scholarly journals Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

2021 ◽  
Author(s):  
Junjie Chen ◽  
Donghai Liu
Keyword(s):  
Water Quality ◽  
Water Supply ◽  
Large Scale ◽  
Relevant Information ◽  
Water Channels ◽  
Aerial Images ◽  
Aerial Image ◽  
Long Distance ◽  
Foreign Objects ◽  
Water Supply Safety

Abstract Foreign objects (e.g., livestock, rafting, and vehicles) intruded into inter-basin channels pose threats to water quality and water supply safety. Timely detection of the foreign objects and acquiring relevant information (e.g., quantities, geometry, and types) is a premise to enforce proactive measures to control potential loss. Large-scale water channels usually span a long distance and hence are difficult to be efficiently covered by manual inspection. Applying unmanned aerial vehicles for inspection can provide time-sensitive aerial images, from which intrusion incidents can be visually pinpointed. To automate the processing of such aerial images, this paper aims to propose a method based on computer vision to detect, extract, and classify foreign objects in water channels. The proposed approach includes four steps, i.e., aerial image preprocessing, abnormal region detection, instance extraction, and foreign object classification. Experiments demonstrate the efficacy of the approach, which can recognize three typical foreign objects (i.e., livestock, rafting, and vehicle) with a robust performance. The proposed approach can raise early awareness of intrusion incidents in water channels for water quality assurance.

scholarly journals VEHICLE DETECTION OF AERIAL IMAGE USING TV-L1 TEXTURE DECOMPOSITION

2016 ◽  
Vol III-3 ◽  
pp. 481-488
Author(s):  
Y. Wang ◽  
G. Wang ◽  
Y. Li ◽  
Y. Huang
Keyword(s):  
Large Scale ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Aerial Image ◽  
Variational Model ◽  
Total Variation Regularization ◽  
The Public ◽  
The Road ◽  
Pavement Surface ◽  
Saliency Analysis

Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the context of road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturally speaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certain distance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and an oscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adopted to obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regions of pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the central objects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with a diverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate that the proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 seconds for a 4656 x 3496 aerial image. It is promising for large-scale transportation management and planning.

scholarly journals Water supply project feasibilities in fringe areas of Kolkata, India

2011 ◽  
Vol 4 (1) ◽  
pp. 9-23
Author(s):  
K. Dutta Roy ◽  
B. Thakur ◽  
T. S. Konar ◽  
S. N. Chakrabarty
Keyword(s):  
Water Quality ◽  
Willingness To Pay ◽  
Water Supply ◽  
Urban Areas ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Relevant Information ◽  
Benefit Analysis ◽  
Standard Cost ◽  
The Impact

Abstract. Water supply management to the peri-urban areas of the developing world is a complex task due to migration, infrastructure and paucity of fund. A cost-benefit methodology particularly suitable for the peri-urban areas has been developed for the city of Kolkata, India. The costs are estimated based on a neural network estimate. The water quality of the area is estimated from samples and a water quality index has been prepared. A questionnaire survey in the area has been conducted for relevant information like income, awareness and willingness to pay for safe drinking water. A factor analysis has been conducted for distinguishing the important factors of the survey and subsequent multiple regressions have been conducted for finding the relationships for the willingness to pay. A system dynamics model has been conducted to estimate the trend of increase of willingness to pay with the urbanizations in the peri-urban areas. A cost benefit analysis with the impact of time value of money has been executed. The risk and uncertainty of the project is investigated by Monte Carlos simulation and tornado diagrams. It has been found that the projects that are normally rejected in standard cost benefit analysis would be accepted if the impacts of urbanizations in the peri-urban areas are considered.

scholarly journals Feature Point Matching Method for Aerial Image Based on Recursive Diffusion Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 407
Author(s):  
Jiayan Shen ◽  
Xiucheng Guo ◽  
Wenzong Zhou ◽  
Yiming Zhang ◽  
Juchen Li
Keyword(s):  
Large Scale ◽  
High Density ◽  
Feature Point ◽  
Aerial Images ◽  
Image Feature ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Matching Accuracy ◽  
Point Matching ◽  
Feature Point Matching

Aerial images are large-scale and susceptible to light. Traditional image feature point matching algorithms cannot achieve satisfactory matching accuracy for aerial images. This paper proposes a recursive diffusion algorithm, which is scale-invariant and can be used to extract symmetrical areas of different images. This narrows the matching range of feature points by extracting high-density areas of the image and improving the matching accuracy through correlation analysis of high-density areas. Through experimental comparison, it can be found that the recursive diffusion algorithm has more advantages compared to the correlation coefficient method and the mean shift algorithm when matching accuracy of aerial images, especially when the light of aerial images changes greatly.

scholarly journals FUSION OF MULTI-VIEW AND MULTI-SCALE AERIAL IMAGERY FOR REAL-TIME SITUATION AWARENESS APPLICATIONS

2015 ◽  
Vol XL-1/W4 ◽  
pp. 201-206 ◽  
Author(s):  
X. Zhuo ◽  
F. Kurz ◽  
P. Reinartz
Keyword(s):  
Real Time ◽  
Reference Frame ◽  
Situation Awareness ◽  
Large Scale ◽  
Bundle Adjustment ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Main Concern ◽  
Multi Scale

Manned aircraft has long been used for capturing large-scale aerial images, yet the high costs and weather dependence restrict its availability in emergency situations. In recent years, MAV (Micro Aerial Vehicle) emerged as a novel modality for aerial image acquisition. Its maneuverability and flexibility enable a rapid awareness of the scene of interest. Since these two platforms deliver scene information from different scale and different view, it makes sense to fuse these two types of complimentary imagery to achieve a quick, accurate and detailed description of the scene, which is the main concern of real-time situation awareness. This paper proposes a method to fuse multi-view and multi-scale aerial imagery by establishing a common reference frame. In particular, common features among MAV images and geo-referenced airplane images can be extracted by a scale invariant feature detector like SIFT. From the tie point of geo-referenced images we derive the coordinate of corresponding ground points, which are then utilized as ground control points in global bundle adjustment of MAV images. In this way, the MAV block is aligned to the reference frame. Experiment results show that this method can achieve fully automatic geo-referencing of MAV images even if GPS/IMU acquisition has dropouts, and the orientation accuracy is improved compared to the GPS/IMU based georeferencing. The concept for a subsequent 3D classification method is also described in this paper.

scholarly journals Integrated Modelling to Support Analysis of COVID-19 Impacts on London's Water System and In-river Water Quality

2021 ◽  
Vol 3 ◽  
Author(s):  
Barnaby Dobson ◽  
Tijana Jovanovic ◽  
Yuting Chen ◽  
Athanasios Paschalis ◽  
Adrian Butler ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Demand ◽  
Large Scale ◽  
Water System ◽  
Domestic Wastewater ◽  
Integrated Modelling ◽  
Long Distance ◽  
Pollutant Concentrations ◽  
River Quality ◽  
At Home

Due to the COVID-19 pandemic, citizens of the United Kingdom were required to stay at home for many months in 2020. In the weeks before and months following lockdown, including when it was not being enforced, citizens were advised to stay at home where possible. As a result, in a megacity such as London, where long-distance commuting is common, spatial and temporal changes to patterns of water demand are inevitable. This, in turn, may change where people's waste is treated and ultimately impact the in-river quality of effluent receiving waters. To assess large scale impacts, such as COVID-19, at the city scale, an integrated modelling approach that captures everything between households and rivers is needed. A framework to achieve this is presented in this study and used to explore changes in water use and the associated impacts on wastewater treatment and in-river quality as a result of government and societal responses to COVID-19. Our modelling results revealed significant changes to household water consumption under a range of impact scenarios, however, they only showed significant impacts on pollutant concentrations in household wastewater in central London. Pollutant concentrations in rivers simulated by the model were most sensitive in the tributaries of the River Thames, highlighting the vulnerability of smaller rivers and the important role that they play in diluting pollution. Modelled ammonia and phosphates were found to be the pollutants that rivers were most sensitive to because their main source in urban rivers is domestic wastewater that was significantly altered during the imposed mobility restrictions. A model evaluation showed that we can accurately validate individual model components (i.e., water demand generator) and emphasised need for continuous water quality measurements. Ultimatly, the work provides a basis for further developments of water systems integration approaches to project changes under never-before seen scenarios.

Water for the City

Global Cities ◽  
2017 ◽  
Author(s):  
Robert Gottlieb ◽  
Simon Ng
Keyword(s):  
Water Quality ◽  
Los Angeles ◽  
Water Supply ◽  
Long Distance ◽  
The North ◽  
State Of Water ◽  
Supply Relationship ◽  
Quality Water ◽  
Non Local ◽  
The City

This chapter describes the history and current state of water supply development and the water quality issues that Los Angeles, Hong Kong, and China have needed to address. It identifies the efforts to undertake long distance imported water transfers and their environmental impacts; water quality problems from surface and groundwater sources, and water management issues, including a discussion of water privatization efforts and increased bottled water sales. It analyzes different river systems and watershed basins and various dependencies on non-local sources, such as Hong Kong’s dependence on water from Guangdong waters, Los Angeles on water from Northern California and the Colorado River, and China’s coastal regions and regions in the north on various transfers from where water is more plentiful to where it is scarce. It also looks at the water quality-water supply relationship and how polluted sources have led to a loss of supply.

scholarly journals Hierarchical Clustering-Aligning Framework Based Fast Large-Scale 3D Reconstruction Using Aerial Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 315
Author(s):  
Xiuchuan Xie ◽  
Tao Yang ◽  
DongDong Li ◽  
Zhi Li ◽  
Yanning Zhang
Keyword(s):  
3D Reconstruction ◽  
Large Scale ◽  
Similarity Transformation ◽  
Estimation Method ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Aerial Image ◽  
Image Clustering ◽  
Partial Structures ◽  
Loop Constraint

With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remotesensing, 3D reconstruction using aerial images has been a vibrant area of research. However,fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale meansthat the number and resolution of images are enormous, which brings significant computationalcost to the 3D reconstruction, especially in the process of Structure from Motion (SfM). In thispaper, for fast large-scale SfM, we propose a clustering-aligning framework that hierarchicallymerges partial structures to reconstruct the full scene. Through image clustering, an overlappingrelationship between image subsets is established. With the overlapping relationship, we proposea similarity transformation estimation method based on joint camera poses of common images.Finally, we introduce the closed-loop constraint and propose a similarity transformation-based hybridoptimization method to make the merged complete scene seamless. The advantage of the proposedmethod is a significant efficiency improvement without a marginal loss in accuracy. Experimentalresults on the Qinling dataset captured over Qinling mountain covering 57 square kilometersdemonstrate the efficiency and robustness of the proposed method.

scholarly journals Water supply project feasibilities in fringe areas of Kolkata, India

2010 ◽  
Vol 3 (2) ◽  
pp. 199-249
Author(s):  
K. Dutta Roy ◽  
B. Thakur ◽  
T. S. Konar ◽  
S. N. Chakrabarty
Keyword(s):  
Water Quality ◽  
Willingness To Pay ◽  
Water Supply ◽  
Urban Areas ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Relevant Information ◽  
Benefit Analysis ◽  
Standard Cost ◽  
The Impact

Abstract. Water supply management to the peri-urban areas of the developing world is a complex task due to migration, infrastructure, paucity of fund etc. A cost-benefit methodology particularly suitable for the peri-urban areas has been developed for the city of Kolkata, India. The costs are estimated based on a neural network estimate. The water quality of the area is estimated from samples and a water quality index has been prepared. A questionnaire survey in the area has been conducted for relevant information like income, awareness and willingness to pay for safe drinking water. A factor analysis has been conducted for distinguishing the important factors of the survey and subsequent multiple regressions have been conducted for finding the relationships for the willingness to pay. A system dynamics model has been conducted to estimate the trend of increase of willingness to pay with the urbanizations in the peri-urban areas. A cost benefit analysis with the impact of time value of money has been executed. The risk and uncertainty of the project is investigated by Monte Carlos simulation and tornado diagrams. It has been found that the projects that are normally rejected in standard cost benefit analysis would be accepted if the impacts of urbanizations in the peri-urban areas are considered.

scholarly journals MULTIPLE VEHICLES AND PEOPLE TRACKING IN AERIAL IMAGERY USING STACK OF MICRO SINGLE-OBJECT-TRACKING CNNS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 163-170
Author(s):  
R. Bahmanyar ◽  
S. M. Azimi ◽  
P. Reinartz
Keyword(s):  
Object Tracking ◽  
Large Scale ◽  
Image Sequences ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Single Object ◽  
Current Frame ◽  
Multiple Vehicles ◽  
Previous Frame

Abstract. Geo-referenced real-time vehicle and person tracking in aerial imagery has a variety of applications such as traffic and large-scale event monitoring, disaster management, and also for input into predictive traffic and crowd models. However, object tracking in aerial imagery is still an unsolved challenging problem due to the tiny size of the objects as well as different scales and the limited temporal resolution of geo-referenced datasets. In this work, we propose a new approach based on Convolutional Neural Networks (CNNs) to track multiple vehicles and people in aerial image sequences. As the large number of objects in aerial images can exponentially increase the processing demands in multiple object tracking scenarios, the proposed approach utilizes the stack of micro CNNs, where each micro CNN is responsible for a single-object tracking task. We call our approach Stack of Micro-Single- Object-Tracking CNNs (SMSOT-CNN). More precisely, using a two-stream CNN, we extract a set of features from two consecutive frames for each object, with the given location of the object in the previous frame. Then, we assign each MSOT-CNN the extracted features of each object to predict the object location in the current frame. We train and validate the proposed approach on the vehicle and person sets of the KIT AIS dataset of object tracking in aerial image sequences. Results indicate the accurate and time-efficient tracking of multiple vehicles and people by the proposed approach.

scholarly journals AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning

2019 ◽  
Author(s):  
Alan Bauer ◽  
Aaron George Bostrom ◽  
Joshua Ball ◽  
Christopher Applegate ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Precision Agriculture ◽  
Large Scale ◽  
Vegetation Index ◽  
Aerial Images ◽  
Aerial Image ◽  
Analysis Platform ◽  
Aerial Image Analysis

AbstractAerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are required, which not only produce high-quality measures of key crop traits, but also support agricultural practitioners to make reliable management decisions of their crops. Here, we report AirSurf-Lettuce, an automated and open-source aerial image analysis platform that combines modern computer vision, up-to-date machine learning, and modular software engineering to measure yield-related phenotypes of millions of lettuces across the field. Utilising ultra-large normalized difference vegetation index (NDVI) images acquired by fixed-wing light aircrafts together with a deep-learning classifier trained with over 100,000 labelled lettuce signals, the platform is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution in the field, based on which global positioning system (GPS) tagged harvest regions can be derived to enable growers and farmers’ precise harvest strategies and marketability estimates before the harvest.

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