scholarly journals APPLICATION FOR 3D SCENE UNDERSTANDING IN DETECTING DISCHARGE OF DOMESTICWASTE ALONG COMPLEX URBAN RIVERS

2016 ◽  
Vol XLI-B3 ◽  
pp. 663-667
Author(s):  
Y. Ninsalam ◽  
R. Qin ◽  
J. Rekittke
Keyword(s):  
Image Classification ◽  
Solid Waste ◽  
Low Cost ◽  
Scene Understanding ◽  
Point Clouds ◽  
Household Waste ◽  
Step Method ◽  
River Bank ◽  
3D Point Clouds ◽  
3D Scene

In our study we use 3D scene understanding to detect the discharge of domestic solid waste along an urban river. Solid waste found along the Ciliwung River in the neighbourhoods of Bukit Duri and Kampung Melayu may be attributed to households. This is in part due to inadequate municipal waste infrastructure and services which has caused those living along the river to rely upon it for waste disposal. However, there has been little research to understand the prevalence of household waste along the river. Our aim is to develop a methodology that deploys a low cost sensor to identify point source discharge of solid waste using image classification methods. To demonstrate this we describe the following five-step method: 1) a strip of GoPro images are captured photogrammetrically and processed for dense point cloud generation; 2) depth for each image is generated through a backward projection of the point clouds; 3) a supervised image classification method based on Random Forest classifier is applied on the view dependent red, green, blue and depth (RGB-D) data; 4) point discharge locations of solid waste can then be mapped by projecting the classified images to the 3D point clouds; 5) then the landscape elements are classified into five types, such as vegetation, human settlement, soil, water and solid waste. While this work is still ongoing, the initial results have demonstrated that it is possible to perform quantitative studies that may help reveal and estimate the amount of waste present along the river bank.

scholarly journals APPLICATION FOR 3D SCENE UNDERSTANDING IN DETECTING DISCHARGE OF DOMESTICWASTE ALONG COMPLEX URBAN RIVERS

2016 ◽  
Vol XLI-B3 ◽  
pp. 663-667
Author(s):  
Y. Ninsalam ◽  
R. Qin ◽  
J. Rekittke
Keyword(s):  
Image Classification ◽  
Solid Waste ◽  
Low Cost ◽  
Scene Understanding ◽  
Point Clouds ◽  
Household Waste ◽  
Step Method ◽  
River Bank ◽  
3D Point Clouds ◽  
3D Scene

In our study we use 3D scene understanding to detect the discharge of domestic solid waste along an urban river. Solid waste found along the Ciliwung River in the neighbourhoods of Bukit Duri and Kampung Melayu may be attributed to households. This is in part due to inadequate municipal waste infrastructure and services which has caused those living along the river to rely upon it for waste disposal. However, there has been little research to understand the prevalence of household waste along the river. Our aim is to develop a methodology that deploys a low cost sensor to identify point source discharge of solid waste using image classification methods. To demonstrate this we describe the following five-step method: 1) a strip of GoPro images are captured photogrammetrically and processed for dense point cloud generation; 2) depth for each image is generated through a backward projection of the point clouds; 3) a supervised image classification method based on Random Forest classifier is applied on the view dependent red, green, blue and depth (RGB-D) data; 4) point discharge locations of solid waste can then be mapped by projecting the classified images to the 3D point clouds; 5) then the landscape elements are classified into five types, such as vegetation, human settlement, soil, water and solid waste. While this work is still ongoing, the initial results have demonstrated that it is possible to perform quantitative studies that may help reveal and estimate the amount of waste present along the river bank.

scholarly journals Scene Wireframes Sketching for Drones

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1193
Author(s):  
Roi Santos ◽  
Xose Pardo ◽  
Xose Fdez-Vidal
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Outdoor Environment ◽  
Point Matching ◽  
3D Point Clouds ◽  
Complementary Approach ◽  
Straight Line ◽  
Scene Representation ◽  
Feature Point Matching ◽  
3D Scene

The increasing use of autonomous UAVs inside buildings and around human-made structures demands new accurate and comprehensive representation of their operation environments. Most of the 3D scene abstraction methods use invariant feature point matching, nevertheless some sparse 3D point clouds do not concisely represent the structure of the environment. Likewise, line clouds constructed by short and redundant segments with inaccurate directions limit the understanding of scenes as those that include environments with poor texture, or whose texture resembles a repetitive pattern. The presented approach is based on observation and representation models using the straight line segments, whose resemble the limits of an urban indoor or outdoor environment. The goal of the work is to get a full method based on the matching of lines that provides a complementary approach to state-of-the-art methods when facing 3D scene representation of poor texture environments for future autonomous UAV.

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals ACCURACY ASSESSMENT OF UNDERWATER PHOTOGRAMMETRIC THREE DIMENSIONAL MODELLING FOR CORAL REEFS

2016 ◽  
Vol XLI-B5 ◽  
pp. 821-828 ◽  
Author(s):  
T. Guo ◽  
A. Capra ◽  
M. Troyer ◽  
A. Gruen ◽  
A. J. Brooks ◽  
...  
Keyword(s):  
Coral Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Modelling ◽  
Relative Accuracy ◽  
3D Point Clouds ◽  
The Individual ◽  
Working Distance

Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

scholarly journals Low-Cost Strategies to Improve Municipal Solid Waste Management in Developing Countries: Experimental Evidence from Nepal

2022 ◽  
Author(s):  
Mani Nepal ◽  
Apsara Karki Nepal ◽  
Madan S. Khadayat ◽  
Rajesh K. Rai ◽  
Priya Shyamsundar ◽  
...  
Keyword(s):  
Developing Countries ◽  
Waste Management ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Waste Disposal ◽  
Low Cost ◽  
Household Waste ◽  
Municipal Solid Waste Management ◽  
Behavioural Interventions ◽  
Waste Segregation

AbstractMany cities in developing countries lack adequate drainage and waste management infrastructure. Consequently, city residents face economic and health impacts from flooding and waterlogging, which are aggravated by solid waste infiltrating and blocking drains. City governments have recourse to two strategies to address these problems: a) ‘hard’ infrastructure-related interventions through investment in the expansion of drainage and waste transportation networks; and/or, b) ‘soft’, low-cost behavioural interventions that encourage city residents to change waste disposal practices. This research examines whether behavioural interventions, such as information and awareness raising alongside provision of inexpensive street waste bins, can improve waste management in the city. We undertook a cluster randomized controlled trial study in Bharatpur, Nepal, where one group of households was treated with a soft, low-cost intervention (information and street waste bins) while the control group of households did not receive the intervention. We econometrically compared baseline indicators – perceived neighbourhood cleanliness, household waste disposal methods, and at-source waste segregation – from a pre-intervention survey with data from two rounds of post-intervention surveys. Results from analysing household panel data indicate that the intervention increased neighbourhood cleanliness and motivated the treated households to dispose their waste properly through waste collectors. The intervention, however, did not increase household waste segregation at source, which is possibly because of municipal waste collectors mixing segregated and non-segregated waste during collection. At-source segregation, a pre-requisite for efficiently managing municipal solid waste, may improve if municipalities arrange to collect and manage degradable and non-degradable waste separately.

scholarly journals Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes

2020 ◽  
Vol 12 (3) ◽  
pp. 543 ◽  
Author(s):  
Małgorzata Jarząbek-Rychard ◽  
Dong Lin ◽  
Hans-Gerd Maas
Keyword(s):  
Large Scale ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Point Clouds ◽  
Thermal Infrared ◽  
Classification Performance ◽  
Space Images ◽  
3D Point Clouds ◽  
3D Scene ◽  
And Control

Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high interest. In this study, we propose an approach for a supervised extraction of façade openings (windows and doors) from photogrammetric 3D point clouds attributed to RGB and thermal infrared (TIR) information. The novelty of the proposed approach is in the combination of thermal information with other available characteristics of data for a classification performed directly in 3D space. Images acquired in visible and thermal infrared spectra serve as input data for the camera pose estimation and the reconstruction of 3D scene geometry. To investigate the relevance of different information types to the classification performance, a Random Forest algorithm is applied to various sets of computed features. The best feature combination is then used as an input for a Conditional Random Field that enables us to incorporate contextual information and consider the interaction between the points. The evaluation executed on a per-point level shows that the fusion of all available information types together with context consideration allows us to extract objects with 90% completeness and 95% correctness. A respective assessment executed on a per-object level shows 97% completeness and 88% accuracy.

scholarly journals A Self-Assembly Portable Mobile Mapping System for Archeological Reconstruction Based on VSLAM-Photogrammetric Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3952 ◽  
Author(s):  
* ◽  
*
Keyword(s):  
Self Assembly ◽  
Average Distance ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Data Capture ◽  
Information Modeling ◽  
3D Point Clouds ◽  
Mean Square Errors

Three Dimensional (3D) models are widely used in clinical applications, geosciences, cultural heritage preservation, and engineering; this, together with new emerging needs such as building information modeling (BIM) develop new data capture techniques and devices with a low cost and reduced learning curve that allow for non-specialized users to employ it. This paper presents a simple, self-assembly device for 3D point clouds data capture with an estimated base price under €2500; furthermore, a workflow for the calculations is described that includes a Visual SLAM-photogrammetric threaded algorithm that has been implemented in C++. Another purpose of this work is to validate the proposed system in BIM working environments. To achieve it, in outdoor tests, several 3D point clouds were obtained and the coordinates of 40 points were obtained by means of this device, with data capture distances ranging between 5 to 20 m. Subsequently, those were compared to the coordinates of the same targets measured by a total station. The Euclidean average distance errors and root mean square errors (RMSEs) ranging between 12–46 mm and 8–33 mm respectively, depending on the data capture distance (5–20 m). Furthermore, the proposed system was compared with a commonly used photogrammetric methodology based on Agisoft Metashape software. The results obtained demonstrate that the proposed system satisfies (in each case) the tolerances of ‘level 1’ (51 mm) and ‘level 2’ (13 mm) for point cloud acquisition in urban design and historic documentation, according to the BIM Guide for 3D Imaging (U.S. General Services).

scholarly journals Post-Earthquake Recovery Phase Monitoring and Mapping Based on UAS Data

2020 ◽  
Vol 9 (7) ◽  
pp. 447
Author(s):  
Nikolaos Soulakellis ◽  
Christos Vasilakos ◽  
Stamatis Chatzistamatis ◽  
Dimitris Kavroudakis ◽  
Georgios Tataris ◽  
...  
Keyword(s):  
Low Cost ◽  
Point Clouds ◽  
Field Studies ◽  
Recovery Phase ◽  
Unmanned Aircraft ◽  
Surface Model ◽  
Building Stock ◽  
Earthquake Scenario ◽  
Video Footage ◽  
3D Point Clouds

Geoinformatics plays an essential role during the recovery phase of a post-earthquake situation. The aim of this paper is to present the methodology followed and the results obtained by the utilization of Unmanned Aircraft Systems (UASs) 4K-video footage processing and the automation of geo-information methods targeted at both monitoring the demolition process and mapping the demolished buildings. The field campaigns took place on the traditional settlement of Vrisa (Lesvos, Greece), which was heavily damaged by a strong earthquake (Mw=6.3) on June 12th, 2017. For this purpose, a flight campaign took place on 3rd February 2019 for collecting aerial 4K video footage using an Unmanned Aircraft. The Structure from Motion (SfM) method was applied on frames which derived from the 4K video footage, for producing accurate and very detailed 3D point clouds, as well as the Digital Surface Model (DSM) of the building stock of the Vrisa traditional settlement, twenty months after the earthquake. This dataset has been compared with the corresponding one which derived from 25th July 2017, a few days after the earthquake. Two algorithms have been developed for detecting the demolished buildings of the affected area, based on the DSMs and 3D point clouds, correspondingly. The results obtained have been tested through field studies and demonstrate that this methodology is feasible and effective in building demolition detection, giving very accurate results (97%) and, in parallel, is easily applicable and suit well for rapid demolition mapping during the recovery phase of a post-earthquake scenario. The significant advantage of the proposed methodology is its ability to provide reliable results in a very low cost and time-efficient way and to serve all stakeholders and national and local organizations that are responsible for post-earthquake management.

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