Geospatial data almost always contains some amount of uncertainty due to inaccuracies in its acquisition and transformation. While the data is commonly visualized (e.g. on digital maps), there are unanswered needs for visualizing uncertainty along with it. Most research on effectively doing this addresses uncertainty in data values at geospatial positions, e.g. water depth, human population, or land-cover classification. Uncertainty in the data’s geospatial positions themselves (positional uncertainty) has not been previously focused on in this regard. In this thesis, techniques were created for visualizing positional uncertainty using World Vector Shoreline as an example dataset. The techniques consist of a shoreline buffer zone to which visual effects such as gradients, transparency, and randomized dots were applied. They are viewed interactively via Web Map Service (WMS). In clutter testing with human subjects, a transparency-gradient technique performed the best, followed by a solid-fill technique, with a dots-density-gradient technique performing worst.

Approved for public release, distribution is unlimited / The objective of this thesis is to provide an aircraft with an optimal route to its destination that avoids encroaching into surface-to-air weapons killing envelopes in real time. The optimal route computed will be updated dynamically, depending on the location of the vehicle and the location of the Surface to Air Missile (SAM) sites. The problem was solved using heuristic algorithms instead of the conventional Dijkstra's & Bellman Ford algorithms, which are computationally expensive. Data fusion techniques such as spatial correlation and triangulation algorithms are presented in detail. Such techniques are important for situational awareness in a real time combat environment. Important information provided by onboard sensors are merged with the preplanned data to provide the operator with a better integrated picture of the combat environment. / Civilian, Singapore Ministry of Defense