Roof Shape Classification from LiDAR and Satellite Image Data Fusion Using Supervised Learning

Castagno, Jeremy D.; Atkins, Ella M.

doi:10.3390/s18113960

Cited by 55 publications

(41 citation statements)

References 28 publications

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“…It also predicts the magnitude and pressure distribution of roof in the building based on the free stream turbulent flow conditions. Automatic labeling of roof shape for the building [2] is done from the GIS data. The roof top detection is done by using ROC analysis [3] to estimate the cost of error from the aerial images of building.…”

Section: A Literature Reviewmentioning

confidence: 99%

Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

Devi*¹,

Basheer²,

Mathew³

2019

IJITEE

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Roof fall of the building is the major threat to the society as it results in severe damages to the life of the people. Recently, engineers are focusing on the prediction of roof fall of the building in order to avoid the damage to the environment and people. Early prediction of Roof fall is the social responsibility of the engineers towards existence of health and wealth of the nation. This paper attempts to identify the essential attributes of the Roof fall dataset that are taken from the UCI Machine learning repository for predicting the existence of roof fall. In this paper, the important features are extorted from the various ensembling methods like Gradient Boosting Regressor, Random Forest Regressor, AdaBoost Regressor and Extra Trees Regressor. The extracted feature importance of each of the ensembling methods is then fitted with multiple linear regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with multiple linear regression to analyze the performance. The Performance analysis is done with the performance parameters such as Mean Squared Log Error (MSLE), Mean Absolute error (MAE), R2 Score, Mean Squared error (MSE) and Explained Variance Score (EVS). The execution is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that before feature scaling, Extra Tree Regressor is found to be effective with the MSE of 0.06, MAE of 0.07, R2 Score of 87%, EVS of 0.89 and MSLE of 0.02 as compared to other ensembling methods. In the same way, after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 96%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.

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Section: A Literature Reviewmentioning

confidence: 99%

Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

Devi*¹,

Basheer²,

Mathew³

2019

IJITEE

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“…[1] [2][3] [4] [48] [49] [50] The application of GIS in recent study has shown higher potential for better societal benefit. A few of the applications are:…”

Section: Introductionmentioning

confidence: 99%

“…Road or Building Footprint: GIS enables to get the accurate terrain, road maps, waterways and the footprints and heights of buildings. [4][5] [18] [20] Apart from the above application areas, GIS has also been used to provide statistics for flood or cyclone affected area, potholes detection, tracking vehicle and geographical changes. [10][17] [47] In this paper, we are going to study the different aspects of the geographic information system such as land management, roof top analysis, traffic analysis, demographic analysis, watershed analysis, etc.…”

Section: Introductionmentioning

confidence: 99%

Spatial Information System Architecture for the Geographic Information System

Gotekar*,

Shaw,

Rout

2020

IJRTE

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Geographic Information System (GIS) combines software, hardware and data for the catching, handling, analyzing and displaying the different types of geographic information. This information can be in the form of digital maps, images taken by the satellite, aerial pictures, data captured through Global Positioning System (GPS), etc. Satellite imagery or Aircrafts specifically unmanned aircraft systems are used to capture images or geographical information. GIS has been greatly in use in various fields such as in Street Network, Land Information System, Facilities Management, Environment and Natural Resources Management, Planning and Engineering field, etc. GIS is the foundation of the various types of location enabled services. GIS system provides accurate maps or the pictures data captured with the help of various tools and techniques for the analysis of particular situation. In this paper, we are going to study the different aspects of the geographic information system such as land management, roof top analysis, traffic analysis, demographic analysis, watershed analysis, etc. Later we have compared LiDAR, satellite input processed with different feature extracted techniques with our proposed hybrid CNN approach. The result achieved is promising as compared with others

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“…The proposed framework performs better than state-of-the-art building extraction methods, given its higher values of recall, precision, and intersection over Union (IoU).2 of 33 remote sensing, and is extensively used in various applications, including urban planning, cartographic mapping, and land use analysis [1,2]. The significant progress in sensors and operating platforms has enabled us to acquire remote sensing images and 3D point clouds from cameras or Light Detection And Ranging (LiDAR) equipped in various platforms (e.g., satellite, aerial, and Unmanned Aerial Vehicle (UAV) platforms); thus, the methods based on images and point clouds are commonly used to extract buildings [3][4][5].Building extraction can be broadly divided into three categories according to data source: 2D image-based methods, 3D point cloud-based methods, and 2D and 3D information hybrid methods. 2D image-based building extraction consists of two stages, namely, building segmentation and regularization.…”

mentioning

confidence: 99%

“…2 of 33 remote sensing, and is extensively used in various applications, including urban planning, cartographic mapping, and land use analysis [1,2]. The significant progress in sensors and operating platforms has enabled us to acquire remote sensing images and 3D point clouds from cameras or Light Detection And Ranging (LiDAR) equipped in various platforms (e.g., satellite, aerial, and Unmanned Aerial Vehicle (UAV) platforms); thus, the methods based on images and point clouds are commonly used to extract buildings [3][4][5].…”

mentioning

confidence: 99%

Building Extraction from UAV Images Jointly Using 6D-SLIC and Multiscale Siamese Convolutional Networks

Zhou

Chen

et al. 2019

Remote Sensing

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Automatic building extraction using a single data type, either 2D remotely-sensed images or light detection and ranging 3D point clouds, remains insufficient to accurately delineate building outlines for automatic mapping, despite active research in this area and the significant progress which has been achieved in the past decade. This paper presents an effective approach to extracting buildings from Unmanned Aerial Vehicle (UAV) images through the incorporation of superpixel segmentation and semantic recognition. A framework for building extraction is constructed by jointly using an improved Simple Linear Iterative Clustering (SLIC) algorithm and Multiscale Siamese Convolutional Networks (MSCNs). The SLIC algorithm, improved by additionally imposing a digital surface model for superpixel segmentation, namely 6D-SLIC, is suited for building boundary detection under building and image backgrounds with similar radiometric signatures. The proposed MSCNs, including a feature learning network and a binary decision network, are used to automatically learn a multiscale hierarchical feature representation and detect building objects under various complex backgrounds. In addition, a gamma-transform green leaf index is proposed to truncate vegetation superpixels for further processing to improve the robustness and efficiency of building detection, the Douglas–Peucker algorithm and iterative optimization are used to eliminate jagged details generated from small structures as a result of superpixel segmentation. In the experiments, the UAV datasets, including many buildings in urban and rural areas with irregular shapes and different heights and that are obscured by trees, are collected to evaluate the proposed method. The experimental results based on the qualitative and quantitative measures confirm the effectiveness and high accuracy of the proposed framework relative to the digitized results. The proposed framework performs better than state-of-the-art building extraction methods, given its higher values of recall, precision, and intersection over Union (IoU).

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Roof Shape Classification from LiDAR and Satellite Image Data Fusion Using Supervised Learning

Cited by 55 publications

References 28 publications

Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

Spatial Information System Architecture for the Geographic Information System

Building Extraction from UAV Images Jointly Using 6D-SLIC and Multiscale Siamese Convolutional Networks

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