Viewshed Analysis

Petrášová, Anna; Harmon, Brendan; Petras, Vaclav; Tabrizian, Payam; Mitášová, Helena

doi:10.1007/978-3-319-89303-7_9

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The analytical methods presented in the article focus on the visual impact of new buildings, including the extent and strength of their visual impact. Similar studies are usually based on viewshed analyses (Petrasova et al 2018;Caha 2017). The theory associated with the measuring of the viewshed has developed for several decades (Tandy 1967, Felleman 1986, Bishop 2003.…”

Section: The Methods and Other Studiesmentioning

confidence: 99%

Assessment of the Visual Impact of a Tall Building on a UNESCO Listed Historic Urban Landscape

Czyńska¹

2023

CAADRIA Proceedings

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This paper presents a methodology designed to study the visual impact of a tall building located in the vicinity of a protected area of exceptional cultural scenic value listed by the UNESCO World Heritage. Digital analyses of the city were carried out using the following methods: a) the Visual Impact Size (VIS), which allows to determine both the visual impact field and the domination of architectural objects in space; b) simulations of selected views from the human eyesight level. The proposed cityscape survey methodology is presented based on the example of the city of Toruń (Poland) in relation to the location of a planned high-rising building. The study used a 3D Digital Surface Model of the city (DSM). All simulations were carried out using proprietary software (C++).

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Section: The Methods and Other Studiesmentioning

confidence: 99%

Assessment of the Visual Impact of a Tall Building on a UNESCO Listed Historic Urban Landscape

Czyńska¹

2023

CAADRIA Proceedings

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“…Notably, the research on greenness should assume a scale related to geographical locations including 2D maps. Also, the integration of information from spatial data is efficiently facilitated through the open-source-gathered Geographical Information System (GIS) and, subsequently, technological breakthroughs, such as DeepLabV3, are applied in commercial applications, such as Esri's ArcGIS Pro 3.2 [25];…”

Section: Usages Of Greenness Measurementioning

confidence: 99%

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

Lee,

Park,

Lee

2024

Sensors

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Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research.

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“…The approach employed by r.survey combines both strategies. First, the visible pixels (cells) are selected according to the line-of-sight method using the pre-existing GRASS command, r.viewshed (Haverkort et al 2009, Petrasova et al 2018. Then, three principal outputs (visibility metrics) are given, based on trigonometric calculations, to provide quantitative information about how the terrain is perceived from the selected observation points (or viewpoints).…”

Section: Design Of Rsurveymentioning

confidence: 99%

“…In the second block (Figure 1(b)), the coordinates of each viewpoint are used by the r. viewshed module to compute the viewshed area (Haverkort et al 2009, Petrasova et al 2018, selecting only those pixels that have a direct line of sight from the viewpoint. This tool also returns the vertical angle value between the viewpoint and each visible DEM cell, and the curvature of the earth can be considered.…”

Section: Single Point Loopmentioning

confidence: 99%

r.survey: a tool for calculating visibility of variable-size objects based on orientation

Bornaetxea

Marchesini

2021

International Journal of Geographical Information Science

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Identification of terrain surface features can be done using approaches such as visual observation or remote sensing image processing. Accurate detection of survey targets at the ground level primarily depends on human visual acuity or sensor resolution, and then on acquisition geometry (i.e. the relative position and orientation between the surveyor and the terrain). Further, the delimitation of the observer's viewshed boundary or of the sensor's ground footprint is sometimes insufficient to ensure that all enclosed targets can be correctly detected. Size and orientation can hamper ground target visibility. In this paper we describe a new release of r.survey, an open-source spatial analysis tool for terrain survey assessment. This tool offers the necessary information to assess how terrain morphology is perceived by observers and/or sensors by means of three basic visibility metrics: 3D distance, view angle, and solid angle. It is also fully customizable, allowing single or multiple observation points, ground or aerial point of view, and size setting of the observed target, making it useful for many different purposes.

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Viewshed Analysis

Cited by 4 publications

References 3 publications

Assessment of the Visual Impact of a Tall Building on a UNESCO Listed Historic Urban Landscape

Assessment of the Visual Impact of a Tall Building on a UNESCO Listed Historic Urban Landscape

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

r.survey: a tool for calculating visibility of variable-size objects based on orientation

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