Detection of Water-Bodies Using Semantic Segmentation

Talal, Mina; Panthakkan, Alavikunhu; Mukhtar, Husameldin; Mansoor, Wathiq; Mansoori, Saeed Al; Al-Ahmad, Hussain

doi:10.1109/cspis.2018.8642743

Cited by 24 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, new tools can increase the cost-effectiveness of DML, making it a suitable solution for a greater number of areas. For example, drones and detection of water bodies from satellite imagery using AI can be used to optimize the detection and treatment of water bodies, 19,20 and analysis of weather conditions and patterns may help to choose the best timing for interventions.…”

Section: Discussionmentioning

confidence: 99%

Digitally managed larviciding as a cost-effective intervention for urban malaria: Operational lessons from a pilot in São Tomé and Príncipe guided by the Zzapp system

Vigodny¹,

Aharon²,

Wharton-Smith

et al. 2022

Preprint

View full text Add to dashboard Cite

Background Once a mainstay of malaria elimination operations, larval source management - the treatment of mosquito breeding habitats - has been marginalized in Africa, due to insufficient effectiveness. However, the development of new technologies, and mosquitoes growing resistance to insecticides used in bed nets and house spraying raise renewed interest in this method. Methods A digitally managed larviciding operation in three of the seven districts of Sao Tome and Principe (STP) was launched by the Ministry of Health and ZzappMalaria LTD, guided by the Zzapp map-and-GPS mobile application and dashboard. During the operation, quality assurance procedures and field management methods were developed and implemented. Findings 12,788 water bodies were located and treated a total of 128,864 times. The reduction impact on mosquito population and on malaria cases was 74.90% and 52.65%, respectively. The overall cost per person protected (PPP) was US$0.78 and US$0.41 PPP in the urban areas. Various cost and effectiveness drivers were identified. Interpretation Digitally managed larviciding can yield highly cost-effective results, especially in urban areas. Digital tools facilitate standardization of operations, implementation of quality assurance procedures, and monitoring of fieldworkers performance. Digitally generated spatial data also have the potential to assist integrated vector management operations. A randomized controlled trial with a larger sample is needed to further substantiate findings. Funding The operation was funded by ZzappMalaria LTD and the STP Ministry of Health (MOH).

show abstract

Section: Discussionmentioning

confidence: 99%

Digitally managed larviciding as a cost-effective intervention for urban malaria: Operational lessons from a pilot in São Tomé and Príncipe guided by the Zzapp system

Vigodny¹,

Aharon²,

Wharton-Smith

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In particular, two state-ofthe-art segmentation networks were employed in this work, which are SegNet and a recent segmentation network known as DeepLabv3+ networks. These two networks were selected based on their superior performances demonstrated in previous studies [71][72][73][74][75][76]. In a recent study, Khan et al examined several semantic segmentation models including SegNet and DeepLabv3+ for smoke segmentation.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera

et al. 2021

View full text Add to dashboard Cite

The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities.

show abstract

“…Pixel accuracy is the ratio of correctly classified PV plants pixels to the total number of PV plants pixels in the original mask image [57], which can be mathematically represented as Equation (2).…”

Section: Metricsmentioning

confidence: 99%

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

2021

View full text Add to dashboard Cite

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.

show abstract

Detection of Water-Bodies Using Semantic Segmentation

Cited by 24 publications

References 12 publications

Digitally managed larviciding as a cost-effective intervention for urban malaria: Operational lessons from a pilot in São Tomé and Príncipe guided by the Zzapp system

Digitally managed larviciding as a cost-effective intervention for urban malaria: Operational lessons from a pilot in São Tomé and Príncipe guided by the Zzapp system

Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

Contact Info

Product

Resources

About