scholarly journals QUANTIFYING UNCERTAINTY IN CLASSIFIED POINT CLOUD DATA FOR GEOSPATIAL APPLICATIONS

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 87-93
Author(s):  
S. Sen ◽  
N. Turel
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Geospatial Data ◽  
Training Data ◽  
Point Cloud Data ◽  
Positional Accuracy ◽  
Data Infrastructure ◽  
Cloud Data ◽  
Digital Twins ◽  
Quantifying Uncertainty

Abstract. Classified Point Cloud data are increasingly the form of geospatial data that are used in engineering applications, smart digital twins and geospatial data infrastructure around the globe. Characterized by high positional accuracy such dense 3D datasets are often rated very highly for accuracy and reliability. However such data pose important challenges in semantic segmentation, especially in the context of Machine Learning(ML) techniques and the training data employed to provide classification codes to every point in massive point cloud datasets. These challenges are particularly significant since ML based processing of data is almost unavoidable due to the massive nature of the data that. We review different sources of uncertainty introduced by ML based classification and segmentation and outline concepts of uncertainty that is inherent in such automatically processed data. We also provide a theoretical framework for quantification of such uncertainty and argue that the standards of accuracy of such data should account for errors and omissions during auto segmentation and classification in addition to positional accuracy measures. Interestingly, the ability to quantify accuracies of ML based automation for processing such data is limited by the volume and velocity of such data.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals 3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level

2021 ◽  
Vol 11 (19) ◽  
pp. 8996
Author(s):  
Yuwei Cao ◽  
Marco Scaioni
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Second Step ◽  
Point Cloud Data ◽  
Dynamic Graph ◽  
Cloud Data ◽  
Supervised Methods ◽  
Global And Local

In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmentation network to be applied to point clouds of buildings. However, training such networks requires large amounts of fine-labeled buildings’ point-cloud data, presenting a major challenge in practice because they are difficult to obtain. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. In order to reduce the number of required annotated labels, we proposed a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision, named 3DLEB-Net. In general, it consists of two steps. The first step (Autoencoder, AE) is composed of a Dynamic Graph Convolutional Neural Network (DGCNN) encoder and a folding-based decoder. It is designed to extract discriminative global and local features from input point clouds by faithfully reconstructing them without any label. The second step is the semantic segmentation network. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluated our approach based on the Architectural Cultural Heritage (ArCH) dataset. Compared to the fully supervised DL methods, we found that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labeled training data from fully supervised methods as input. Moreover, we conducted a series of ablation studies to show the effectiveness of the design choices of our model.

scholarly journals FULLY CONVOLUTIONAL NETWORKS FOR STREET FURNITURE IDENTIFICATION IN PANORAMA IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 13-20
Author(s):  
Y. Ao ◽  
J. Wang ◽  
M. Zhou ◽  
R. C. Lindenbergh ◽  
M. Y. Yang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Fine Tuning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Panoramic Images ◽  
Street Furniture

<p><strong>Abstract.</strong> Panoramic images are widely used in many scenes, especially in virtual reality and street view capture. However, they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images. This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks (FCN). FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction. In this study, we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data. The results show that in both pre-trained model and fine-tuning, transformed images have better prediction results than panoramic images.</p>

scholarly journals TAILORED FEATURES FOR SEMANTIC SEGMENTATION WITH A DGCNN USING FREE TRAINING SAMPLES OF A COLORED AIRBORNE POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 339-346
Author(s):  
E. Widyaningrum ◽  
M. K. Fajari ◽  
R. C. Lindenbergh ◽  
M. Hahn
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Blue Color ◽  
Point Cloud Data ◽  
Dynamic Graph ◽  
Cloud Data ◽  
Training Samples ◽  
Map Generation ◽  
Fast Development

Abstract. Automation of 3D LiDAR point cloud processing is expected to increase the production rate of many applications including automatic map generation. Fast development on high-end hardware has boosted the expansion of deep learning research for 3D classification and segmentation. However, deep learning requires large amount of high quality training samples. The generation of training samples for accurate classification results, especially for airborne point cloud data, is still problematic. Moreover, which customized features should be used best for segmenting airborne point cloud data is still unclear. This paper proposes semi-automatic point cloud labelling and examines the potential of combining different tailor-made features for pointwise semantic segmentation of an airborne point cloud. We implement a Dynamic Graph CNN (DGCNN) approach to classify airborne point cloud data into four land cover classes: bare-land, trees, buildings and roads. The DGCNN architecture is chosen as this network relates two approaches, PointNet and graph CNNs, to exploit the geometric relationships between points. For experiments, we train an airborne point cloud and co-aligned orthophoto of the Surabaya city area of Indonesia to DGCNN using three different tailor-made feature combinations: points with RGB (Red, Green, Blue) color, points with original LiDAR features (Intensity, Return number, Number of returns) so-called IRN, and points with two spectral colors and Intensity (Red, Green, Intensity) so-called RGI. The overall accuracy of the testing area indicates that using RGB information gives the best segmentation results of 81.05% while IRN and RGI gives accuracy values of 76.13%, and 79.81%, respectively.

Learning from Synthetic Point Cloud Data for Historical Buildings Semantic Segmentation

2020 ◽  
Vol 13 (4) ◽  
pp. 1-16
Author(s):  
Christian Morbidoni ◽  
Roberto Pierdicca ◽  
Marina Paolanti ◽  
Ramona Quattrini ◽  
Raissa Mammoli
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Cloud Data ◽  
Historical Buildings ◽  
Cloud Data

scholarly journals WEAKLY SUPERVISED PSEUDO-LABEL ASSISTED LEARNING FOR ALS POINT CLOUD SEMANTIC SEGMENTATION

2021 ◽  
Vol V-2-2021 ◽  
pp. 43-50
Author(s):  
P. Wang ◽  
W. Yao
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Training Data ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Prediction Probability ◽  
Weakly Supervised

Abstract. Competitive point cloud semantic segmentation results usually rely on a large amount of labeled data. However, data annotation is a time-consuming and labor-intensive task, particularly for three-dimensional point cloud data. Thus, obtaining accurate results with limited ground truth as training data is considerably important. As a simple and effective method, pseudo labels can use information from unlabeled data for training neural networks. In this study, we propose a pseudo-label-assisted point cloud segmentation method with very few sparsely sampled labels that are normally randomly selected for each class. An adaptive thresholding strategy was proposed to generate a pseudo-label based on the prediction probability. Pseudo-label learning is an iterative process, and pseudo labels were updated solely on ground-truth weak labels as the model converged to improve the training efficiency. Experiments using the ISPRS 3D sematic labeling benchmark dataset indicated that our proposed method achieved an equally competitive result compared to that using a full supervision scheme with only up to 2‰ of labeled points from the original training set, with an overall accuracy of 83.7% and an average F1 score of 70.2%.

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