I have downloaded several HDF files from the MODIS database.
According to the documentation, the layers have to be multiplied by 0.1 to obtain the real values.
I get an error when I put the name of the HDF-layer in the Raster Calculator, however it does work when I export it as a new raster before. But after multiplication with 0.1, I still do not get a continuous scale image but only black and white areas. I excluded the seven highest values as indicated in the documentation, but still no change.
Another way of getting the MODIS files is to use the respective toolbox. Data imported with this tool does show up correctly, but I cannot import most of it even though it is available under the link indicated above:
Failed to execute (CreateCustomGeoTransformation)
Failed to execute (ImportEvapotranspiration)
Has anyone experienced something similar?
Related
How to have continuous features for a shapefile ?
I mean NOT cut by the dateline to respect [-180:180] longitude excursion that I do not
want to respect.
Here is an example where I display the Russia shapefile in a leaflet map.
In fact I would like to have continuous continent.
Shapefile comes from
https://gadm.org/about.html
Any command from gdal or ogr2ogr to merge separated features ?
Thanks
If you load the GADM level-0 layer into QGIS and toggle Show Feature Count, you'll see that, even though the shape seems split, the actual layer only has a single feature:
Your shape gets cut off because the polygon crosses the boundary in the projection you are using and gets wrapped around. This doesn't mean the features get actually split.
If you want to display it as a continuous feature, you need to specify an appropriate projection. For instance, using the example here gives me this:
This is just one way, there might be different projections that fit your purpose better. Also, getting this done in leaflet is a different question.
I have been reading OCR papers such as this one https://arxiv.org/pdf/1704.08628.pdf , and I am have trouble finding out how these datasets are actually generated.
In the linked paper, they use a regressor to predict the start location (a point) and height of a line of text. Then, based on that starting point and height, a second network performs OCR and end of line detection. I realize this is a very simplified explanation, but it follows that their dataset consists (at least in part) of full page text 'images' annotated with where each line begins, and then a transcription of the text on a given line. Alternatively, they could have just used the lower left point of bounding boxes as the start point and the height of the box as the word height (avoiding the need to re-annotate if the data was previously prepared using bounding boxes).
So how is a dataset like this actually created? Looking at other datasets it seems like there is some software that can create XML files containing the ground truths relevant to each image, can someone point me in the right direction? I've been googling around and finding lots of tools for annotating text with sentiment etc and other tools for annotating images for segmentation (for something like a YOLO network), but I'm coming up empty for the creation something like the Maurdoor dataset used in the linked paper.
Thank you
So after submitting this, the related threads window showed me many threads that my googling did not turn up. This http://www.prima.cse.salford.ac.uk/tools software seems to be what I was looking for, but I would still love to hear other ideas.
Suppose we have two similar plots.
Plot1 (already published in a paper)
Plot2 (calculated by using any software)
My question is: How can I compare my calculated plot (pdf, png, jpeg, etc) with the plot in the paper.
Thank You
To the best of my knowledge, there is currently no software that would enable you to re-convert images into their nominal data.
However, it's not that hard to write a piece of code that does it.
Here are the steps (at a high level):
extract the images from the pdf document (use iText)
separate out those images that look like a plot. You can train a neural network to do this, or you can simply look for images that contain a lot of white (assuming that's the background) and have some straight lines in black (assuming that's the foreground). Or do this step manually.
Once you have the image(s), extract axis information. I'll assume a simple lineplot. Extract the minimal x and y value, and the maximum x and y value.
separate out the colors of the lines in your lineplot, and get their exact pixel coordinates. Then, using the axis information, scale them back to their original datapoint.
apply some kind of smoothing algorithm. E.g. Savitzky-Golay
If you ever use this data in another paper, please mention that you gathered this data by approximation of their graph. Make it clear you did not use the original source data.
Reading material:
https://developers.itextpdf.com/examples
https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter
https://docs.oracle.com/javase/tutorial/2d/images/index.html
I have an undocumented tiff image which I need to use with a software that can read only geotif files. my simplest idea was to pretend the image is at 0N, 0W with a pixel size of 0.00000899928° (1m) in both directions.
I have rea the thread here but I was unable to reproduce the answer.
Thanks for helping. I am a dummy in geodesics, GIS and the like.
You are attempting to georeference a raster, which is often a difficult task, with multiple techniques. It's not possible to provide an answer for your question given the information you have supplied. Also, never assume that lengths in degrees can be converted to lengths in metres (the Earth isn't flat).
Search around GIS.SE for ideas , e.g. using the [georeferencing] tag. There are tools available with QGIS to help manually georeference rasters to other geospatial data.
I just downloaded libsvm-3.17 abt two weeks ago. I tried heart_scale (dataset provided in the libsvm-3.17 package) with easy.py. An image or plot is produced (from gnuplot) to illustrate the best c and best gamma. I cannot post the image here because I am new here and do not have enough reputation.
I would like to ask from the many colors curves in the plot, how to extract from the plot that the best log2(c)=11 (which gives c=2048) and the best log2(gamma)=-13 (which gives gamma = 0.0001220703125).
Thank you very much.
the chosen parameters are reported by easy.py (cannot run it now, but you will find them). the plot is just a visual aid to manually verify the parameter neighborhood. with some experience you can interpret the diagram. without experience simply trust easy.py