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THE POTENTIAL OF BUILDING DETECTION FROM SAR AND LIDAR USING DEEP LEARNING
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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and surveying applications Unlike other conventional sensors, airborne SAR mapping approach offers practicality and significant cost savings for the nation minimizing the need for ground control points on ...
the ground in addition to providing high-resolution, day-and-night, cloud coverage and weather independent images, which in turn provides faster turnaround times for creation of large area geospatial data ...
The last method is based on the analysis of the density of the raw DSM LIDAR data. ...
doi:10.5194/isprs-archives-xlii-4-w16-489-2019
fatcat:xhlrmiru5reo5fbqkhafxucl6a
Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection
[article]
2018
arXiv
pre-print
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Satellite and LIDAR data fusion is shown to provide greater classification accuracy than through use of either data type individually. ...
Satellite imagery and LIDAR data from Witten, Germany are fed to convolutional neural networks (CNN) to extract salient feature vectors. ...
Note that the full paper will contain additional background on CNNs in the context of RGB and LIDAR feature detection and classification problems.
III. ...
arXiv:1802.06274v1
fatcat:4jlkmopllrdphbe25nkaf3x4hi
Multimodal Learning Models based on Data Fusion Analysis for Fully Autonomous Vehicle Navigation and Operation
[article]
2020
Figshare
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The previous research based on the Multimodal Learning Models and Data Fusion techniques. ...
A fast and flexible Mask R-CNN based detection system can satisfy the need for real-time automatic detection. B. ...
To generate a more efficient network and can be deployed on mobile terminals, lightweight network design is mainly to design very simple but high-performance networks, such as the MobileNet neural network ...
doi:10.6084/m9.figshare.12001155
fatcat:u6iog636nbfbzaphiheikv2fqa
Deep Fusion of Remote Sensing Data for Accurate Classification
2017
IEEE Geoscience and Remote Sensing Letters
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The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). ...
Then, a fully connected DNN is designed to fuse the heterogeneous features obtained by the previous CNNs. ...
The CNNs are designed to extract spectral-spatial-elevation features of MSI/HSI and LiDAR data, and the DNN is designed to fuse the extracted features. ...
doi:10.1109/lgrs.2017.2704625
fatcat:bmplmahdureynasabirdfavz24
CNN-based 3D object classification using Hough space of LiDAR point clouds
2020
Human-Centric Computing and Information Sciences
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Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. ...
Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average. ...
Therefore, 3D object classification based on LiDAR point clouds remains a challenging problem. ...
doi:10.1186/s13673-020-00228-8
fatcat:kgfmyvtbpzgazbzg557i4jfuau
Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks
2018
Remote Sensing
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In this paper, a novel framework is proposed for the fusion of hyperspectral images and LiDAR-derived elevation data based on CNN and composite kernels. ...
Recently, convolutional neural networks (CNN) have been intensively investigated for the classification of remote sensing data by extracting invariant and abstract features suitable for classification. ...
Committee for distributing the Houston data set. ...
doi:10.3390/rs10101649
fatcat:7dnh6ggccnenrayg3f7tvhxhxu
Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network
2017
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This paper proposes a novel framework for the fusion of hyperspectral and LiDAR-derived rasterized data using extinction profiles (EPs) and deep learning. ...
Index Terms-Convolutional neural network, deep learning, extinction profile, graph-based feature fusion, hyperspectral, LiDAR, random forest, support vector machines. ...
of CNN-based classification on (d) hyperspectral data, (e) the stack of LiDAR and hyperspectral data, and (f) the proposed approach using GBFF. feature extraction on the stacked features may improve the ...
doi:10.1109/jstars.2016.2634863
fatcat:ft2d3fc6tnfcvkwvfcctthjl5u
MAPPING URBAN TREES WITHIN CADASTRAL PARCELS USING AN OBJECT-BASED CONVOLUTIONAL NEURAL NETWORK
2019
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this context, the objective of this research is to map urban tree coverage per cadastral parcel of Sandy Bay, Hobart from very high-resolution aerial orthophoto and LiDAR data using an Object Based ...
Instead of manual preparation of a large number of required training samples, automatically classified Object based image analysis (OBIA) output is used as an input samples to train CNN method. ...
We also thank Land Information System Tasmania for providing LiDAR point cloud and cadastral parcel datasets. ...
doi:10.5194/isprs-annals-iv-5-w2-111-2019
fatcat:pbynjlyv5nhapdw3vqj4rksb6q
Building Footprint Extraction in Dense Area from LiDAR Data using Mask R-CNN
2022
International Journal of Advanced Computer Science and Applications
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The proposed workflow includes data preprocessing and deep learning, for instance, segmentation was introduced and applied to a light detecting and ranging (LiDAR) point cloud in a dense rural area. ...
Thus, we introduced a modified workflow to train ensemble of the mask R-CNN using two backbones ResNet (34, 101). ...
These techniques include image-based, LiDAR-based, and data fusion-based [6] . For instance, Image-based technique use spectral properties. ...
doi:10.14569/ijacsa.2022.0130643
fatcat:7zxq5xcnxnb6vdp6iutofydabq
Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
2019
Applied Sciences
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First, the important pixels for CNN-based localization are determined, and the quality index of supervised data is defined based on differences in these pixels. ...
To address this issue, we propose a quality index for supervised data based on correlations between consecutive frames visualizing the important pixels for CNN-based localization. ...
CNN-Based Localization This section introduces the localization method based on the SPAD LiDAR and CNN. ...
doi:10.3390/app9101983
fatcat:3mylo7475bd3tdugm5scfep27y
A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet
2020
Sensors
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The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. ...
In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classification. ...
We combine the advantages of ResNet and CapsNet to design the ResCapNet to obtain more detailed information of LiDAR data for classification applications. ...
doi:10.3390/s20041151
pmid:32093132
pmcid:PMC7071473
fatcat:qfasy4ix6bge3csvqw3axxxwjy
KNOWLEDGE BASED 3D BUILDING MODEL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS FROM LIDAR AND AERIAL IMAGERIES
2016
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical ...
learning of features that are extracted from both LiDAR and aerial ortho-photos. ...
Another data driven method for building detection using LiDAR data was developed based on multi-scale data decomposition. ...
doi:10.5194/isprs-archives-xli-b3-833-2016
fatcat:nb6htqadpfautjicq2krgywjv4
KNOWLEDGE BASED 3D BUILDING MODEL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS FROM LIDAR AND AERIAL IMAGERIES
2016
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical ...
learning of features that are extracted from both LiDAR and aerial ortho-photos. ...
Another data driven method for building detection using LiDAR data was developed based on multi-scale data decomposition. ...
doi:10.5194/isprsarchives-xli-b3-833-2016
fatcat:bspeta6osze3rlyrpmmpy6l3w4
Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network
2021
Remote Sensing
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To tackle this task, a novel network based on an end-to-end deep learning framework is proposed to detect and classify buildings features. ...
The third was fixed on extracting deep features using the fusion of channel one and channel two, respectively. ...
[31] proposed an automatic building methodology based on a created mask R-CNN assembly for identifying rotational bounding boxes. ...
doi:10.3390/rs13234803
fatcat:txwva7f4qzhj7apc7ahjtpxl4e
Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest
2019
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The competition was based on urban land use and land cover classification, aiming to distinguish between very diverse and detailed classes of urban objects, materials, and vegetation. ...
and ranging (LiDAR). ...
Diaz for preprocessing and preparing the data, as well as F. F. Shahraki and S. Mukherjee for their contributions in the preparation of the ground truth. D. Cerra, M. Pato, and E. ...
doi:10.1109/jstars.2019.2911113
fatcat:r5qtkkthfvf7dpde3adq6xsrh4
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