201 - Working with geotiff files using rasterio in python (also quick demo of NDVI calculation)

[@sergiomorellmonzo1780]

Last 3 videos was especially usefull for me! Thank you, thank you, thank you... Remote sensing users are grateful

[@murushyam]

Thanks for making such videos, I love it ❤️. Please make a video on this data using CNN multiple classification/ segmentation. Thank you.

[@rahishashahil4662]

Thank you so much for your effort, this was most awaited lecture to me.✌

[@tyhuffman5447]

This is why I love KZbin! Great lecture! Thank you.

[@khushpatelmd]

Making extremely complicated topic super easy and fun is a skill which will make this channel goldmine of AI tutorials.

[@lkledu2]

love this content, i spent sooo much time searching on how to do this. Thank you alot

[@vimalshrivastava6586]

This is really a valuable info. Thanks for making such videos. It will be very helpful if you can make a video on semantic segmentation using cnn on any satellite dataset.

[@DigitalSreeni]

I made a few videos already, please check other videos on my channel.

[@davidsalazar154]

This video was amazing ! Thank you so much !

[@XinhLe]

wow, so happy that my request and others came true.😍😍😍 Do you need to create a new conda env for this particular video? Another request :) Could you make a video on intensity imagery data normalisation? For example, I would like to compare (pixel by pixel comparison or classified/segmented images comparison) different intensity images to see detect the change of land cover overtime or seafloor changing overtime (or whatever it is the content we're observing). But, for some reasons (equipment is not calibrated, influenced of the environment during data acquisition ...), the intensity levels are not on the same scale even we are observing the same thing/area/objects. What are the different ways of normalising images for comparison and how to implement them in Python? Thanks so much! Xinh

[@mollywilliams738]

This video has saved me! Thank you so much :D

[@DigitalSreeni]

Glad it helped!

[@jacobusstrydom7017]

Thanks for this, very informative

[@kadircanidrisoglu5172]

Hey @DigitalSreeni thanks for the video! I have a question. Which file format do you recommend for building a deep learning model, .TIFF or .JPG ? You used JPG format in your other segmentation video for satellite imagery. I'm confused a little. Best regards..

[@vimalshrivastava6586]

One more informative video. Thank you for making such videos. If possible, please make a video on how to create masks corresponding to these satellite images that can be used to train UNet model. Thanks in advance...!!!

[@cassieferrante8740]

Great video!

[@nithinmohan3140]

Thank you...great video.

[@RajeshSharma-bd5zo]

Amazing 👍

[@AhmedEmamAI1]

please sreeni do some videos on hyperspectral images

[@nourhanhamdy8670]

Thanls alot, It is avery interesting topic. would you please make video of land use land cover classification for a satellite imagery with multi-bands

[@ismailelkhrachy3425]

Thank you Dr. Sreeni for you informative presentation. If i have only one satellite image, how can I prepare train and test data if i like to conduct multi layers clarification (4 or more buildings, roads, water and bare land) machine learning supervised algorithm.

[@ArchSrid]

Hi, Thank you for an easy approach. However I ma facing issue when I try installing rasterio which I didnt face with GDAL. Kindly help me out.

[@IMRANKHAN-lc7vx]

First of all thank you for your excellent videos. I had one question: Which ML technique to use if we have input(Set of parameters and Images) and output (A time step series of 3D images - Very large file). Kindly can you please give me a suggestion?

[@kulsoompanhwar190]

very nice sir

[@baharekl4708]

Thanks 🙏

[@yashpurbey567]

hello sir, everytime i try to open such bands using rasterio and print out my arrays they all come out as zeros. could you help me out with why thats happening?

[@W0lfbaneShikaisc00l]

I tried this out with a GEOTiff file but found that the file only reported 1 band, confused really not sure how I can use this if I can only detect 1 band.

[@surflaweb]

Cool

[@v.lobanov]

Thanks for the lecture! Is it possible to split image into smaller tiles, make classification and then reconstruct spatial reference?

[@DigitalSreeni]

Yes, of course. One of my upcoming videos will be about exactly that for semantic segmentation.

[@musabejeanbosco6902]

Is it possible to introduce us OSTU Algorthm, RF and MLP to the geotif files

[@ahtashamahmed9136]

still gdal & rasterio not working

[@PaulMoriarty]

The URL for images doesn't work. It redirects to Registry of Open Data on AWS

[@DigitalSreeni]

That is unfortunate. I hope you can find other sources to download geotiff files. It took a while for me to google search and identify this source.

[@thoudamyoihenba6407]

numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject got this error while importing rasterio. how do I solve this

[@DigitalSreeni]

You may be using the very latest python. In any case, please try uninstalling numpy and reinstalling it. pip uninstall numpy pip install numpy

[@thoudamyoihenba6407]

@@DigitalSreeni I got it. Removed that error. But now the problem is installing georasters, it can't be installed in any way given in ozak or other sites

[@DigitalSreeni]

Yeah, these libraries come with weird dependencies making it hard to install them. I managed to install rasterio, I never tried georaster.

[@Cloud-577]

what about OS?

[@satyamsuryawanshi102]

helllo sir can you plz make a video on satellite image farm boundary detection

[@ahtashamahmed9136]

can you help me sir?

[@Ekklo_]

I've sent u an email asking for some help, i'll so glad if u could take the time to read it and maybe help me

[@muhammadbudid7693]

sir, I want to ask about the results after numpy. I use Landsat TM C1 level 2, and the image showed the purple full, without showing the image, and it had min and max (-3.4028235e+38, 3.4028235e+38). Thank you

[@busracolak7799]

Thanks 🙏

[@DigitalSreeni]

You’re welcome 😊