Reconnaissance de bâtiments et localisation de photographies au moyen d’un descripteur de texture

Suleiman, Wassim; Favier, Eric; Joliveau, Thierry

doi:10.3166/rig.22.439-459

Cited by 1 publication

(1 citation statement)

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“…While 2.5D raster analyses do provide information on the distance up to which a building can be seen and indications as to which areas may be affected by its visual impact, they do not address vertical surfaces, especially facades, as noted by Suleiman et al [35]. In other words, such analyses mostly provide information about the visual impact of a landmark on a building from its higher vantage point.…”

Section: Previous Scientific Work In Respect To Users' Needsmentioning

confidence: 99%

Exploring Landscape Composition Using 2D and 3D Open Urban Vectorial Data

Pedrinis

Samuel

Appert

et al. 2022

IJGI

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Methods and tools for assessing the visual impact of objects such as high-rises are rarely used in planning, despite the increase in opportunities to develop automated visual assessments, now that 3D urban data are acquired and used by municipalities as well as made available through open data portals. This paper presents a new method for assessing city visibility using a 3D model on a metropolitan scale. This method measures the view composition in terms of city objects visible from a given viewpoint and produces a georeferenced and semantically rich database of those visible objects in order to propose a thematic vision of the city and its urban landscape. As far as computational efficiency is concerned and considering the large amount of data needed, the method relies on a dedicated system of automatic data organization for analyzing visibility over vast areas (hundreds of square kilometers), offering various possibilities for uses on different scales. In terms of operational uses, as shown in our paper, the various results produced by the method (quantitative data, georeferenced databases and 3D schematic images) allow for a wide spectrum of applications in urban planning.

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Section: Previous Scientific Work In Respect To Users' Needsmentioning

confidence: 99%