Efficiency of local minima and GLM techniques in sinkhole extraction from a LiDAR-based terrain model

Enyedi, Péter; Pap, Melinda; Kovács, Zoltán; Takács-Szilágyi, László; Szabó, Szilárd

doi:10.1080/17538947.2018.1501107

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…Furthermore, traditional statistical analysis or machine learning can provide valuable data for all types of geographical analyses (e.g. Allen, C. et al 2016;Szabó, Z. et al 2017;Balázs, B. et al 2018;Enyedi, P. et al 2018). Our study focused on image classification, but the procedure also works with tabular data.…”

Section: The Linear Type Of Discriminant Analysismentioning

confidence: 99%

Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery – a comparison

et al. 2018

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Identification of roofing material is an important issue in the urban environment due to hazardous and risky materials. We conducted an analysis with Discriminant Function Analysis (DFA) and Random Forest (RF) on WorldView-2 imagery. We applied a three-and a six-class approach (red tile, brown tile and asbestos; then dividing the data into shadowed and sunny roof parts). Furthermore, we applied pan-sharpening to the image. Our aim was to reveal the efficiency of the classifiers with a different number of classes and the efficiency of pan-sharpening. We found that all classifiers were efficient in roofing material identification with the classes involved, and the overall accuracy was above 85 per cent. The best results were gained by RF, both with three and with six classes; however, quadratic DFA was also successful in the classification of three classes. Usually, linear DFA performed the worst, but only relatively so, given that the result was 85 per cent. Asbestos was identified successfully with all classifiers. The results can be used by local authorities for roof mapping to build registers of buildings at risk.

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Section: The Linear Type Of Discriminant Analysismentioning

confidence: 99%

Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery – a comparison

et al. 2018

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“…Herein, we trained the models on one study area and tested them with repeated cross-validation on two other spatially independent areas, which can ensure reliable outcome measures supporting better generalization of these classifications. Whereas this approach was recently used in sinkhole feature extraction [47] and in gully feature extraction [48], the application of the SVM and RF methods were not reported. Nevertheless, these studies provide the impetus for testing the performance of these ML methods, including the Linear Discriminant Analysis (LDA), another promising ML method.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

Phinzi

Abriha

Bertalan

et al. 2020

IJGI

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Gullies reduce both the quality and quantity of productive land, posing a serious threat to sustainable agriculture, hence, food security. Machine Learning (ML) algorithms are essential tools in the identification of gullies and can assist in strategic decision-making relevant to soil conservation. Nevertheless, accurate identification of gullies is a function of the selected ML algorithms, the image and number of classes used, i.e., binary (two classes) and multiclass. We applied Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Random Forest (RF) on a Systeme Pour l’Observation de la Terre (SPOT-7) image to extract gullies and investigated whether the multiclass (m) approach can offer better classification accuracy than the binary (b) approach. Using repeated k-fold cross-validation, we generated 36 models. Our findings revealed that, of these models, both RFb (98.70%) and SVMm (98.01%) outperformed the LDA in terms of overall accuracy (OA). However, the LDAb (99.51%) recorded the highest producer’s accuracy (PA) but had low corresponding user’s accuracy (UA) with 18.5%. The binary approach was generally better than the multiclass approach; however, on class level, the multiclass approach outperformed the binary approach in gully identification. Despite low spectral resolution, the pan-sharpened SPOT-7 product successfully identified gullies. The proposed methodology is relatively simple, but practically sound, and can be used to monitor gullies within and beyond the study region.

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“…Aerial LiDAR is a promising technology for collecting large amounts of data even in areas which are hard to access due to dense vegetation or their topography [64]. Surveys result in continuous data from the target areas, and the outcomes are point clouds with several million data points with horizontal and vertical coordinates, the intensity of the returning beams, and, in the case of multiple echoes, the number of returns [65].…”

Section: Discussionmentioning

confidence: 99%

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Szabó

Tóth

Holb

et al. 2020

Sensors

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Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain.

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Efficiency of local minima and GLM techniques in sinkhole extraction from a LiDAR-based terrain model

Cited by 7 publications

References 44 publications

Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery – a comparison

Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery – a comparison

Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

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