Hello Christian
There is of course nice work that could be done, but it depends on which area
you would prefer to work. Referencing? Coverage? Geometry?
In this email I will assume coverage based on your coverage-jdbc plugin, but I
could develop a bit about Referencing if it can be useful. However in order to
give more detailed suggestions, it would help if we had some idea about when the
work would start (because the proposal may depends on ungoing work) and how long
you can work on it.
I would also like to know which kind of scientific theory you are looking for.
Is is computer science, mathematic or some application field (oceanography,
meteorology).
Below is a proposal applicable to oceanography which would require a good
background in mathematic. If you choose those kind of proposal, we would be glad
on our side to try to help you to achieve them.
Proposal Number #1
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In oceanography we have GridCoverage2D of different parameters
calculated from Remote Sensing data. Some of the most commons
parameters are:
- Sea Surface Temperature (°C)
- Chlorophyl-a concentration (mg/m³)
- Sea Level Anomaly (cm)
Unfortunatly some of those data may be missing because of weater
conditions. Sea Surface Temperature are not available if the sky
is cloudy, which is very common in tropical area. Sea Level Anomaly
can be available despite cloud cover, except if it is raining hard.
In some cases we really need some estimation of a missing parameter
even if it is just a very approximative idea. If a Sea Surface
Temperature value is missing because of a cloud cover, we can still
get some idea using other parameters because they usually have a
strong correlation. For example cold water is often associated with
low value of Sea Level Anomaly, and conversely (hot water is often
associated with high value of Sea Level Anomaly).
There is what we could do, most simplist approach first, more
elaborated approach later:
1) Compute the correlation between two arbitrary parameters
(in our example Sea Surface Temperature with Sea Level
Anomaly) using some historical data. Then when a Sea
Surface Temperature is missing, use the correlation for
computing an estimation of "probable" value using the
Sea Level Anomaly.
2) Above approach is very naive (real nature is much more
complex than the linear relationship assumed above). We
can still try the same idea, but replacing the linear
relationship by a neuronal network which has learn from
many parameters: Sea Level Anomaly, but also geographic
area, time of the year, wind speed, etc.
3) Above approach 2 is better than 1 but still not yet quite
satisfying. If give just one number (the temperature in our
example) while we would like to have some estimation of its
uncertanties. A value inferred in such indirect way from other
parameters is less "certain" than a direct measurement of Sea
Surface Temperature. Bayesian network may be a solution (but
I'm probably out of scope of a master thesis here).
I used "Sea Surface Temperature" vs "Sea Level Anomaly" above as
a real-world example (with real applications on our side), but
such a project would actually be against any arbitrary set of
geophysics parameters.
Proposal Number #2
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Same goals than above, but working on a single image without any attempt to
leverage the correlation between geophysics parameters:
http://sprott.physics.wisc.edu/pubs/paper276.htm
Is it the kind of suggestions you were looking for?
Martin
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