Alexandria Dominique Farias | University of Cape Town (original) (raw)
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Papers by Alexandria Dominique Farias
New Space, 2020
Solar-geomagnetic superstorms like the 1859 Carrington Event pose significant risks to electrical... more Solar-geomagnetic superstorms like the 1859 Carrington Event pose significant risks to electrical power utilities and satellite infrastructure. Damage to this critical infrastructure could have significant legal and ethical ramifications, ranging from worldwide economic crises to international disaster response needs of unseen magnitude. Despite these potential consequences, the legal and ethical frameworks for space weather risk management are only just beginning to be developed by regulatory agencies. Furthermore, existing international regulations and ethical standards are designed for typical natural disasters (i.e., floods, droughts, earthquakes, and hurricanes) and may not account for the additional complexity and cascading effects associated with a future Carrington Event, such as combined and large-scale loss of electrical grid and satellite capabilities in countries that typically provide, rather than receive, disaster relief. To address this insufficiency, this article outlines the legal and ethical issues associated with solar-geomagnetic superstorms and provides recommendations for incorporating specific action plans into existing international agreements, national policies, commercial space best practices, and international disaster response law guidelines.
8th International Conference on Signal, Image Processing and Pattern Recognition
The data produced from Earth Observation (EO) satellites has recently become so abundant that man... more The data produced from Earth Observation (EO) satellites has recently become so abundant that manual processing is sometimes no longer an option for analysis. The main challenges for studying this data are its size, its complex nature, a high barrier to entry, and the availability of datasets used for training data. Because of this, there has been a prominent trend in techniques used to automate this process and host the processing in massive online cloud servers. These processes include data mining (DM) and machine learning (ML). The techniques that will be discussed include: clustering, regression, neural networks, and convolutional neural networks (CNN). This paper will show how some of these techniques are currently being used in the field of earth observation as well as discuss some of the challenges that are currently being faced. Google Earth Engine (GEE) has been chosen as the tool for this study. GEE is currently able to display 40 years of historical satellite imagery, including publicly available datasets such as Landsat, and Sentinel data from Copernicus. Using EO data from Landsat and GEE as a processing tool, it is possible to classify and discover historical algal blooms over the period of ten years in the Baltic Sea surrounding the Swedish island of Gotland. This paper will show how these technical advancements including the use of a cloud platform enable the processing and analysis of this data in minutes.
The data produced from Earth Observation (EO) satellites has recently become so abundant that man... more The data produced from Earth Observation (EO) satellites has recently become so abundant that manual processing is sometimes no longer an option for analysis. The main challenges for studying this data are its size, its complex nature, a high barrier to entry, and the availability of datasets used for training data. Because of this, there has been a prominent trend in techniques used to automate this process and host the processing in massive online cloud servers. These processes include data mining (DM) and machine learning (ML). The techniques that will be discussed include: clustering, regression, neural networks, and convolutional neural networks (CNN). This paper will show how some of these techniques are currently being used in the field of earth observation as well as discuss some of the challenges that are currently being faced. Google Earth Engine (GEE) has been chosen as the tool for this study. GEE is currently able to display 40 years of historical satellite imagery, including publicly available datasets such as Landsat, and Sentinel data from Copernicus. Using EO data from Landsat and GEE as a processing tool, it is possible to classify and discover historical algal blooms over the period of ten years in the Baltic Sea surrounding the Swedish island of Gotland. This paper will show how these technical advancements including the use of a cloud platform enable the processing and analysis of this data in minutes.
The data produced from Earth Observation (EO) satellites has recently become so abundant that man... more The data produced from Earth Observation (EO) satellites has recently become so abundant that manual processing is sometimes no longer an option for analysis. The main challenges for studying this data are its size, its complex nature, a high barrier to entry, and the availability of datasets used for training data. Because of this, there has been a prominent trend in techniques used to automate this process and host the processing in massive online cloud servers. These processes include data mining (DM) and machine learning (ML). The techniques that will be discussed include: clustering, regression, neural networks, and convolutional neural networks (CNN).
New Space, 2020
Solar-geomagnetic superstorms like the 1859 Carrington Event pose significant risks to electrical... more Solar-geomagnetic superstorms like the 1859 Carrington Event pose significant risks to electrical power utilities and satellite infrastructure. Damage to this critical infrastructure could have significant legal and ethical ramifications, ranging from worldwide economic crises to international disaster response needs of unseen magnitude. Despite these potential consequences, the legal and ethical frameworks for space weather risk management are only just beginning to be developed by regulatory agencies. Furthermore, existing international regulations and ethical standards are designed for typical natural disasters (i.e., floods, droughts, earthquakes, and hurricanes) and may not account for the additional complexity and cascading effects associated with a future Carrington Event, such as combined and large-scale loss of electrical grid and satellite capabilities in countries that typically provide, rather than receive, disaster relief. To address this insufficiency, this article outlines the legal and ethical issues associated with solar-geomagnetic superstorms and provides recommendations for incorporating specific action plans into existing international agreements, national policies, commercial space best practices, and international disaster response law guidelines.
8th International Conference on Signal, Image Processing and Pattern Recognition
The data produced from Earth Observation (EO) satellites has recently become so abundant that man... more The data produced from Earth Observation (EO) satellites has recently become so abundant that manual processing is sometimes no longer an option for analysis. The main challenges for studying this data are its size, its complex nature, a high barrier to entry, and the availability of datasets used for training data. Because of this, there has been a prominent trend in techniques used to automate this process and host the processing in massive online cloud servers. These processes include data mining (DM) and machine learning (ML). The techniques that will be discussed include: clustering, regression, neural networks, and convolutional neural networks (CNN). This paper will show how some of these techniques are currently being used in the field of earth observation as well as discuss some of the challenges that are currently being faced. Google Earth Engine (GEE) has been chosen as the tool for this study. GEE is currently able to display 40 years of historical satellite imagery, including publicly available datasets such as Landsat, and Sentinel data from Copernicus. Using EO data from Landsat and GEE as a processing tool, it is possible to classify and discover historical algal blooms over the period of ten years in the Baltic Sea surrounding the Swedish island of Gotland. This paper will show how these technical advancements including the use of a cloud platform enable the processing and analysis of this data in minutes.
The data produced from Earth Observation (EO) satellites has recently become so abundant that man... more The data produced from Earth Observation (EO) satellites has recently become so abundant that manual processing is sometimes no longer an option for analysis. The main challenges for studying this data are its size, its complex nature, a high barrier to entry, and the availability of datasets used for training data. Because of this, there has been a prominent trend in techniques used to automate this process and host the processing in massive online cloud servers. These processes include data mining (DM) and machine learning (ML). The techniques that will be discussed include: clustering, regression, neural networks, and convolutional neural networks (CNN). This paper will show how some of these techniques are currently being used in the field of earth observation as well as discuss some of the challenges that are currently being faced. Google Earth Engine (GEE) has been chosen as the tool for this study. GEE is currently able to display 40 years of historical satellite imagery, including publicly available datasets such as Landsat, and Sentinel data from Copernicus. Using EO data from Landsat and GEE as a processing tool, it is possible to classify and discover historical algal blooms over the period of ten years in the Baltic Sea surrounding the Swedish island of Gotland. This paper will show how these technical advancements including the use of a cloud platform enable the processing and analysis of this data in minutes.
The data produced from Earth Observation (EO) satellites has recently become so abundant that man... more The data produced from Earth Observation (EO) satellites has recently become so abundant that manual processing is sometimes no longer an option for analysis. The main challenges for studying this data are its size, its complex nature, a high barrier to entry, and the availability of datasets used for training data. Because of this, there has been a prominent trend in techniques used to automate this process and host the processing in massive online cloud servers. These processes include data mining (DM) and machine learning (ML). The techniques that will be discussed include: clustering, regression, neural networks, and convolutional neural networks (CNN).