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ردیف | عنوان | نوع |
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1 |
Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data
ارزیابی شرایط زهکشی سطحی در مقیاس خیابان و محله: یک روش دید کامپیوتری و جهت جریان اعمال شده به داده های لیدار-2022 Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and
mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-
scale topographical information. This paper addresses this issue by providing a novel method for evaluating
surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging)
measurements. The developed method derives topographical properties and runoff accumulation by applying a
semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology
technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the
SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the
proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales
and identify problematic low points that could be susceptible to water ponding. Municipalities and property
owners can use this information to take targeted corrective maintenance actions. keywords: تقسیم بندی معنایی | جهت جریان | لیدار موبایل | زهکشی سطحی | زیرساخت های زهکشی | Semantic segmentation | Flow direction | Mobile lidar | Surface drainage | Drainage infrastructure |
مقاله انگلیسی |
2 |
Research on BP network for retrieving extinction coefficient from Mie scattering signal of lidar
تحقیقات بر روی شبکه BP برای بازیابی ضریب خاموشی از سیگنال پراکندگی میای LIDAR-2020 Mie lidar is a powerful tool for detecting the optical properties of atmospheric aerosols. However, there
are two unknown parameters in the Mie lidar equation: the extinction coefficient and the backscattering
coefficient. In the common methods for solving the equation, it is necessary to make assumptions about
the relationship between the two unknown parameters. These assumptions will reduce the detection
precision of extinction coefficient. In view of this, the back propagation (BP) neural network is used to
retrieve extinction coefficient from the Mie scattering signal of lidar. Firstly, the structure and main
parameters of the BP network are designed according to the practical application. In order to improve
the convergence speed and prevent falling into local minima, the initial weights and thresholds of BP network
are optimized by genetic algorithm (GA). Then the GA-BP network is trained with Mie scattering
signal and the extinction coefficient retrieved by Raman method. Thus the mathematical relationship
between Mie scattering signal and the extinction coefficient is stored in the BP network. The trained
GA-BP network is then used to retrieve the extinction coefficient from Mie scattering signal in different
conditions and the applicability of the GA-BP network is researched. The research will promote the development
of Mie lidar retrieving algorithm. Keywords: Aerosol | Mie scattering | Lidar | Extinction coefficient | BP network | Genetic algorithm |
مقاله انگلیسی |