landslide identification using multiple machine learning approach

1. ThesisPresentation
on
Feature Extraction Using Deep Learning methods
by
PAWAN SINGH
2019GI02
GIS CELL
Under the supervision of
DR. RAMJI DWIVEDI
ASSISTANT PROFESSOR
GIS CELL

2. TABLE OF CONTENT
• Introduction
• Research gap
• Motivation
• Thesis Objective
• Study area
• Dataset used and other derived dataset
• Methodology
• Results
• Conclusion
• Future work
• Achievement and Publications
• References

3. INTRODUCTION
• Feature extraction is an vast area of interest, using feature extraction machines try to identify
the unit by recognizing its features or by training itself to understand the similarity between
features (Nixon & Aguado 2019)
FIGURE 1: Feature Extraction in Image (Towarddatascience.com)

4. • Using only color satellite imagery we can try to extract any feature but limited by the spatial
resolution.
• Not to get to every features in the satellite imagery we focused on one feature which is the
landslide.
• Landslide, one of the hazardous geological events, is the downslope movement of rock mass
and debris. It has no one particular reason for its occurrence. It can happen due to multiple
reasons like Heavy rainfall, cloud burst, earthquake, improper human settlement or
unorganized constructions (Haigh et al., 2012).
FIGURE 2: Spectral Bands of Landsat 8(blogs.fuberlin.de)

5. RESEARCH GAP
• In India landslide inventory is managed in Bhuvan and Bhukosh portal and latest data
available of Uttarakhand is from 2016. Even though Uttarakhand if one of the worst affected
state by landslides (Khanduri et al., 2018).
• Landsat 8 is very well explored by foreign authors but for Indian subcontinent potential of
Landsat 8 is yet to be explored.
• Uses of parameters by few authors to identify landslides are sometime ambiguous.
• Use of high spatial resolution data for the landslide detection (Martha et al. 2019).

6. Motivation
• To use Landsat 8 data to extract the information of landslide pixel
• To explore the potential for google earth engine
• Exploring behavior of our data over neural network
• Using pre-trained deep learning model

7. THESIS OBJECTIVES
This thesis has the following objectives:
a) To investigate the performance of the machine learning algorithms such as Support vector
machine, Random Forest, Decision tree, Naive Bayes, and minimum distance to classify our
study area into land cover followed by identifying landslides pixel using Google Earth Engine
(GEE).
b) To design and evaluate the Deep Neural Network performance in identifying the landslides
pixels.
c) To investigate the efficacy of various deep learning modules available in MATLAB.

8. STUDY AREA
FIGURE 3: Region of Rudraprayag along with landslide points

9. DATASET USED
Data Resolution Source
Landsat 8 30 m/pixel https://earthexplorer.usgs.gov/
JAXAALOS DSM 30 m https://earthexplorer.usgs.gov/
Landslide Inventory https://bhukosh.gsi.gov.in/
Table 1: Primary Dataset

10. Derived datasets
Figure 4 :Flowchart of Data preparation

11. Dataset preview
Figure 5: Polygon marking in earth engine for Data 1 and Data 2

12. Figure 6: Data 2
Dataset preview
B1 to B8 – reflectance value
DSM – Surface model in meters
Slope – In degrees
Label- 1 as landslide and 0 as no
landslide

13. Figure 7: Data 3
Dataset preview

14. PROCESSING ENVIRONMENT
1. QGIS: We used QGIS to visualize the landslide point and to acquire Data 3. Final results
were also visualized in it.
2. Google Earth Engine: it has petabyte of regularly updated satellite imagery and remote
sensing data, it even provide cloud computation platform. It can perform all basic GIS
operation in it.
3. Google colab : it is online python development environment. It also provide performance
accelerator like GPU and TPU.
4. MATLAB: it is an Mathematical processing environment . We used it to do process like
image cropping and to perform available pre-trained deep learning models in it.

15. METHODOLOGY
i) Methodology for research objective (a)
Figure 8: Flowchart for objective (a)

16. ii) Methodology for research objective (b)
Figure 9: Flowchart for objective (b)

17. Figure 10: Design of Neural Network using Tensor flow

18. iii) Methodology for research objective (c)
Figure 11: Flowchart for objective (c)

19. RESULTS
i) Results for Methodology (a)
Figure 12: (left to right) Classification of the study area Using Support vector machine,
minimum distance, Naïve Bayes

20. Figure 13:Classification of the study area Using Random forest (left), and smile cart (right).

21. Figure 14: Location near Ukhimath and Location near Banadhar Identified as Landslide while classification

22. ii) Results for Methodology (b)
Figure 15:Graph of accuracy and loss of test and train data at every epoch

23. TP FP TN FN Accuracy Precision Recall F1 score
1070 22 2086 33 98.28 97.9 97 96.8
Table 2: Table showing metrics of classification done by model

24. Figure 16: Classifying a location into two classes Landslide or not a landslide
(a)Picture Showing area in Landsat 8 (b) Picture Showing area in Google Image (c) Location
after Classification Red dot shows it is a landslide and blue Not a landslide

25. iii) Results for Methodology (c)
Figure 17: Progress graph of the Google Net Deep learning Module

26. Table 3 : Evaluation and Comparison of all Deep learning Model
Method TP TN FP FN Accuracy Precision Recall F1 score
GoogleNet 14 91 13 7 0.84 0.51852 0.66667 0.58333
Inception v3 11 99 6 9 0.88 0.64706 0.55 0.59459
NasNet Mobile 5 103 1 16 0.864 0.83333 0.2381 0.37037
ResNet-18 10 101 3 11 0.888 0.76923 0.47619 0.58824
ResNet-50 13 102 2 8 0.92 0.86667 0.61905 0.72222
Shufflenet 14 100 4 7 0.912 0.77778 0.66667 0.71795

27. CONCLUSION
Using multiple method we saw we can find the location of the landslides pixels and it also be
visualized on map. Main learnings derived while going through the experiment are as follow:
a) Data is very important, more the data more better will be the performance of the model.
b) If Variety of landslides pixel given to model it can degrade the performance of the model, so
addition of parameters needs to be kept in mind while training .
c) More number of parameter doesn’t promise high performance but the correct parameter which
can easily differentiate will help to improve the performance.
d) High accuracy or high metrics parameters sometimes doesn’t reflect on map.

28. FUTURE
We can take inspiration from the already developed deep learning model and redesign our network
which can very identify the more number of feature.
Finding or developing new indices/parameters based on features focused can help to get better result.
A platform can be developed where in new satellite imagery we can automatically detect changes
related to landslides and can update the location of new or enhanced landslides.

29. Achievements & Publication
• Methodology (a) was performed using ISRO’s LISIII data in the IITB-ISRO-AICTE Mapathon
2021 and chosen in top 25 list of winners
• Paper title “PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE”
coauthors Vipin kumar maurya & Dr. Ramji Dwivedi is presented in IEEE IGARSS 2021 conference
and was based on methodology (a).

30. REFRENCES
• Khanduri, S., Sajwan, K. S., Rawat, A., Dhyani, C., & Kapoor, S. (2018). Disaster in
Rudraprayag District of Uttarakhand Himalaya: a special emphasis on geomorphic changes
and slope instability. J Geogr Nat Disast, 8(218), 2167-0587.
• Nixon, M., & Aguado, A. (2019). Feature extraction and image processing for computer
vision. Academic press.
• Haigh, M., & Rawat, J. S. (2012). Landslide disasters: Seeking causes–A case study from
Uttarakhand, India. In Management of Mountain Watersheds (pp. 218-253). Springer,
Dordrecht. https://doi.org/10.1007/978-94007-2476-1_18
• Martha, T. R., Roy, P., Khanna, K., Mrinalni, K., & Kumar, K. V. (2019). Landslides
mapped using satellite data in the Western Ghats of India after excess rainfall during
August 2018. Curr. Sci, 117(5), 804-812.

31. THANK YOU