Crop Land Classification in Earth Engine [GEE]
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- Опубликовано: 3 янв 2022
- The uploaded content is the learning and interpreting attempt of the crop classification in earth engine. In this video, we are summarizing about the whole process in a short possible way, to make ourselves aware. We have not talked about so many things and delved a little to incite our curiosity.
It is sure to be least helpful for any advanced learners who are already aware about such classification procedures.
The codes are from the community section from the earth engine guides. This particular example uses RF and CART classifier to classify the maize growing area and the non-maize area.
The example is very helpful for new learners to be able to see how the classification such as crop classification works.
Please follow the link for more detailed description: developers.google.cn/earth-en...
Any comments or suggestions are welcomed.
Thanks!
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#earthengine
#cropclassification
#Nigeria
![Geometry in Earth Engine [GEE]](http://i.ytimg.com/vi/i8rwIgpt70M/mqdefault.jpg)
![Geometry in Earth Engine [GEE]](/img/tr.png)
![Rice Mapping using Sentinel 1, 2 in Earth Engine [GEE]](http://i.ytimg.com/vi/We9VoweOuhM/mqdefault.jpg)
Great thanks!!
:-)
good work
Thankyou sir
Please how did you get the ground truth data ?
Hi, thank you for the useful tutorial! I have a question, when I try to run the classifier on a large area, the bug came out that out of memory. I'm wondering if you have the same situation and how do you solve this problems?
thankyou for the question.
The easy solution would be to decrease the study area/region if that is okay to go with. This might not always be possible if you just want all the area. My approach would be to try breaking the study area into smaller area, then merge the classified results as the single result. This is possible when you are doing smaller districts, and then you merge the classified results as one single image.
The other option can also be changing with the pixel values, doing the classification with the scale at 10m would also result the error, may be trying it more coarse might help.
The other thing is we get the out of memory due to the specific case, and you can try solving particularly in that way. Please search the multiple solutions in stack exchange which may be helpful. Here is one discussion, similar to ours. gis.stackexchange.com/questions/342792/user-memory-limit-exceeded-error-for-classification-in-google-earth-engine
If you are more aware, try performing the same analysis with same approach in planetary computer, may be that will be helpful.
Hope my reply was helpful.
You can reply here later, how you solved this.
Thankyou
great video, could you please tell me how can we classify our crops into different crop types?
For classification, we will need field information of that crops. The spectral information of that crop field can be used to train the model helping further to distinguish the whole boundary/area. This is my current understanding.
For, classifying considering several things are of importance. The first will sure be to dive in many articles.
Hope my reply helps.
Thankyou
How can we mask the forest with crop?
For this, we can work with land cover dataset. We can select any bands related with the forest. Then the mask is applied with the updateMask. Please check this helpful section from earth engine guide.
developers.google.cn/earth-engine/tutorials/tutorial_api_05?hl=zh-cn#masking
Depending on the need of yours, try searching gis-stackexchange, there might be questions similar to what your are asking.
I hope my reply helps.
-Thanks
Can U plz send me the step-by-step procedure so that it's easy for me to understand what was done in this project.....!!!!!
Hi, you can check the link in description section. Hope that helps. You can message me if you want to discuss a little more. Thankyou
How to see Shurb Land in GEE?
One way will be to check MODIS landcover 500 m. Or ESA world cover 10 m.
The other ways might be trying to classify the images.
Thankyou
@@ksabmagar7 Thanks
I still have question to you if you do not mind, what are your var values0 ,var values1, var values2 used for, I still not understand in your explanation in your video, you have 30 classes? which means your paddy has 30 classes you have to separate? thanks in advance!
var values0 = [1, 2, 3, 4,5,
6,7,8,9,10,
11,12,13,14,15,
16,17,18,19,20,
21,22,23,24,25,26,27,28,29,30];
var values1 = [0, 0, 0, 1,0,
1,0,1,0,0,
0,0,1,0,0,
1,1,0,0,0,
0,0,0,0,0,
0,0,0,0,0];
var values2 = [0, 0, 0, 2,0,
3,0,4,0,0,
0,0,1,0,0,
2,3,0,0,0,
0,0,0,4,0,
0,0,0,0,0]; why only values0, values1 and values2, is there any other values you need to modify?
yes, you can question as much as you like. this helps me too, i will try to answer if I can.
The remap function is used to rename the clusters. Yes, we had 30 clusters. We are regrouping it to rice and non-rice by using the remap function.
You can check more about this function from the reference in earthengine.
developers.google.com/earth-engine/apidocs/ee-image-remap
Thankyou
how can I apply VHR in GEE?WHAT IS THE NAME OF VHR IN GEE?
There are few datasets in earth engine with highres in earth engine. developers.google.com/earth-engine/datasets/tags/highres
But, it covers only some area. May be that helps.
How to improve the validation accuracy?
Well, this cannot be said for sure. It depends on type of data mainly how big it is, analysis we are doing, what validation accuracy we are aiming for. It might also depend on type of model we are applying. I think if you consider what you are doing, then the validation accuracy will be within that. I hope you find your answer in your learnings. Thankyou for asking it here. : )
@@ksabmagar7 I am doing landuse landcover classification and everytime validation accuracy is low. Even i took more than 300 training samples for each land cover.
@@omaizamalik229 How are you taking the training samples? And which models are you trying? May be trying with different models, and collecting training points from higher resolution might help. Also doing the classification in time series images not just in single image might help. Please try looking more articles.
I also cannot suggest that much as classification approaches have multiple approaches. Hope it helps.
can you share the code please?
Hello, there the codes are from the tutorial page, developers.google.cn/earth-engine/tutorials/community/classify-maizeland-ng?hl=zh-cn thankyou
VHR IN GEE it seems that there is not China's image?
Probably not, I believe there already must be different resources for that, but not in GEE as we see. Thankyou
Please how did you get the ground truth data ?
If you are asking about the general process, usually large number of volunteers help in the collection of the ground data, Or the researchers themselves may travel a lot collecting the locational information.
Regarding the codes in the video, these are adapted from the earth engine tutorials. As mentioned in the page, the data were collected by teams who worked on TAMASA project.
Getting the ground truth data is an intensive process. But other ways may be getting it from high resolution image, such as images from Google Earth.
Hope my reply helps.
High resolution sentinel images? How please