title: Teaching about volcanoes: Practices, perceptions, and implications for professional development

authors: Jolley, Alison

abstract: Volcanology education is important for the development of geoscientists and scientifically literate citizens. We surveyed 55 volcanology instructors to determine their learning and teaching practices, perceptions of academic development, and educational support needs. Instructors reported using a wide range of practices and tools, but lectures, field experiences, maps, rock samples, academic literature, and inherited teaching materials are the most common. Instructors valued educational support from others (e.g., talking with colleagues and students, consulting with learning and teaching specialists) over conducting their own investigations. However, they did not report engaging in as many of these activities as they valued. Instructors requested more support in resource sharing and collation, conference workshops, and co-creation of resources and educational research. We suggest that instructors and academic development staff work together to share and build knowledge in the learning and teaching of volcanology in higher education, and to improve student learning outcomes.

text: Teaching about volcanoes: Practices, perceptions, and implications for professional development Volcanology education is important for the development of geoscientists and scientifically literate citizens. We surveyed 55 volcanology instructors to determine their learning and teaching practices, perceptions of academic development, and educational support needs. Instructors reported using a wide range of practices and tools, but lectures, field experiences, maps, rock samples, academic literature, and inherited teaching materials are the most common. Instructors valued educational support from others (e.g., talking with colleagues and students, consulting with learning and teaching specialists) over conducting their own investigations. However, they did not report engaging in as many of these activities as they valued. Instructors requested more support in resource sharing and collation, conference workshops, and co-creation of resources and educational research. We suggest that instructors and academic development staff work together to share and build knowledge in the learning and teaching of volcanology in higher education, and to improve student learning outcomes.

year: 2022

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: The source, composition and typology of ‘Limestone’ Adzes from Eastern North Island, New Zealand

authors: Nelson, Campbell S.

subjects: adzes

abstract: Collections of stone adzes (toki) from the eastern North Island of New Zealand include a number of typologically early (Archaic) forms made from what has been previously described as ‘siliceous or silicified limestone’. Seventy five finished adzes and preforms of this material were recorded in the present study. Their geographic distribution is primarily restricted to southern Hawkes Bay – Wairarapa, and they apparently originated from a single manufacturing centre at Owahanga, on the northern Wairarapa coast. Chemical and mineralogical analyses (by X-ray fluorescence, portable XRF, vacuum gasometry and X-ray diffraction) of two artefacts from Owahanga show they contain about 20–30 per cent CaCO3 (as calcite), while samples of the presumed local source rock have a slightly higher carbonate content but comparable silica (quartz) and Rb concentrations. The rock type used in the manufacture of adzes was therefore not a true limestone but calcareous mudstone, compositionally distinct from siliceous limestones in southern Wairarapa and probably derived from the Whangai Formation. Possible reasons for the use of this stone material are discussed.

text: The source, composition and typology of ‘Limestone’ Adzes from Eastern North Island, New Zealand Collections of stone adzes (toki) from the eastern North Island of New Zealand include a number of typologically early (Archaic) forms made from what has been previously described as ‘siliceous or silicified limestone’. Seventy five finished adzes and preforms of this material were recorded in the present study. Their geographic distribution is primarily restricted to southern Hawkes Bay – Wairarapa, and they apparently originated from a single manufacturing centre at Owahanga, on the northern Wairarapa coast. Chemical and mineralogical analyses (by X-ray fluorescence, portable XRF, vacuum gasometry and X-ray diffraction) of two artefacts from Owahanga show they contain about 20–30 per cent CaCO3 (as calcite), while samples of the presumed local source rock have a slightly higher carbonate content but comparable silica (quartz) and Rb concentrations. The rock type used in the manufacture of adzes was therefore not a true limestone but calcareous mudstone, compositionally distinct from siliceous limestones in southern Wairarapa and probably derived from the Whangai Formation. Possible reasons for the use of this stone material are discussed.

year: 2016