Landslide Detection using Deep learning Neural Network | Landslide4Sense | GeoDev

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Landslides are a natural phenomenon with devastating consequences, frequent in many parts of the world. Thousands of small and medium-sized ground movements follow earthquakes or heavy rainfalls. Landslides have become more damaging in recent years due to climate change, population growth, and unplanned urbanization in unstable mountain areas. Early landslide detection is critical for quick response and management of the consequences. Accurate detection provides information on the landslide's exact location and extent, which is necessary for landslide susceptibility modelling and risk assessment.
Recent advances in machine learning and computer vision combined with the growing availability of satellite imagery and computational resources have facilitated rapid progress in landslide detection. Landslide4Sense aims to promote research in this direction and challenges participants to detect landslides around the globe using multi-sensor satellite images. The images are collected from diverse geographical regions offering an important resource for remote sensing, computer vision, and machine learning communities.
Timestamp,
0:00 Background
1:22 Introduction
4:12 Installation of Libraries and basic setup
5:37 Testing the dataset
8:42 Creating training dataset
13:43 Custom loss function (Dice Loss)
14:33 Visualization of train dataset
15:00 Train/test split data
16:50 UNet model for segmentation
21:12 Model testing
23:25 Prediction for validation data
26:20 Writing the prediction into h5 format
27:55 Summary and final note
#landslide #deeplearning #neuralnetwork #landslide4sense
Tutorial notebook: github.com/iamtekson/landslid...
Landslide4Sense challenge and data: www.kaggle.com/datasets/tekba...
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Опубликовано:

1 авг 2024

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Комментарии : 57

@mario5554 2 дня назад

Great Tutorial!!! Congratulations, and of caourse, thanks for not being jealous with your knowledge and thanks for sharing everything you did.

@geodev 2 дня назад

My pleasure! Glad you liked it!

@MrSafeerUllah 2 года назад

I really appreciate it, so far the best video about the ML application in natural hazards.

@geodev 2 года назад

More contents related to ML/DL are coming on satellite imagery. Stay tuned!

@Sandhya_Bytes 6 месяцев назад

Sir i dont know how to express my happiness for this wonderful content, This is really very helpful for my academic project. Thank u thank u sooooooo much. We need more content like this...........

@geodev 6 месяцев назад

It is my pleasure! All the best and stay tuned for future similar tutorials

@nemeziz_prime 2 года назад

Great video 👍🏻 hope you make more such tutorials

@geodev 2 года назад

Thank you, Sure I will create more tutorials on deep learning. Stay tuned!

@EcoresolveInc 2 года назад

Great video and important topic Tek!

@geodev 2 года назад

Glad you think so-:) Thank you Mikey!

@roitai-dev 7 месяцев назад

Thank you so much professor.

@geodev 7 месяцев назад

You are very welcome

@rahulds001 2 года назад

Thank you so much...❤️ expecting more deep learning tutorial for geosciences application which is very rare in RU-vid. No body teaches that

@rahulds001 2 года назад

Can you tell me how can we merge this patches to create a single output if we have a geotiff image and how can we convert h5 into geotiff

@geodev 2 года назад

More tutorials on the way. Stay tuned!

@geodev 2 года назад

From this tiles, it is not possible because there are the tiles from random location. But for such work, I recently wrote the library named as "geotile" which will help you to create tiles as well as merge tiles. Library github: github.com/iamtekson/geotile

@rahulds001 2 года назад

@@geodev Thank you so much. I would like to request one video on how to create our own dataset(patches and masks) from satellite data.

@geodev 2 года назад

@@rahulds001 definitely, i will creat. Stay tuned😃

@yogeshkumardurugwar815 Год назад

This video is very informative. Such types of tutorial are very rare. I am also working on landslide it will help me for my research. My best wishes for you and i am waiting for such videos on landslide inventory and prediction

@geodev Год назад

Glad it was helpful! More videos are on way, stay tuned!

@yogeshkumardurugwar815 Год назад

@@geodev sir please share with me your contact email id and ph. no. i have more work related to landslide, we can do with collaboration.

@user-dv9cp1we9g 5 месяцев назад

the dataset provided, is it from a specific study area, im very new to ml and related topics but find it interesting. i wanna try this with a dataset used in a research paper for a specific geographic area, it state that it used Digital terrain model along with Enhanced Natural Terrain Landslide Inventory (ENTLI). it would also be beneficial for me to create something for a specific area. Also some papers mentioned other factors like rainfall and such, can this project use factors like that as a parameter. This topic was more complex than i anticipated so im asking alot of questions

@shubhammaurya492 Год назад

Thank you Sir much awaited topic

@geodev Год назад

Always welcome

@shubhammaurya3822 2 года назад

Great video

@geodev 2 года назад

Glad you enjoyed it

@user-pi5ym1ki6w Год назад

Great video. Kindly ask that for the Sentinel 2 images which platform was used to download it, maybe different imges download platform has diffenent outcomes. Thanks!

@geodev Год назад

I am also exactly not sure, which platform was used to download the original imagery. Please have a look to the published paper or landslide4sense website.

@davidpark2584 Год назад

Your lecture is very useful. Please let me know where I can download your dataset. I can't download it at this moment.

@niflag 4 месяца назад

Why would you set NoData to 0.000001 instead of 0 or 256?

@LokeshSharma-jm4dp Год назад

Can you please make such video for air pollution prediction😊

@neefiyasingh3232 Год назад

I really like your videos. I want to know the dataset you have take above in landslide detection it's corrupted file. I tried to download train data and it shows file is corrupted. What should i do?

@geodev Год назад

Did you download the data from here: www.iarai.ac.at/landslide4sense/challenge/? I have worked on this data and it is not corrupted.

@sanjithhegde6008 20 дней назад

Thank you for the video sir 🙏 Could you please help to know about How to import utils? Im getting error in importing

@geodev 19 дней назад

Hi, you need to write utils.py file as well. Please check the github repo and download the full code.

@pavanchaganti1776 Год назад

How to get mask data for validation dataset... its not provided by land4sense too!! Can u help with doing that

@geodev Год назад

For the validation set, you can test and generate the result. Sorry I haven't tested the model for validation set.

@ibiswas8548 9 месяцев назад

Very knowledgeable video I want to know What deep learning model is it ??? Is it CNN??

@geodev 9 месяцев назад

Yes it is CNN. To be more precise, It is Unet model

@priyanthabandara1437 Год назад

Can I do similar analysis with LiDAR data? If it is possible please do a video please

@geodev Год назад

I think it will be possible with RGB imagery along with LiDAR point cloud. At the end, we need to create the DEM/DSM for landslide detection.

@BinhNguyen-cp9dv 2 года назад

How can I convert from TIFF to H5 or H5 to tiff, or any gis software can do it ?

@geodev 2 года назад

H5 format doesn't come with coordinate system. But anyway if you want to export as a image, write it using rasterio or gdal.

@OmenJap 2 года назад

Did you only use landslide location to run the model? So, We don't need to include non-landslide points in the inventory data.

@geodev 2 года назад

Sorry, I used the data from landslide4sense challenge, which is not geolocational data.

@OmenJap 2 года назад

@@geodev okay, I understand that random forest method needs for dependent data such as yes/no or landslide/non landslide. But you run the model only with landslide data, is that right?

@rockyard100 Год назад

@@OmenJap Not really. In segmentation tasks you have masks/patches of the corresponding satellite image which acts as a label (for model training) and these masks/patches includes pixels from both the landslide and non-landslide class. So, such models can identify both landslides and non-landslides pixles and segment only the class of interest.