title: Super-resolution of satellite imagery

authors: Bull, Daniel

subjects: Super-resolution

abstract: Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

text: Super-resolution of satellite imagery Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

year: 2021

title: Substantive and procedural legislation in the Philippines to combat webcam-related child sexual abuse

authors: Dizon, Michael

abstract: This chapter examines the criminal laws and procedural rules in the Philippines that apply to webcam or online child sexual abuse. It analyzes the relevant treaties, legislation and case law, particularly those relating to sexual offenses involving minors and cybercrime investigations. The chapter also tackles specific legal issues concerning the use of Sweetie 2.0 in combating webcam-related child sexual abuse including child pornography, cybersex, child sex tourism, entrapment, and criminal investigations in an online context. Based on the research, many of the sex-related crimes under Philippine law can be used to investigate and prosecute the sexual abuse and exploitation of real children and minors via webcam or other similar means or media. However, most of these crimes do not apply in the case of Sweetie 2.0 because they require as an essential element for their commission the involvement of a real child or minor victim. As such, the possible crimes that may be committed in relation to Sweetie 2.0 are limited to cybersex, attempted cybersex, grooming, and luring. Also, since the general rule is that criminal investigations can only conducted when a crime has been, is being or is about to be committed, Sweetie 2.0 can only be used in the first stance to investigate these four offenses.

text: Substantive and procedural legislation in the Philippines to combat webcam-related child sexual abuse This chapter examines the criminal laws and procedural rules in the Philippines that apply to webcam or online child sexual abuse. It analyzes the relevant treaties, legislation and case law, particularly those relating to sexual offenses involving minors and cybercrime investigations. The chapter also tackles specific legal issues concerning the use of Sweetie 2.0 in combating webcam-related child sexual abuse including child pornography, cybersex, child sex tourism, entrapment, and criminal investigations in an online context. Based on the research, many of the sex-related crimes under Philippine law can be used to investigate and prosecute the sexual abuse and exploitation of real children and minors via webcam or other similar means or media. However, most of these crimes do not apply in the case of Sweetie 2.0 because they require as an essential element for their commission the involvement of a real child or minor victim. As such, the possible crimes that may be committed in relation to Sweetie 2.0 are limited to cybersex, attempted cybersex, grooming, and luring. Also, since the general rule is that criminal investigations can only conducted when a crime has been, is being or is about to be committed, Sweetie 2.0 can only be used in the first stance to investigate these four offenses.

year: 2019

title: An evaluation of Te Rau Puawai workforce 100: Academic mentor's perspectives

authors: Nikora, Linda Waimarie

subjects: Maori students

abstract: To evaluate the Te Rau Puawai programme, the Ministry of Health commissioned the
Maori and Psychology Research Unit of the University of Waikato in July 2001. The
overall aim of the evaluation was to provide the Ministry with a clearer understanding
of the programme including: the perceived critical success factors, the barriers if any
regarding Te Rau Puawai, the impact of the programme, the extent to which the
programme may be transferable, gaps in the programme, and suggested
improvements.
As an integral component of the Te Rau Puawai programme and as a specialised
source of support, academic mentors were identified as an important group to receive
comment from. This report documents the evaluative exchange that occurred by way
of email questionnaire, that we had with 18 out of 46 academic mentors of Te Rau
Puawai bursars in the College of Humanities and Social Sciences responded to our
questionnaire.

text: An evaluation of Te Rau Puawai workforce 100: Academic mentor's perspectives To evaluate the Te Rau Puawai programme, the Ministry of Health commissioned the
Maori and Psychology Research Unit of the University of Waikato in July 2001. The
overall aim of the evaluation was to provide the Ministry with a clearer understanding
of the programme including: the perceived critical success factors, the barriers if any
regarding Te Rau Puawai, the impact of the programme, the extent to which the
programme may be transferable, gaps in the programme, and suggested
improvements.
As an integral component of the Te Rau Puawai programme and as a specialised
source of support, academic mentors were identified as an important group to receive
comment from. This report documents the evaluative exchange that occurred by way
of email questionnaire, that we had with 18 out of 46 academic mentors of Te Rau
Puawai bursars in the College of Humanities and Social Sciences responded to our
questionnaire.

year: 2002-05-01