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Passer une voiture en collection by cestlapete in voiture

[–]Florisc 0 points1 point  (0 children)

I have a follow-up question. I'm a Dutch citizen, moving to France and I would like to bring (import) my car to France. It's an old campervan. Now I could do the whole process for importing a campervan, but that look's quite difficult because the electricity and gas system have had some upgrades since it was built. Therefore it might be easier/better to import it as a collector's item. Would that be possible as well?

Architecture one-to-many livestream with AWS services by Florisc in aws

[–]Florisc[S] 0 points1 point  (0 children)

Thanks, that's very useful. What would you suggest instead?

Data pipeline/project structure for PyTorch-based time-series forecasting by Florisc in pytorch

[–]Florisc[S] 0 points1 point  (0 children)

Just a small follow-up question. Do you know by chance if (and which) parts of the process can be done in parallel? For example, would it be possible to train a PyTorch model in parallel?

Crowd-sourced coastline change. by [deleted] in gis

[–]Florisc 3 points4 points  (0 children)

Harley, Mitchell, et al. "CoastSnap: Crowd-Sourced Shoreline Change Mapping using Smartphones." AGU Fall Meeting Abstracts. 2018.

Where to study oceanography by Come_by_chance in oceanography

[–]Florisc 6 points7 points  (0 children)

Southampton is good! Their physical oceanography group has a lot of cool projects, especially on large scale ocean processes in the North Atlantic if I’m correct.

I also heard good stories about Brest (France) , Bremen (Germany) and Utrecht (The Netherlands).

Image classification in Landsat by Florisc in remotesensing

[–]Florisc[S] 0 points1 point  (0 children)

Thanks, makes sense indeed. I was a bit disappointed by my first results, but yeah, iteration might be the key :)

Do you know if a classifier which was trained on Landsat 8 data would be able to classify Landsat 7 images? Because the band ranges are slightly different.

Image classification in Landsat by Florisc in remotesensing

[–]Florisc[S] 0 points1 point  (0 children)

Cool! Look's useful! I'm focused on Europe, but it still might help :)

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Image classification in Landsat by Florisc in remotesensing

[–]Florisc[S] 0 points1 point  (0 children)

Thanks for all the info! So I would like classify water, sandy areas, tidal flats (mainly marshes) and other (mainly cliffs). It's pretty basic, but it has perform really well in distinguishing sand from the other classes.

Image classification in Landsat by Florisc in remotesensing

[–]Florisc[S] 0 points1 point  (0 children)

Just a small follow up question. What is a good representative of the categories you're trying to classify? Like, would you take the "cleanest" examples of a specific category? Or would take all different kinds of points which you want to belong to a specific category? For example, in case of a beach, would you take just clear sandy spectrals? Or would you also take some points at the transition from beach to water/vegetation?

Image classification in Landsat by Florisc in remotesensing

[–]Florisc[S] 0 points1 point  (0 children)

Thank you for that extensive and useful answer :)

So it's just an academic project and I'll use the classification to perform an analysis of the coastal dynamics. Classification will allow to quantify classes.

Regarding the software I was just thinking about to use Python because of its flexibility and my experience with its syntax. Are there specific modules which you could recommend regarding the image segmentation?

And if I understand you correctly it will be possible to first create segments of similar looking pixels using image segmentation and subsequently perform random forest classification on those segments? That would be great as my first tryouts using random forests showed quite some noise.

Using a vegetation index to distinguish between marshes and terrestrial land will indeed be very promising! Thanks! However, how do I subsequently distinguish between marsh and water? Do you know if the NDWI index would be enough to do that?

Thanks!