Synergy of sampling techniques and ensemble classifiers for classification of urban environments using full-waveform LiDAR data

Azadbakht, Mohsen; Fraser, Clive S.; Khoshelham, Kourosh

doi:10.1016/j.jag.2018.06.009

Cited by 27 publications

(27 citation statements)

References 36 publications

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“…By using the bootstrap method, each tree trains on a random subset with replacement of the entire training dataset [50], [51]. RFR is known as an effective ML method not only for its good prediction accuracy, but also for its great ability to deal with nonlinear and complex real world problems [52], [53].The key initiative of RFR is the combination of a collection of decision trees and selection of a subset of explanatory variables at individual trees. Subsequently, each of the built decision trees provides an individual value, and then the algorithm considers the average value as the final prediction in regression tasks.…”

Section: Phase 3: Lst Downscalingmentioning

confidence: 99%

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Ebrahimy

Aghighi

Azadbakht

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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MODIS land surface temperature (LST) product (MOD11A1) has been widely used in analysing spatiotemporal trends of LST. However, its applicability is limited, partially due to its coarse spatial resolution (i.e., 1 km). In this study, an Adaptive random forest regression (ARFR) method was developed for LST downscaling at national scale. This study also provided a framework to shift from downscaling single-time image sets to extensive time-series of MOD11A1 LST images in an operational approach (i.e., a 19-years spatiotemporal LST trend analysis over Iran) using the Google Earth Engine (GEE) cloud computing platform. The performance of ARFR was assessed by comparing the results of the downscaled LSTs with the Landsat-8 LST data on different dates of six consecutive years (2014-2019) over ten different sub-areas in Iran. The results demonstrated the effectiveness of the proposed method with an average root mean square error and mean absolute error of 2.22°C and 1.59°C, respectively. The results of spatiotemporal LST trend analysis showed that 25.08%, 10.05%, 56.68%, 1.04%, and 32.84% of Iran experienced significant positive trends during a full year, spring, summer, fall, and winter, respectively. Significant negative trends were also observed over the 3.09%, 23.84%, 7.54%, 17.38%, and 18.77% of Iran during a full year, spring, summer, fall, and winter, respectively. In summary, the outcomes of this study not only exhibit the spatiotemporal trends of LST across Iran, but also reveal the substantial benefits of the ARFR method in downscaling LST using GEE. Index Terms-Adaptive random forest, downscaling, Google Earth Engine (GEE), land surface temperature (LST), MODIS, trend analysis. I. INTRODUCTIONR EMOTELY sensed land surface temperature (LST) data is a unique source of information in climate change studies. Climate change has significant impacts on environmental

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Section: Phase 3: Lst Downscalingmentioning

confidence: 99%

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Ebrahimy

Aghighi

Azadbakht

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…Over the years, many researchers have studied methods for constructing good ensembles of classifiers [16,22,30,32,42,46], showing that indeed ensemble classifiers are often much more accurate than the individual classifiers within the ensemble [30]. Classifiers combination is widely applied to many different fields, such as urban environment classification [3,53] and medical decision support [2,49]. In many cases, the performance of an ensemble method cannot be easily formalized theoretically, but it can be easily evaluated on an experimental basis in specific working conditions (that is, a specific set of classifiers, training data, etc.…”

Section: Introductionmentioning

confidence: 99%

Voting with random classifiers (VORACE): theoretical and experimental analysis

Cornelio

Donini

Loreggia

et al. 2021

Auton Agent Multi-Agent Syst

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In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

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“…For example, a rule-based algorithm was used for classification tasks [2,36]. Other methods are based on classic machine learning that uses handcrafted features, such as nonlinear classification and support vector machine (SVM) classifiers [24], which have been widely used with point cloud classification tasks using handcrafted features from full-waveform LiDAR data [6,7,14,23,54]. Furthermore, for land-use classification tasks, Wang et al [37] demonstrated the importance of spatial distributional and handcrafted features of waveforms.…”

Section: Full-waveform Lidar Data Analysismentioning

confidence: 99%

Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction

Shinohara

Xiu

Matsuoka

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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During the last few years, in the field of computer vision, sophisticated deep learning methods have been developed to accomplish semantic segmentation tasks of 3D point cloud data. Additionally, many researchers have extended the applicability of these methods, such as PointNet or PointNet++, beyond semantic segmentation tasks of indoor scene data to large-scale outdoor scene data observed using airborne laser scanning systems equipped with light detection and ranging (LiDAR) technology. Most extant studies have only investigated geometric information (x, y, and z or longitude, latitude, and height) and have omitted rich radiometric information. Therefore, we aim to extend the applicability of deep learning-based model from the geometric data into radiometric data acquired with airborne full-waveform LiDAR without converting the waveform into 2D images or 3D voxels. We simultaneously train two models: a local module for local feature extraction and a global module for acquiring wide receptive fields for the waveform. Furthermore, our proposed model is based on waveform-aware convolutional techniques. We evaluate the effectiveness of the proposed method using benchmark large-scale outdoor scene data. By integrating the two outputs from the local module and the global module, our proposed model had achieved higher mean recall value 0.92 than previous methods and higher F1 scores for all six classes than the other 3D Deep Learning method. Therefore, our proposed network consisting of the local and global module successfully resolves the semantic segmentation task of full-waveform LiDAR data without requiring expert knowledge. CCS CONCEPTS • Computing methodologies → Scene understanding.

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Synergy of sampling techniques and ensemble classifiers for classification of urban environments using full-waveform LiDAR data

Cited by 27 publications

References 36 publications

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran

Voting with random classifiers (VORACE): theoretical and experimental analysis

Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction

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