# Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution

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## Abstract

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## 1. Introduction

## 2. Case Study and Materials

- Area I, corresponding to the western coastal strip, where the morphology of the original model of “slope-over-wall” is preserved; in terms of morpho-structural evolution, it corresponds to the initial and unperturbed stress stage.
- Area II, corresponding to the intermediate stage of the evolutionary process; the original cliff is fragmented by gullies and ravines affected by erosive and flow processes triggered by shallow retrogressive landslides.
- Area III, representing the space-time expression of the definitive gravity-driven evolution of the coastal slope; it corresponds to the area progressively affected by active, reactivated and deep-seated landslides.

## 3. Methods

- Building of a 5 m resolution LiDAR-derived DTM.
- Computation of morphometric parameters for each individual coastal section.
- Selection of the morphometric parameters that are deemed significant for our classification problem, using Neighborhood Component Analysis (NCA).
- Training of a few selected models, their validation and choice of the one providing the best accuracy.
- Testing of the trained model on two different areas characterized by the same morpho-evolutionary process.

#### 3.1. Maps of Geomorphometric Parameters

#### 3.2. Feature Selection Using Neighborhood Component Analysis

_{i}is the average Leave-One-Out probability of correct classification of the observation i. There is only one regularization parameter λ for all weights, which can drive some of them to 0.

_{i}of the features have been also estimated using the technique called Stochastic Gradient Descent (SGD), an effective learning algorithm when the training set is large. Features with a weight below a certain threshold T:

#### 3.3. Supervised Machine Learning Classification

## 4. Results

#### 4.1. Morphometric Maps

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#### 4.2. Feature Selection

^{−4}. The range of values of λ analyzed was chosen in order to identify the minimum of the function whereas the step chosen is a threshold value, based on tests. The procedure was repeated for the five different folds.

#### 4.3. Supervised Machine Learning Classification

#### Optimization of the Weighted k-NN Classification

#### 4.4. Validation of the Model Performance on the Training Area

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Study area. (

**a**) Map of Italy, the circlet identifies the area; (

**b**) A zoomed-in view of the area, the box highlights the training area; (

**c**) The training area, with the three sections highlighted.

**Figure 6.**Accuracy score. (

**a**) 9 features; (

**b**) 8 features; (

**c**) Accuracy score differences (9–8 features). The algorithms are sorted in ascending order of accuracy.

**Figure 7.**Model accuracy indicators trained with the 9 features (Difc, Slins, Rot, Asp, Crosc, TRc, Extc, Verc, Unsph); color brightness is directly proportional to percentage values.

**Figure 8.**Model accuracy indicators trained with the 8 features (Difc, Slins, Rot, Asp, Crosc Extc, Verc, Unsph); color brightness is directly proportional to percentage values.

**Figure 10.**Classification overlaid on the test area DTM and the three coastal sections identified by expert judgment (white edge polygons).

**Figure 12.**Test area “Ripe Rosse”. Classified map superimposed on contour lines; (

**a**,

**b**) details from Google Earth images.

**Figure 13.**Marina di Ascea test area. (

**a**) Image from Google Earth; (

**b**) Classified map superimposed on contour lines.

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**MDPI and ACS Style**

Barbarella, M.; Di Benedetto, A.; Fiani, M.
Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution. *Remote Sens.* **2021**, *13*, 4782.
https://doi.org/10.3390/rs13234782

**AMA Style**

Barbarella M, Di Benedetto A, Fiani M.
Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution. *Remote Sensing*. 2021; 13(23):4782.
https://doi.org/10.3390/rs13234782

**Chicago/Turabian Style**

Barbarella, Maurizio, Alessandro Di Benedetto, and Margherita Fiani.
2021. "Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution" *Remote Sensing* 13, no. 23: 4782.
https://doi.org/10.3390/rs13234782