# Predicting change of shapes/coordinates

I'm trying to find a way to predict/calculate how a shape (e.g. outline of a glacier) will change in the future—based on its history (previous shape) and additional factors (e.g. Δtemperature).

In my example: I have the shape/coordinates of a glacier and an average temperature at 1970, 1985, 2000, 2015. How can I give an estimate on how that shape will look like in 2030, based on the previous shapes and a predicted temperature?

The shapes would ideally come in a format similar to this:

[
[
[-113.74926783355818, 48.32440832757374],
[-113.74926767330584, 48.32440813255405],
[-113.74926748239692, 48.32440458296232],
[-113.74926717549286, 48.32439887665663],
[-113.74926686858888, 48.32439317035087],
...
],
[
[-113.75763099733634, 48.32877681033601],
[-113.75763955455557, 48.32877660502744],
[-113.75764546550784, 48.32878027200765],
[-113.75765402272751, 48.32878006669797],
[-113.75766003497893, 48.32878563609638],
...
]
]


But I imagine it could be necessary to convert this to coordinate offsets (Δx, Δy) similar to what sketch_rnn is doing.

Any pointers would help. Even that this is too ambitious. Thanks!

Disclaimer: I'm a beginner with this and hope the question isn't too naïve :)

• If you could partition the area into points, where each point either appears of disappear, using sequential models to encode the evolution overtime May 23, 2018 at 18:24
• This a fabulous but ambitious question. I would start by thinking about shape representation, then learn the warp or deformation. Take a look at DeepWarp and Deformation Based Curved Shape Representation (code). Let the date be a feature. The only problem is that you need more than four data points, so go and get some. At least get pictures of other glaciers so your model can learn the manifold of glaciers if not their temporal dynamics.
– Emre
May 23, 2018 at 22:19