A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System

Villar-Cano, Miriam; Jiménez-Martínez, María Jesús; Marqués-Mateu, Ángel

doi:10.3390/ijgi8090378

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…According to the calculations, the RMSE was .05m in the rural areas, indicating that the average difference between automatically and manually calculated DTMs was 5cm. The used maps in the urban and rural planning had the scale of 1:2000 [40]; therefore, the error of 5cm is totally acceptable.…”

Section: The First Study Areamentioning

confidence: 99%

Rule-based Learning Techniques to Derive Automated Digital Terrain Model Using Airborne LiDAR Data

2022

IJG

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Constructing an accurate Digital Terrain Model is costly and time-consuming, leading to more challenges in urban environments due to the presence of different objects. This research performs the step by step analysis of LiDAR data using a rule-based algorithm to create an automatic DTM. This method needs no extra data and has a precision equal to that of a DTM, which is constructed manually. The DTM constructed in this research was compared to the DTM constructed manually to investigate the accuracy of the results. It was found that the mean difference between the elevations in both DTMs in the rural and urban areas was equal to zero and 0.10 m, respectively, while the mean difference between the slopes was 1.2 and 1.6%, respectively. However, in the areas which lacked buildings, the elevation and slope characteristics were equal, revealing identical DTMs, which was also confirmed by sig=.441 from t-test. Although sig=0.0 in the t-test shows a difference between the two DTMs in the urban and rural areas, it does not reveal the value of this difference. Thus, the RMSE method was used to examine this difference, leading to the values of ±0.20m, ±0.05m, and ±0.04m for the urban, rural, and areas without buildings, respectively. Considering that the precision required for urban and rural planning is 0.4m, it is totally acceptable to use the proposed algorithm instead of the manual method.

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Section: The First Study Areamentioning

confidence: 99%

Rule-based Learning Techniques to Derive Automated Digital Terrain Model Using Airborne LiDAR Data

2022

IJG

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“…J. Geo-Inf. 2020, 9,101 3 of 25 The tile in the first zoom level is indicated as 0, and the four tiles in the next zoom level are indicated as 00, 01, 02, and 03. [1].…”

Section: Raster Tilesmentioning

confidence: 99%

Performance Testing on Vector vs. Raster Map Tiles—Comparative Study on Load Metrics

Nétek

Masopust

Pavlicek

et al. 2020

IJGI

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Recent developments in web map applications have widely affected how background maps are rendered. Raster tiles are currently considered as a regular solution, while the use of vector tiles is becoming more widespread. This article describes an experiment to test both raster and vector tile methods. The concept behind raster tiles is based on pre-generating an original dataset including a customized symbology and style. All tiles are generated according to a standardized scheme. This method has a few disadvantages: if any change in the dataset is required, the entire tile-generating process must be redone. Vector tiles manipulate vector objects. Only vector geometry is stored on the server, while symbology, rendering, and defining zoom levels run on the client-side. This method simplifies changing symbology or topology. Based on eight pilot studies, performance testing on loading time, data size, and the number of requests were performed. The observed results provide a comprehensive comparison according to specific interactions. More data, but only one or two tiles, were downloaded for vector tiles in zoom and move interactions, while 40 tiles were downloaded for raster tiles for the same interactions. Generally, the WebP format downloaded about three times fewer data than Portable Network Graphics (PNG).

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A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System

Cited by 2 publications

References 17 publications

Rule-based Learning Techniques to Derive Automated Digital Terrain Model Using Airborne LiDAR Data

Rule-based Learning Techniques to Derive Automated Digital Terrain Model Using Airborne LiDAR Data

Performance Testing on Vector vs. Raster Map Tiles—Comparative Study on Load Metrics

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