135 research outputs found

    Extracting individual trees from lidar point clouds using treeseg

    Get PDF
    Recent studies have demonstrated the potential of lidar-derived methods in plant ecology and forestry. One limitation to these methods is accessing the information content of point clouds, from which tree-scale metrics can be retrieved. This is currently undertaken through laborious and time-consuming manual segmentation of tree-level point clouds from larger-area point clouds, an effort that is impracticable across thousands of stems. Here, we present treeseg, an open-source software to automate this task. This method utilises generic point cloud processing techniques including Euclidean clustering, principal component analysis, region-based segmentation, shape fitting and connectivity testing. This data-driven approach uses few a priori assumptions of tree architecture, and transferability across lidar instruments is constrained only by data quality requirements. We demonstrate the treeseg algorithm here on data acquired from both a structurally simple open forest and a complex tropical forest. Across these data, we successfully automatically extract 96% and 70% of trees, respectively, with the remainder requiring some straightforward manual segmentation. treeseg allows ready and quick access to tree-scale information contained in lidar point clouds. treeseg should help contribute to more wide-scale uptake of lidar-derived methods to applications ranging from the estimation of carbon stocks through to descriptions of plant form and function

    ๋Šํ‹ฐ๋‚˜๋ฌด ๋ฐ ๋ฒš๋‚˜๋ฌด ๊ฐ€๋กœ์ˆ˜ ๊ฐœ๋ณ„๋ชฉ์„ ๋Œ€์ƒ์œผ๋กœ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™),2019. 8. ์ด๋™๊ทผ.๊ทผ๋Œ€ํ™” ์ดํ›„ ์ธ๋ฅ˜์˜ ํ™œ๋™์€ ๊ธ‰์†ํ•œ ๊ณผํ•™๊ธฐ์ˆ ์˜ ๋ฐœ์ „๊ณผ ๊ฒฝ์ œ์„ฑ์žฅ์œผ๋กœ ์ง€๊ตฌ์ƒํƒœ๊ณ„์— ์ง์ ‘์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ผ์ณ์™”๋‹ค. ์ƒํƒœ๊ณ„ ํŒŒ๊ดด์™€ ๊ด€๋ จ๋œ ํ™˜๊ฒฝ๋ฌธ์ œ, ํŠนํžˆ ์ง€๊ตฌ์˜จ๋‚œํ™” ๋ฌธ์ œ๊ฐ€ ๊ฐ€์†ํ™” ๋จ์— ๋”ฐ๋ผ ๋งŽ์€ ์—ฐ๊ตฌ์ž๋“ค์ด ๋„์‹œ์˜ ์ฃผ๋ณ€ํ™˜๊ฒฝ, ๋„์‹œ ๋…น์ง€, ๋„์‹œ๊ณต์› ๋ฐ ์œก์ƒ ๋…นํ™”์™€ ๊ฐ™์€ ๋Œ€์ƒ์— ์ฃผ๋ชฉํ•˜๋Š” ์ถ”์„ธ์ด๋‹ค. ๊ทธ ๊ฐ€์šด๋ฐ ๋„๋กœ ์ƒํƒœํ™˜๊ฒฝ์˜ ์ค‘์š”ํ•œ ๋ถ€๋ถ„์ธ ๊ฐ€๋กœ์ˆ˜๋Š” ๋„์‹œ์˜ ๋…นํ™” ํ™œ๋™์˜ ์ค‘์‹ฌ์ง€์ด์ž, ๋„์‹œ์˜ ํƒ„์†Œ ์ €์žฅ์ง€๋กœ ๊ผฝํžŒ๋‹ค. ์ˆ˜๋ชฉ์€ ํƒ„์†Œ ์ˆœํ™˜์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ์ˆ˜๋ชฉ์˜ ์ฒด์ , ๋ฐ”์ด์˜ค๋งค์Šค, ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์€ ํƒ„์†Œ ๊ณ ์ •์—์„œ๋ถ€ํ„ฐ ๊ด‘ํ•ฉ์„ฑ์„ ํ†ตํ•œ ์‚ฐ์†Œ ๋ฐฉ์ถœ ๋“ฑ ํƒ„์†Œ์ˆœํ™˜๊ธฐ๋Šฅ์„ ํ‰๊ฐ€ํ•˜๋Š” ์ค‘์š”ํ•œ ๋งค๊ฐœ๋ณ€์ˆ˜๋กœ ํ™œ์šฉ๋œ๋‹ค. ๊ธฐํ›„๋ณ€ํ™”์™€ ์ง€๊ตฌ์˜จ๋‚œํ™”์˜ ์šฐ๋ ค์™€ ํ•จ๊ป˜ ๋„์‹œ์—์„œ ์—๋„ˆ์ง€์‚ฌ์šฉ๊ณผ ์ด์‚ฐํ™”ํƒ„์†Œ ๋ฐœ์ƒ์„ ์ €๊ฐ์‹œํ‚ค๊ณ  ํƒ„์†Œํก์ˆ˜์›์„ ์ฆ์ง„์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ์ค‘์š”ํ•œ ์ •์ฑ…์œผ๋กœ ๋ฐ›์•„๋“ค์—ฌ์ง€๋ฉด์„œ ๋„์‹œ ์ˆ˜๋ชฉ๊ณผ ๋…น์ง€์˜ ๊ธฐํ›„๋ณ€ํ™” ๋Œ€์‘ ๊ด€๋ จ ๊ธฐ๋Šฅ์ด ๋”์šฑ ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ˆ˜๋ชฉ์˜ ์žฌ์  ๋ฐ ๋ฐ”์ด์˜ค๋งค์Šค๋ฅผ ์‚ฐ์ •ํ•  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค. ๊ธฐ์กด์˜ ์ธก์ •๋ฐฉ๋ฒ•์€ ์ˆ˜๋ชฉ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ์ธก์ •ํ•˜์—ฌ ์ƒ๋Œ€์ƒ์žฅ์‹, ์ž…๋ชฉ์žฌ์ ์‹์„ ํ†ตํ•ด ์‚ฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ๋ฐฉ๋ฒ•์€ ๋…ธ๋™์ง‘์•ฝ์ ์ด๊ณ  ์‹œ๊ฐ„์ด ์˜ค๋ž˜ ๊ฑธ๋ฆฌ๋ฉฐ ํŒŒ๊ดด์ ์ด๋ผ๋Š” ์ ์ด ํ•œ๊ณ„๋กœ ์ง€์ ๋˜๊ณ  ์žˆ๋‹ค. ์ตœ๊ทผ์—๋Š” ์ง€์ƒ LiDAR (Light Detection and Ranging) ์„ผ์„œ๊ฐ€ 3์ฐจ์› ์ˆ˜๋ชฉ ๊ตฌ์กฐ๋ฅผ ์ •ํ™•ํ•˜๊ณ  ํšจ์œจ์ ์œผ๋กœ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๋„๊ตฌ๋กœ ํญ๋„“๊ฒŒ ํ™œ์šฉ๋œ๋‹ค. ๋ชฉํ‘œ๊ฐ€ ๋˜๋Š” ๋ฌผ์ฒด๋ฅผ ์ง€์ƒ LiDAR ์„ผ์„œ๋ฅผ ํ™œ์šฉํ•ด ์ ๊ตฐ๋ฐ์ดํ„ฐ(Point cloud) ํ˜•ํƒœ์˜ ์ •ํ™•ํ•œ 3์ฐจ์› ๊ธฐ์ˆ ์€ ๊ฐ๊ด€์ ์ธ ๋ฌผ์ฒด๋ฅผ ๋ณต์›ํ•˜๊ณ , ์ง„์‹ค์ ์œผ๋กœ ํ‘œ๋ฉด์˜ ํ˜•ํƒœ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋Š” ํŠน์„ฑ์ด ์žˆ์œผ๋ฉฐ ์œ„์น˜ ์ •๋ณด๋ฅผ ์ถœ๋ ฅํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜์—ฌ 3D ๋ชจ๋ธ๋ง ๋ฐ ์ˆ˜๋ชฉ์˜ ํ‘œ๋ฉด ์žฌ๊ตฌ์„ฑ ๋ถ„์•ผ์—์„œ๋„ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ ์„œ์šธ์—์„œ ๊ฐ€๋กœ์ˆ˜๋กœ ์ฃผ๋กœ ์‹์žฌ๋˜๋Š”๋Šํ‹ฐ๋‚˜๋ฌด์™€ ๋ฒš๋‚˜๋ฌด๋ฅผ ์—ฐ๊ตฌ๋Œ€์ƒ์œผ๋กœ 3์›”๊ณผ 6์›” ์„œ๋กœ ๋‹ค๋ฅธ ์‹œ๊ธฐ์— ์ง€์ƒ LiDAR ์„ผ์„œ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ทจ๋“ํ•˜๊ณ , ๋‹จ๋ชฉ ๋ถ€์œ„๋ณ„ ์ฒด์  ์‚ฐ์ •๋Ÿ‰์„ ๋น„๊ตํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ˆ˜์ข…๋ณ„ ์‚ฐ์ •๋ฐฉ๋ฒ•์œผ๋กœ ๊ธฐ์กด ์‚ฐ๋ฆผ์ธก๋Ÿ‰๋ถ„์•ผ์—์„œ ์‚ฌ์šฉํ•˜๋Š” ์ž…๋ชฉ์žฌ์ ์‹, ์ƒ๋Œ€์ƒ์žฅ์‹, ๊ทธ๋ฆฌ๊ณ  ์ง€์ƒ LiDAR ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ Voxelํ™”(Voxelize) ์‚ฐ์ • ๋ฐฉ๋ฒ• ๋ฐ ๊ณ„์ธตํ™” ๋ฐฉ๋ฒ• ๋“ฑ ๋‹ค์–‘ํ•œ ์‚ฐ์ • ๋ฐฉ๋ฒ•์„ ํ†ตํ•˜์—ฌ ์ˆ˜๋ชฉ์˜ ํ˜•ํƒœ๋ฅผ ๊ณ ๋ คํ•œ ๋‹จ๋ชฉ ๋ถ€์œ„๋ณ„ ์ฒด์ ์„ ์‚ฐ์ •ํ•˜๊ณ  ๋น„๊ตํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ธฐ์กด ์‚ฐ๋ฆผ์ธก๋Ÿ‰ ๋ถ„์•ผ์—์„œ ์‚ฌ์šฉํ–ˆ๋˜ ์ž…๋ชฉ์žฌ์ ์‹์— ๋น„ํ•˜์—ฌ ๋Šํ‹ฐ๋‚˜๋ฌด์˜ ๊ฒฝ์šฐ ์ˆ˜๊ฐ„๋ถ€ ๋ณต์…€ํ™” ์žฌ์ ์ด 25.79% ์ฆ๊ฐ€ํ•˜๊ฑฐ๋‚˜ ๋งˆ์ด๋„ˆ์Šค 16.39%๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๊ณ„์ธตํ™”๋กœ ์ถ”์ •ํ•˜๋Š”๋ฐ 17.36% ์ฆ๊ฐ€ํ•˜๊ฑฐ๋‚˜ 25.17% ๋‚ฎ์•˜๋‹ค. ๋ฒš๋‚˜๋ฌด์˜ ์ˆ˜๊ฐ„๋ถ€ ๋ณต์…€ํ™” ์žฌ์ ์€ 42.14% ์ฆ๊ฐ€ํ•˜๊ฑฐ๋‚˜ ๋งˆ์ด๋„ˆ์Šค 26.24%๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ  ๊ณ„์ธตํ™”๋กœ ์ถ”์ •ํ•˜๋Š”๋ฐ 22.96% ์ฆ๊ฐ€ํ•˜๊ฑฐ๋‚˜ ๋งˆ์ด๋„ˆ์Šค 26.24% ๋‚ฎ์•˜๋‹ค. ๊ฐ€์ง€๋ถ€ ๋ณต์…€ํ™” ์žฌ์ ๊ฒฐ๊ณผ๋ฅผ ๋ดค์„ ๋•Œ์— ๋ฒš๋‚˜๋ฌด 66.40% ์ฆ๊ฐ€ํ•˜๊ฑฐ๋‚˜ 43.44% ๋‚ฎ์•˜์œผ๋ฉฐ ๋Šํ‹ฐ๋‚˜๋ฌด๋Š” 24.98 ์ฆ๊ฐ€ํ•˜๊ฑฐ๋‚˜ 20.89% ๋‚ฎ์•˜๋‹ค. ์—ฐ๊ตฌ๋Š” ๊ฐ€๋กœ์ˆ˜ ์ˆ˜์ข…์ธ ๋Šํ‹ฐ๋‚˜๋ฌด, ๋ฒš๋‚˜๋ฌด์˜ ์ž…๋ชฉ์žฌ์ ๋Ÿ‰์„ LiDAR ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋‹ค์–‘ํ•œ ์‚ฐ์ •์‹์„ ํ™œ์šฉํ•ด ์ถ”์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์‚ฐ์ •๋ฐฉ๋ฒ•์˜ ์ฐจ์ด์— ๋”ฐ๋ผ ์ˆ˜์ข…๋ณ„ ์ž…๋ชฉ์žฌ์ ๋Ÿ‰์˜ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ˆ˜๋ชฉ์˜ ๊ตฌ์กฐ์™€๋„ ์ผ์ •ํ•œ ์ฐจ์ด ๋ฐ ๊ด€๊ณ„๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.์ œ1์žฅ ์„œ ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  3 ์ œ2์žฅ ์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 4 ์ œ1์ ˆ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 4 1. ์ง€์ƒ LiDAR 4 2. ์ž…๋ชฉ์žฌ์  5 ์ œ2์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 6 1. LiDAR ์ธก๋Ÿ‰์„ ํ†ตํ•œ ์ˆ˜๋ชฉ ์žฌ์  ์—ฐ๊ตฌ 6 2. ์†Œ๊ฒฐ 8 ์ œ3์žฅ ์—ฐ๊ตฌ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 9 ์ œ1์ ˆ ์—ฐ๊ตฌํ๋ฆ„ 9 ์ œ2์ ˆ ์—ฐ๊ตฌ๋ฒ”์œ„ 10 ์ œ3์ ˆ ๋ฐ์ดํ„ฐ ํš๋“ ๋ฐ ์ „์ฒ˜๋ฆฌ 11 1. ํ˜„์žฅ์กฐ์‚ฌ 11 2. ์ง€์ƒ LiDAR ์ธก๋Ÿ‰ 15 ์ œ4์ ˆ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 17 1. ์ง€์ƒ LiDAR ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ 17 1.1. ๋‹จ์ผ ์ˆ˜๋ชฉ ์ถ”์ถœ 17 1.2. ํ‰๊ณ ์ง๊ฒฝ ์ถ”์ถœ 18 1.3. ์ˆ˜๊ณ  ์ถ”์ถœ 19 2. ์ˆ˜๋ชฉ์˜ ๋ถ€์œ„๋ณ„ ์žฌ์  ์ถ”์ • 20 2.1. ์ง€์ƒ LiDAR ์ธก๋Ÿ‰์— ์˜ํ•œ ์ˆ˜๊ฐ„๋ถ€ ์žฌ์  ์ถ”์ • 20 2.1.1. Smalian์‹์— ์˜ํ•œ ์ˆ˜๊ฐ„๋ถ€ ์žฌ์  ์ถ”์ • 20 2.1.2. ๊ณ„์ธตํ™” ๋ฐฉ์‹์— ์˜ํ•œ ์žฌ์  ์ถ”์ • 21 2.1.3. Voxelํ™” ๋ฐฉ์‹์— ์˜ํ•œ ์žฌ์  ์ถ”์ • 22 2.2. ์ง€์ƒ LiDAR ์ธก๋Ÿ‰์— ์˜ํ•œ ๊ฐ€์ง€๋ถ€ ์žฌ์  ์ถ”์ • 23 2.2.1. ์ƒ๋Œ€์ƒ์žฅ์‹์„ ํ†ตํ•œ ๊ฐ€์ง€๋ถ€ ์žฌ์  ์ถ”์ • 23 2.2.2. Voxelํ™” ๋ฐฉ์‹์— ์˜ํ•œ ๊ฐ€์ง€๋ถ€ ์žฌ์  ์ถ”์ • 23 ์ œ4์žฅ ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 24 ์ œ1์ ˆ ๋ถ€์œ„๋ณ„ ์žฌ์ ๋Ÿ‰ ์‚ฐ์ • ๊ฒฐ๊ณผ 24 1. ์ˆ˜๊ฐ„๋ถ€ ์žฌ์  24 2. ๊ฐ€์ง€๋ถ€ ์žฌ์  36 2. ์˜†์ธต๋ถ€ ์ฒด์  42 2. ์ˆ˜๋ชฉ ์ง€์ƒ๋ถ€ ๋ฐ”์ด์˜ค๋งค์Šค 44 ์ œ2์ ˆ ์ˆ˜๋ชฉ์˜ ํ‰๊ณ ์ง๊ฒฝ ๋ฐ ์ˆ˜๊ณ  ์‚ฐ์ •๊ฒฐ๊ณผ 51 ์ œ5์žฅ ๊ฒฐ๋ก  56 ์ฐธ๊ณ ๋ฌธํ—Œ 59Maste

    Stem Quality Estimates Using Terrestrial Laser Scanning Voxelized Data and a Voting-Based Branch Detection Algorithm

    Get PDF
    A new algorithm for detecting branch attachments on stems based on a voxel approach and line object detection by a voting procedure is introduced. This algorithm can be used to evaluate the quality of stems by giving the branch density of each standing tree. The detected branches were evaluated using field-sampled trees. The algorithm detected 63% of the total amount of branch whorls and 90% of the branch whorls attached in the height interval from 0 to 10 m above ground. The suggested method could be used to create maps of forest stand stem quality data

    The benefit of 3D laser scanning technology in the generation and calibration of FEM models for health assessment of concrete structures

    Get PDF
    Terrestrial laser scanning technology (TLS) is a new technique for quickly getting three-dimensional information. In this paper we research the health assessment of concrete structures with a Finite Element Method (FEM) model based on TLS. The goal focuses on the benefits of 3D TLS in the generation and calibration of FEM models, in order to build a convenient, efficient and intelligent model which can be widely used for the detection and assessment of bridges, buildings, subways and other objects. After comparing the finite element simulation with surface-based measurement data from TLS, the FEM model is determined to be acceptable with an error of less than 5%. The benefit of TLS lies mainly in the possibility of a surface-based validation of results predicted by the FEM model

    Tree biomass equations from terrestrial LiDAR : a case study in Guyana

    Get PDF
    Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates (R-2 = 0.92-0.93) than traditional pantropical models (R-2 = 0.85-0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested (R-2 = 0.89) and predicted AGB accurately across all size classes-which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees

    RESEARCH ON THE 3D DOCUMENTATION SYSTEM OF CLASSICAL CHINESE GARDENS IN SCENIC AREAS โ€“ TAKING KUNSHAN SUIYUAN AS AN EXAMPLE

    Get PDF
    Classical Chinese gardens built in scenic areas have characteristics in rich scales, multi-type elements, and a complex development history. Due to the designed landscape characteristics of "hidden the garden in bigger landscapes", it is often difficult to obtain high-quality heritage information through traditional surveying approaches. The aim of this research is to establish a digital 3D documentation technology system for the classical gardens in scenic areas. Suiyuan Garden, a classical Chinese garden with a history of more than 300 years, was used as a case study. We have integrated UAV low-altitude photogrammetry, handheld laser scanning, ground-mounted laser scanning, macro structured light scanning and other technologies to obtain the 3D data, and to build a digital twin by developing the technical workflow and data standard. The innovation of the research lies in: 1) We used a variety of digital mapping methods to record the pattern between the garden and the surrounding landscape; 2) We adopted a combination of handheld and ground-mounted laser scanning systems to build spatial structures and better cover the ground spaces; 3) We established a workflow for the conversion from point cloud to mesh model for different garden features, which can improve the usability of 3D heritage information; 4) We used the 3D cultural heritage modelling approach to improve the standards of the digital conservation of historic plants. The outcomes of this research could be used as the basis for the construction of Historic Landscape Information Model (HLIM) in the near future
    • โ€ฆ
    corecore