming is taught. Typical strategies used to avoid detection include changing variable names and adding empty spaces or comments to the code. Although these changes affect the visual components of the source code, the underlying structure of the code remains the same. This similarity in structure can indicate the presence of plagiarism. A system has been developed to detect the similarity in the structure of student programs. The detection system works in two phases: The first phase parses the source code and creates a syntax tree, representing the syntactical structure of each of the programs, while the second takes as inputs two program syntax trees and applies various comparison algorithms to detect their similarity. The outcome of the comparison allows the system to report a result from one of four similarity categories: identical structure, isomorphic structure, containing many structural similarities, and containing few structural similarities. Empirical tests on small sample programs show that the prototype implementation is effective in detecting plagiarism in source code, although in some cases manual checking is needed to confirm the presence of plagiarism.
- Full Text:
- Date Issued: 2020
- Authors: Bradshaw, Karen L , Chindeka, Vongai
- Date: 2020
- Subjects: To be catalogued
- Language: English
- Type: text , book
- Identifier: http://hdl.handle.net/10962/440260 , vital:73761 , ISBN 9783030356286 , https://doi.org/10.1007/978-3-030-35629-3_14
- Description: Plagiarism is prevalent in most undergraduate programming courses, including those where more advanced programming is taught. Typical strategies used to avoid detection include changing variable names and adding empty spaces or comments to the code. Although these changes affect the visual components of the source code, the underlying structure of the code remains the same. This similarity in structure can indicate the presence of plagiarism. A system has been developed to detect the similarity in the structure of student programs. The detection system works in two phases: The first phase parses the source code and creates a syntax tree, representing the syntactical structure of each of the programs, while the second takes as inputs two program syntax trees and applies various comparison algorithms to detect their similarity. The outcome of the comparison allows the system to report a result from one of four similarity categories: identical structure, isomorphic structure, containing many structural similarities, and containing few structural similarities. Empirical tests on small sample programs show that the prototype implementation is effective in detecting plagiarism in source code, although in some cases manual checking is needed to confirm the presence of plagiarism.
- Full Text:
- Date Issued: 2020
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