A GIS-based Land Cover Classification Approach Suitable for Fine‐scale Urban Water Management

Hiscock, Oscar H.; Back, Yannick; Kleidorfer, Manfred; Urich, Christian

doi:10.1007/s11269-021-02790-x

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…Furthermore, the producer's accuracy for sidewalk and street-grass decreased by −7.5 and −6.0%, respectively; whereas the accuracy for grass and car-road increased by 7.0% and 5.6%, respectively. Therefore, in terms of accuracy changes, there was not a large impact of the reduced variable number on buildings, forests, and street-trees, corroborating the use of nDSM and NDVI in previous studies to accurately classify buildings and forests [55][56][57]. Vanhuysse et al [58] reported that when nDSM was used as input data, there were improvements in the quantitative and qualitative analysis of building and other classification results.…”

Section: Accuracy Verificationsupporting

confidence: 67%

Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area

Park

Song

et al. 2022

Drones

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The development of UAV sensors has made it possible to obtain a diverse array of spectral images in a single flight. In this study, high-resolution UAV-derived images of urban areas were employed to create land cover maps, including car-road, sidewalk, and street vegetation. A total of nine orthoimages were produced, and the variables effective in producing UAV-based land cover maps were identified. Based on analyses of the object-based images, 126 variables were derived by computing 14 statistical values for each image. The random forest (RF) classifier was used to evaluate the priority of the 126 variables. This was followed by optimizing the RF through variable reduction and by comparing the initial and optimized RF, the utility of the high-priority variable was evaluated. Computing variable importance, the most influential variables were evaluated in the order of normalized digital surface model (nDSM), normalized difference vegetation index (NDVI), land surface temperature (LST), soil adjusted vegetation index (SAVI), blue, green, red, rededge. Finally, no significant changes between initial and optimized RF in the classification were observed from a series of analyses even though the reduced variables number was applied for the classification.

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Section: Accuracy Verificationsupporting

confidence: 67%

Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area

Park

Song

et al. 2022

Drones

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“…Specifically, the image super-resolution algorithm based on learning is to first find the relationship between a low-resolution image and its corresponding high-resolution image. en, it corresponds to the low-frequency and high-frequency parts of the high-resolution image, performs intensive learning and training after partitioning, and finally applies this relationship to the superresolution reconstruction of other images, as shown in Figure 2 [22]. concept of probability transition model and the relationship between the models.…”

Section: The Basic Methods Of Super-resolution Image Reconstructionmentioning

confidence: 99%

Application of Digital Image Processing and GIS Technology in the Evaluation of Rationality of Urban and Rural Planning Spatial Layout

Hao

Xiaoyu

2022

Security and Communication Networks

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This work investigates digital image processing technology and presents a super-resolution image reconstruction approach to increase the logic of urban and rural planning spatial layout. Furthermore, this work integrates GIS technology with super-resolution image reconstruction techniques to create a digital image processing and GIS-based urban and rural spatial layout design model. In addition, this paper mathematically expresses the objects and scenes that need to be simulated as a collection of three-dimensional graphic objects stored in the computer. The platform is constructed with a three-tier system of B/S architecture, and the logical structure of the entire system is composed of the client, application server, database server, and other functional servers. Furthermore, this paper constructs the functional structure of urban and rural spatial layout planning model based on digital image processing and GIS technology and verifies the model in this paper through experimental research. The experimental statistical results verify the reasonableness of the model in this paper.

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“…Hiscock et al's research team used the high-precision GIS images obtained by overpass technology to create a multifunctional classification system of urban land cover. Through case study, it was found that the overall classification accuracy reached 89.3%, which is conducive to improving the working method of urban water management [6]. Deng et al applied GIS sensor technology to collect and analyze the data of community buildings in Changsha, China.…”

Section: Related Workmentioning

confidence: 99%

Application of GIS Sensor Technology in Digital Management of Urban Gardens under the Background of Big Data

Chang

Tan

2022

Journal of Sensors

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Since the reform and opening up, China’s urbanization level has been continuously improved, and the national demand for urban greening is also increasing. However, at present, there are many problems in domestic urban gardens, such as low management quality and high management cost, which have a certain negative impact on urban development and residents’ life. In this study, a digital management system of urban garden plant growth state based on sensor client/server structure, GIS (geographic information system) sensor technology and big data technology are designed, and its practicability is tested. The test results show that 52.90% and 40.70% of the people have positive comments on the satisfaction of the system client and the sensor comprehensive application value of the system based on WebGIS sensor technology, respectively. The former is 12.2 percentage points higher than the latter, and the server response speed and CPU (central processing unit) resource consumption of the former are also better. In addition, the robustness of the former is not significantly different from that of the latter. The data show that the digital sensor management system for the growth state of urban garden plants designed in this paper has complete and normal functions and good user experience.

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A GIS-based Land Cover Classification Approach Suitable for Fine‐scale Urban Water Management

Cited by 11 publications

References 19 publications

Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area

Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area

Application of Digital Image Processing and GIS Technology in the Evaluation of Rationality of Urban and Rural Planning Spatial Layout

Application of GIS Sensor Technology in Digital Management of Urban Gardens under the Background of Big Data

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