Throughout history, geographical information has been vital in different contexts, such as national security matters, economics, geopolitics, military, and natural resources. Due to the various applications, geographical information has been handled as valuable and sensitive information. As technology evolves, geographical information is becoming increasingly more available. This thesis investigates the data attributes relevant to its sensitivity and creates an information security classification model suitable for the satellite imagery produced, analyzed, and maintained by Maxar Technologies Ltd. All geographical information is of value. Everything from terrain information to protected areas where features such as roads, critical infrastructure, and buildings are of extra interest. Other factors that affect the sensitivity of the imagery are the resolution, amount of information, type of files (3D or other processed data), legislation, and more. The methodology used to achieve this consisted of two major parts, a risk assessment procedure and translating risk contexts and parameters into a classification model. The classification levels identified are PUBLIC, VALUABLE, SENSITIVE, and CLASSIFIED. A classification model is defined for individual imagery and a separate model for projects. A project gets at least the same classification as the highest classed file and other contexts that may affect the sensitivity. Lastly, the thesis explore automation possibilities and a supervised learning approach is tested on the model created for the classification of files. Various machine learning models are fitted to a dataset that is collected from the satellite imagery products of Maxar and manually classed using the defined classification levels. F-score and MCC are used to evaluate the automation. These are metrics based on the occurrences of false positives and negatives. Furthermore, the thesis discusses topics related to the sensitivity of geographical information and how to handle such information. This thesis tries to lay the foundation for many future work possibilities.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-186206 |
| Date | January 2022 |
| Creators | Elander, Marcus, Gunnarsson, Philip |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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