Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data

Lu, Zhenyu; Im, Jungho; Rhee, Jinyoung; Hodgson, Michael E.

doi:10.1016/j.landurbplan.2014.07.005

Cited by 103 publications

(63 citation statements)

References 50 publications

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“…Meanwhile, RF was used for both deterministic and probabilistic detection of CI. DT has been widely used for classification and regression tasks in the remote sensing field (Li et al, 2013;Lu et al, 2014;Torbick and Corbiere, 2015). See5, developed by RuleQuest Research, Inc. (Quinlan, 2015), was employed to perform the DT-based classification of clouds into CI and non-CI.…”

Section: Deterministic and Probabilistic Approaches For CI Detectionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm T b was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6-11.2 µm)-(11.2-12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20-40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

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Section: Deterministic and Probabilistic Approaches For CI Detectionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

Self Cite

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“…See 5.0 uses information gain to select which candidate attribute is used at each node. See 5.0 has been widely used for various remote sensing applications, including land cover/land use classification [50][51][52], climate region delineation [53], vegetation species mapping [54,55], ice mapping [56] and change detection [57,58]. Using a See 5.0 decision tree has some advantages.…”

Section: Machine Learning Algorithms For Lead Detectionmentioning

confidence: 99%

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Lee

Kim

et al. 2016

Remote Sensing

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Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.

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“…2016, 8,415 5 of 23 than a predetermined threshold are chosen as future seed points. The procedure is iteratively executed until all LiDAR points have been visited.…”

Section: Smoothness-oriented Rooftop Patch Optimizationmentioning

confidence: 99%

“…2016, 8,415 6 of 23 2d, a yellow point and blue point form a line to connect the first layer and the second layer. Moreover, in Figure 2e, a yellow point, blue point, and red point form a line to connect the first layer, the second layer, and the third layer.…”

Section: Generation Of Layer-connection Pointsmentioning

confidence: 99%

Three-Dimensional Reconstruction of Building Roofs from Airborne LiDAR Data Based on a Layer Connection and Smoothness Strategy

Wang

Cheng

et al. 2016

Remote Sensing

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Abstract:A new approach for three-dimensional (3-D) reconstruction of building roofs from airborne light detection and ranging (LiDAR) data is proposed, and it includes four steps. Building roof points are first extracted from LiDAR data by using the reversed iterative mathematic morphological (RIMM) algorithm and the density-based method. The corresponding relations between points and rooftop patches are then established through a smoothness strategy involving "seed point selection, patch growth, and patch smoothing." Layer-connection points are then generated to represent a layer in the horizontal direction and to connect different layers in the vertical direction. Finally, by connecting neighboring layer-connection points, building models are constructed with the second level of detailed data. The key contributions of this approach are the use of layer-connection points and the smoothness strategy for building model reconstruction. Experimental results are analyzed from several aspects, namely, the correctness and completeness, deviation analysis of the reconstructed building roofs, and the influence of elevation to 3-D roof reconstruction. In the two experimental regions used in this paper, the completeness and correctness of the reconstructed rooftop patches were about 90% and 95%, respectively. For the deviation accuracy, the average deviation distance and standard deviation in the best case were 0.05 m and 0.18 m, respectively; and those in the worst case were 0.12 m and 0.25 m. The experimental results demonstrated promising correctness, completeness, and deviation accuracy with satisfactory 3-D building roof models.

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Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data

Cited by 103 publications

References 50 publications

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Three-Dimensional Reconstruction of Building Roofs from Airborne LiDAR Data Based on a Layer Connection and Smoothness Strategy

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