873 research outputs found

    Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

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    This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

    Integrating BIM with ArcGIS for Indoor Navigation

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    With increasing demand for indoor navigation and rapid developments in Building Information Modeling (BIM), indoor routing and analysis attracts attention from both the GIS and architecture worlds. This projectโ€™s goal was to integrate BIM with GIS and utilize it for indoor navigation use. It aimed to provide executable methods in ArcGIS for indoor path generation and to explore the possibilities for further applications. In this project, Data Interoperability Extension was used to operating the transformation from Industry Foundation Classes (IFC) to geodatabase. After importing the data, two methods were proposed: Mesh and TIN. The Mesh method used a standard-sized grid graph as the referencing network for a floor and subsequently mapping the movement on a 2D plane to the movement along grid edges. TIN method utilized the TIN network as the base; it maps the movement on a 2D plane to the movement along TIN edges. Both of the methods were achieved by using tools and functions in ArcGIS. In conclusion, the result shows that the Mesh approach provided a very precise network for the building floor, whereas the TIN approach was efficient on the generating process side

    On the way of integrating evacuation approaches

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    With the growing pressure on available urban space and the construction of more and more complex building infrastructures, the navigational task for building users is getting increasingly difficult. As people react more impulsive under stressful situations, emergencies can exacerbate way finding problems. Additionally, leadership and familiarity with the (topological structure of a) building can influence the ease of finding appropriate evacuation routes. In research, two separate and distinctive techniques for modelling evacuation paths have been developed: evacuation simulation modelling through complex computer simulations and 3D network modelling based on graph theory. Taking into account a global user perspective, the 3D network modelling approach has the advantage to preserve a close connection with the semantic building structure. Following this approach, existing three dimensional evacuation routing algorithms tend to use Dijkstra's shortest path algorithm. However, as more factors, compared to path length, influence evacuation situations, literature acknowledges a void in representing more realistic, complete and accurate emergency situations. This paper presents a first step in creating such integral algorithm by implementing capacity constraints based on user flow control on a 3D geometric network model. In the future additional topics such as zonal partitioning can be added to the algorithm, moving to an integration of both evacuation approaches

    ์Šค์บ” ๋„๋ฉด์„ ํ™œ์šฉํ•œ ์ด๋™์•ฝ์ž์šฉ ์‹ค๋‚ด ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ตฌ์ถ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2021.8. ๋ฐ•์Šฌ์•„.์‚ฌ๋žŒ๋“ค์˜ ์‹ค๋‚ด ํ™œ๋™์ด ๋‹ค์–‘ํ•ด์ง€๋ฉด์„œ ๊ฑด๋ฌผ์˜ ๊ทœ๋ชจ๊ฐ€ ์ปค์ง€๊ณ  ๊ตฌ์กฐ๊ฐ€ ๋ณต์žกํ•ด์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‹ค๋‚ด ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”๋Š” ๊ตํ†ต์•ฝ์ž์˜ ์ด๋™์„ฑ ๋ณด์žฅ์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ๊ด€์‹ฌ์„ ์ฆ๊ฐ€์‹œ์ผฐ์œผ๋ฉฐ, ๊ตํ†ต์•ฝ์ž ๋งž์ถคํ˜• ์‹ค๋‚ด ๋ผ์šฐํŒ… ์„œ๋น„์Šค์— ๋Œ€ํ•œ ์ˆ˜์š” ๋˜ํ•œ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ํŠนํžˆ ๋งŽ์€ ์ด๋™ ์ œ์•ฝ์„ ๊ฐ€์ง€๋Š” ์ด๋™์•ฝ์ž ๋Œ€์ƒ ์„œ๋น„์Šค์˜ ๊ฒฝ์šฐ์—๋Š”, ์ตœ์  ๊ฒฝ๋กœ๋ฅผ ๊ณ„ํšํ•˜๋Š” ๊ณผ์ •์—์„œ ๊ฐœ์ธ์˜ ์„ ํ˜ธ๋‚˜ ๊ฒฝํ—˜์ด ๋ฐ˜์˜๋œ ๊ฐœ์ธํ™”๋œ ์„œ๋น„์Šค๋กœ ๋ฒ”์œ„๊ฐ€ ํ™•์žฅ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฐ๊ฒฝ์—์„œ, ์Šคํ‚ค๋งˆ๊ฐ€ ์œ ์—ฐํ•˜๊ณ  ๋ฐ์ดํ„ฐ์˜ ๊ฐ€๊ณต ๋ฐ ์ฒ˜๋ฆฌ๊ฐ€ ํšจ์œจ์ ์ธ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ๊ตฌ์ถ•์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์Šค์บ”ํ•œ ๋„๋ฉด ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•œ ์ด๋™์•ฝ์ž์šฉ ์‹ค๋‚ด ๊ทธ๋ž˜ํ”„๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ตฌ์ถ• ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ €, ๊ตญ๋‚ด์™ธ ์‹ค๋‚ด ๊ณต๊ฐ„ ๊ด€๋ จ ํ‘œ์ค€ ๋ฐ ์„ค๊ณ„ ๊ธฐ์ค€๋“ค์˜ ๊ฒ€ํ† ๋ฅผ ํ†ตํ•ด ์ด๋™์•ฝ์ž์˜ ํ†ตํ–‰๊ณผ ๊ด€๋ จ๋œ ์‹ค๋‚ด ๊ณต๊ฐ„ ๋ฐ ๊ฐ์ฒด, ์˜ํ–ฅ ์š”์ธ๋“ค์„ ๋„์ถœํ•˜์—ฌ ๊ฐœ๋…์  ๋ฐ์ดํ„ฐ ๋ชจ๋ธ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‹ค๋‚ด์˜ ๊ฐ ๊ณต๊ฐ„๊ณผ ์‹œ์„ค๋ฌผ์˜ ๊ธฐํ•˜์ •๋ณด์™€ ์œ„์ƒ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ด๋™์•ฝ์ž์˜ ์ ‘๊ทผ์„ฑ ๋ฐ ํ†ตํ–‰ ๊ฐ€๋Šฅ์„ฑ์„ ์ •๋Ÿ‰ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ์ ‘๊ทผ์„ฑ ์ง€์ˆ˜๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์Šค์บ” ๋„๋ฉด์„ ์ž…๋ ฅํ•˜์—ฌ ์ด๋™์•ฝ์ž์šฉ ์‹ค๋‚ด ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ตฌ์ถ•์„ ์œ„ํ•œ ํ”„๋กœ์„ธ์Šค๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ํ”„๋กœ์„ธ์Šค๋Š” ์ „์ดํ•™์Šต ๊ธฐ๋ฐ˜ ์ ‘๊ทผ ๋ฐฉ์‹์„ ํ†ตํ•ด ์Šค์บ” ๋„๋ฉด์—์„œ ๊ณต๊ฐ„์˜ ๊ตฌ์กฐ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๊ณ , ํ† ํด๋กœ์ง€ ์ถ”์ถœ ๋ฐ ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ์ด๋™์•ฝ์ž์šฉ ๋„คํŠธ์›Œํฌ ๋ชจ๋ธ์„ ์ƒ์„ฑํ•˜๋ฉฐ, ์ƒ์„ฑํ•œ ๋„คํŠธ์›Œํฌ ๋ชจ๋ธ์„ ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋กœ ์ž๋™ ๋ณ€ํ™˜ํ•˜๋Š” ๊ณผ์ •์„ ํฌํ•จํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ œ์•ˆ ํ”„๋กœ์„ธ์Šค๋Š” ์ˆ˜์ •๋œ ResNet ๊ธฐ๋ฐ˜์˜ ๋ชจ๋ธ์„ ์ƒˆ๋กญ๊ฒŒ ๋ผ๋ฒจ๋งํ•œ ๋„๋ฉด์œผ๋กœ ๋ฏธ์„ธ ์กฐ์ •ํ•˜์—ฌ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ์‹ค๋‚ด ๊ตฌ์กฐ๋งต์„ ์ƒ์„ฑํ•œ๋‹ค. ์ดํ›„ ์ถ”์ถœ๋œ ๊ฐ์ฒด๋“ค์˜ ๊ณต๊ฐ„ ๊ด€๊ณ„๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฐ ๊ณต๊ฐ„์„ ๋…ธ๋“œ์™€ ๋งํฌ๋กœ ํ‘œํ˜„ํ•œ ์‹ค๋‚ด ๋„คํŠธ์›Œํฌ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•œ๋‹ค. ๊ฐ ๊ณต๊ฐ„์˜ ์ ‘๊ทผ์„ฑ ์ •๋ณด๋Š” ์ œ์•ˆ๋œ ์ ‘๊ทผ์„ฑ ์ง€์ˆ˜์™€ ์ž„๊ณ„๊ฐ’์„ ์‚ฌ์šฉํ•˜์—ฌ ์ƒ์„ฑ๋œ ํ›„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์— ์ €์žฅ๋˜์–ด, ์ด๋™์•ฝ์ž๋ฅผ ์œ„ํ•œ ์ ‘๊ทผ ๊ฐ€๋Šฅํ•œ ๊ทธ๋ž˜ํ”„ ์ถ”์ถœ์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์„ ์„œ์šธ๋Œ€ํ•™๊ต ๋„๋ฉด ๋ฐ์ดํ„ฐ ์…‹์— ์ ์šฉํ•˜์—ฌ ์ด๋™์•ฝ์ž์šฉ ์‹ค๋‚ด ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ตฌ์ถ•ํ•œ ์‹ค๋‚ด ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์ธต ๊ฒฝ๋กœ ๊ณ„ํš๊ณผ ์‹ค๋‚ด์™ธ ์—ฐ๊ณ„ ๊ฒฝ๋กœ ๊ณ„ํš์˜ 2๊ฐ€์ง€ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ผ ์ตœ์  ๊ฒฝ๋กœ๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ผ๋ฐ˜ ๋ณดํ–‰์ž์˜ ์ตœ์  ๊ฒฝ๋กœ์™€ ๋น„๊ตํ•˜์—ฌ ์ด๋™์•ฝ์ž์šฉ ์ตœ์  ๊ฒฝ๋กœ๋Š” ๊ฐ€๊นŒ์šด ๊ณ„๋‹จ์ด ์•„๋‹Œ ์—˜๋ฆฌ๋ฒ ์ดํ„ฐ๋ฅผ ํ†ตํ•œ ์ˆ˜์ง ์ด๋™์„ ํฌํ•จํ•˜์˜€์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ ‘๊ทผ ๋ถˆ๊ฐ€๋Šฅํ•œ ๊ณต๊ฐ„์„ ํšŒํ”ผํ•˜๋„๋ก ๋„์ถœ๋˜์—ˆ๋‹ค. ์ฆ‰, ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์ด๋™์•ฝ์ž ์ธก๋ฉด์—์„œ ํ†ตํ–‰ ์žฅ์•  ์ •๋ณด๋ฅผ ํฌํ•จํ•˜์—ฌ ์‹ค๋‚ด ํ™˜๊ฒฝ์„ ์ ์ ˆํ•˜๊ฒŒ ๋ฌ˜์‚ฌํ•˜๋Š” ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ๊ตฌ์ถ•์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ์ถœ์ž…๋กœ๋กœ ๋ช…๋ช…๋œ ๊ด€๊ณ„ ์ƒ์„ฑ๋งŒ์œผ๋กœ ์Šค์ผ€์ผ์ด๋‚˜ ์ขŒํ‘œ ๋ณ€ํ™˜ ์—†์ด ์‹ค๋‚ด์™ธ ์—ฐ๊ณ„ ๊ฒฝ๋กœ ๊ณ„ํš์ด ๊ฐ€๋Šฅํ•˜์˜€๋Š”๋ฐ, ์ด๋Š” ๋…๋ฆฝ์ ์ธ ๋ฐ์ดํ„ฐ ๊ฐ„ ์—ฐ๊ณ„ ์‚ฌ์šฉ์— ์ ํ•ฉํ•œ ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•œ ๊ฒฐ๊ณผ๋กœ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ธฐ์—ฌ๋Š” ์Šค์บ”ํ•œ ๋„๋ฉด์„ ์‚ฌ์šฉํ•˜์—ฌ ์ด๋™์•ฝ์ž์šฉ ์‹ค๋‚ด ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•œ ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐœ๋ฐœํ•œ ๊ฒƒ์ด๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ด๋™์•ฝ์ž์˜ ์ด๋™์— ์ดˆ์ ์„ ๋‘๊ณ  ์„ค๊ณ„ํ•œ ๋ฐ์ดํ„ฐ ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ตฌ์ถ•์ด ๊ฐ€๋Šฅํ•˜๋ฏ€๋กœ ์ด๋™์•ฝ์ž์šฉ ์‹ค๋‚ด ๊ธธ์•ˆ๋‚ด ์„œ๋น„์Šค์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ํ† ํด๋กœ์ง€ ๊ตฌ์ถ• ๋ฐ ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋กœ์˜ ๋ณ€ํ™˜์„ ์œ„ํ•œ ํ•˜์œ„ ํ”„๋กœ์‹œ์ ธ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€์œผ๋ฉฐ, ์ œ์•ˆ ํ”„๋กœ์„ธ์Šค๋Š” ํ•ด๋‹น ํ”„๋กœ์‹œ์ ธ๋“ค๋กœ ๊ตฌ์„ฑ๋˜์–ด ๋„๋ฉด ์ž…๋ ฅ์„ ํ†ตํ•ด ์ด๋™์•ฝ์ž์šฉ ์‹ค๋‚ด ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ตฌ์ถ•์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ํ•ด๋‹น ํ•˜์œ„ ํ”„๋กœ์‹œ์ ธ๋“ค์€ ์ž๋™์œผ๋กœ ์ˆ˜ํ–‰๋  ์ˆ˜ ์žˆ์–ด ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ตฌ์ถ• ์‹œ ์†Œ์š”๋˜๋Š” ์‹œ๊ฐ„๊ณผ ๋น„์šฉ์„ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๋‹ค์–‘ํ•œ ์ •ํ˜• ๋ฐ ๋น„์ •ํ˜• ๋ฐ์ดํ„ฐ์˜ ์—ฐ๊ณ„์— ์ ํ•ฉํ•œ ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ํŠน์ง•์— ์˜ํ•ด, ์ œ์•ˆํ•œ ํ”„๋กœ์„ธ์Šค๋ฅผ ํ†ตํ•ด ๊ตฌ์ถ•ํ•œ ์‹ค๋‚ด ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋Š” ๊ธฐ์กด ๊ณต๊ฐ„ ๋ชจ๋ธ์˜ ๊ธฐ๋Šฅ์„ ํฌํ•จํ•˜๋ฉด์„œ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ๊ธธ์•ˆ๋‚ด ์„œ๋น„์Šค์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Changes to the indoor environment have increased social interest in ensuring the mobility of people with disabilities. Therefore, the demand for customized indoor routing services for people with mobility disabilities (PWMD), who have many travel restrictions, is increasing. These services have progressed from spatial routing to personalized routing, which reflects personal preferences and experiences in planning an optimal path. In this regard, it is necessary to generate a database for PWMD with a flexible schema suitable for the efficient manipulation and processing of data. This study aims to propose a technique of generating an indoor graph database for PWMD using scanned floor plans. First, a conceptual data model was developed by deriving relevant indoor features and influential factors, considering various international regulations on indoor environments. Also, the accessibility index was designed based on the data model to quantify the difficulties in accessing spaces based on each indoor spaces geometric characteristics. Next, a three-stage process was proposed: retrieving the structure of spaces from scanned floor plans through a transfer learning-based approach, retrieving topology and assessing accessibility for creating an indoor network model for PWMD, and converting the network model into a graph database. Specifically, an indoor structure map is created by fine-tuning the modified Resnet-based model with newly annotated floor plans for extracting structure information. Also, based on the spatial relationship of the extracted features, the indoor network model was created by abstracting indoor spaces with nodes and links. The accessibility of each space is determined by the proposed indices and thresholds; thereby, a feasible network for PWMD could be derived. Then, a process was developed for automatically converting an indoor network model, including accessibility property, into a graph database. The proposed technique was applied to the Seoul National University dataset to generate an indoor graph database for PWMD. Two scenario-based routing tests were conducted using the generated database to verify the utility of results: multi-floor routing and integrated indoor-outdoor routing. As a result, compared with the path for general pedestrians, the optimal path for PWMD was derived by avoiding inaccessible spaces, including vertical movement using elevators rather than the nearest stairs. In other words, applying the proposed technique, a database that adequately described an indoor environment in terms of PWMD with sufficient mobile constraint information could be constructed. Moreover, an integrated indoor-outdoor routing could be conducted by only creating an entrance-labeled relationship, without scale and coordinate transformation. This result reflects the usability of the generated graph database and its suitability regarding the incorporation of multiple individual data sources. The main contribution lies in the development of the process for generating an indoor graph database for PWMD using scanned floor plans. In particular, the database for PWMD routing can be generated based on the proposed data model with PWMD-related features and factors. Also, sub-procedures for topology retrieval and graph database conversion are developed to generate the indoor graph database by the end-to-end process. The developed sub-procedures are performed automatically, thereby reducing the required times and costs. It is expected that the target database of the proposed process can be generated considering utilization for various types of routing since the graph database is easily integrated with multiple types of information while covering the existing spatial models function.1. Introduction 1 1.1 Objectives and contributions 1 1.2 Related works 7 1.2.1 Indoor environment conceptualization 7 1.2.2 Indoor data construction 11 1.2.3 Accessibility assessment 19 1.3 Research scope and flow 22 2. Conceptual modeling 26 2.1 Relevant features and factors 28 2.2 Proposed data model 30 2.3 Space accessibility for PWMD 36 2.3.1 Influential factors within indoor environments 37 2.3.2 Accessibility index 41 3. Indoor graph database for PWMD from scanned floor plans 43 3.1 Retrieving structure of indoor spaces 43 3.1.1 Pre-trained model for detecting indoor geometry 45 3.1.2 Dataset with new annotation 47 3.1.3 Transfer learning-based approach 52 3.2 Generating the indoor network model for PWMD 56 3.2.1 Definition of nodes and links in the network model 60 3.2.2 The classification rule of space polygons 63 3.2.3 Connection between general spaces and doors 68 3.2.4 Node-link generation for horizontal transition spaces 71 3.2.5 Vertical link generation 75 3.2.6 Connectivity and accessibility information generation 79 3.3 Indoor graph database for PWMD 80 3.3.1 Graph representation of indoor environments 80 3.3.2 Conversion of network model into graph database 83 3.4 Entire process 87 4. Experiment and results 89 4.1 Experimental setup and test data 89 4.2 Evaluation for retrieved information 92 4.2.1 Results of structure retrieval 92 4.2.2 Results of topology retrieval 99 4.3 Generated indoor graph database for PWMD 128 4.3.1 Results of the indoor graph database for PWMD 128 4.3.2 Query-based routing 136 5. Conclusion 147 References 150 Appendix 166 ๊ตญ๋ฌธ์ดˆ๋ก 178๋ฐ•

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