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Research ArticlePeer-Reviewed Articles

Artificial Intelligence in Landscape Architecture

A Literature Review

Phillip Fernberg and Brent Chamberlain
Landscape Journal, May 2023, 42 (1) 13-35; DOI: https://doi.org/10.3368/lj.42.1.13
Phillip Fernberg
Phillip Fernberg is a landscape designer, PhD candidate, and researcher in Utah State University’s Visualization, Instrumentation and Virtual Interaction Design (VIVID) Laboratory. He has earned an MLA from Louisiana State University and a BA in Latin American Studies from Brigham Young University. Fernberg’s current research focuses on spatial cognition in complex virtual environments and the implications of artificial intelligence for landscape architecture practice. He has published articles in several international journals and magazines and is a current recipient of the LAF Fellowship for Innovation and Leadership.
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Brent Chamberlain
Brent Chamberlain, PhD, is an associate professor of landscape architecture and environmental planning at Utah State University. His expertise as a computational environmental planner is built on three foci: 1) visualization and spatial data science, 2) applied computational approaches (including optimization and artificial intelligence), and 3) environmental perception and affect related to built and natural environments. His work has been published in several international journals, and his research has been funded by several national and state agencies, including the NSF, DoD, NIDILRR, UT DOT, and UT Public Lands. More can be found at:
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REFERENCES

  1. ↵
    1. Abdollahi, S.,
    2. Ildoromi, A.,
    3. Salmanmahini, A., &
    4. Fakheran, S.
    (2022). Optimization of geographical space of ecosystem service areas and land-use planning, Iran. Environmental Monitoring and Assessment, 194(8), 527. https://doi.org/10.1007/s10661-022-10204-7
    OpenUrl
  2. ↵
    1. Abduljabbar, R.,
    2. Dia, H.,
    3. Liyanage, S., &
    4. Bagloee, S. A.
    (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), Article 1. https://doi.org/10.3390/su11010189
    OpenUrl
  3. ↵
    1. Abdullah, M. S.,
    2. Kimble, C.,
    3. Benest, I., &
    4. Paige, R.
    (2006). Knowledge-based systems: A re-evaluation. Journal of Knowledge Management.
  4. ↵
    1. Abioye, S. O.,
    2. Oyedele, L. O.,
    3. Akanbi, L.,
    4. Ajayi, A.,
    5. Davila Delgado, J. M.,
    6. Bilal, M.,
    7. Akinade, O. O., &
    8. Ahmed, A.
    (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299. https://doi.org/10.1016/j.jobe.2021.103299
    OpenUrl
  5. ↵
    1. Abusaada, H., &
    2. Elshater, A.
    (2021). Competitiveness, distinctiveness and singularity in urban design: A systematic review and framework for smart cities. Sustainable Cities and Society, 68, 102782. https://doi.org/10.1016/j.scs.2021.102782
    OpenUrl
  6. ↵
    1. Akerkar, R., &
    2. Sajja, P.
    (2009). Knowledge-based systems. Jones & Bartlett Publishers.
  7. ↵
    1. Alegria, C.,
    2. Roque, N.,
    3. Albuquerque, T.,
    4. Fernandez, P., &
    5. Ribeiro, M. M.
    (2021). Modelling maritime pine (Pinus pinaster aiton) spatial distribution and productivity in Portugal: Tools for forest management. Forests, 12(3). Scopus. https://doi.org/10.3390/f12030368
  8. ↵
    1. Alina, P.,
    2. Oliviu-Dorin, M.,
    3. Iuliana, B., &
    4. Valean, H.
    (2016). Developing a feasible and maintainable ontology for automatic landscape design. International Journal of Advanced Computer Science and Applications, 7(3). https://doi.org/10.14569/IJACSA.2016.070351
  9. ↵
    1. Allam, Z., &
    2. Dhunny, Z. A.
    (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032
    OpenUrl
  10. ↵
    1. Almahmood, M., &
    2. Skov-Petersen, H.
    (2020). Public space public life 2.0: Agent-based pedestrian simulation as a dynamic visualisation of social life in urban spaces. Journal of Digital Landscape Architecture, 2020(5), 305–317. Scopus. https://doi.org/10.14627/537690032
    OpenUrl
  11. ↵
    1. Amorós, L., &
    2. J. Ledesma
    , 2020. Aerial robotics and forest management and seeding. Chapter 7, pp102-111, in Elliott S., G, Gale & M. Robertson (Eds), Automated Forest Restoration: Could Robots Revive Rain Forests? Proceedings of a brainstorming workshop, Chiang Mai University, Thailand. 254 pp.
  12. ↵
    1. Anderson, K. E.,
    2. Glenn, N. F.,
    3. Spaete, L. P.,
    4. Shinneman, D. J.,
    5. Pilliod, D. S.,
    6. Arkle, R. S.,
    7. McIlroy, S. K., &
    8. Derryberry, D. R.
    (2018). Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning. Ecological Indicators, 84, 793–802. https://doi.org/10.1016/j.ecolind.2017.09.034
    OpenUrl
  13. ↵
    1. Ask, P., &
    2. Carlsson, M.
    (2000). Nature conservation and timber production in areas with fragmented ownership patterns. Forest Policy and Economics, 1(3–4), 209–223. Scopus. https://doi.org/10.1016/s1389-9341(00)00016-2
    OpenUrl
  14. ↵
    1. ASLA
    . Landscape Architecture: A Solution to the Climate Crisis. (2022). asla.org. Retrieved February 2023, from https://www.asla.org/climateaction.aspx
  15. ↵
    1. Barbarash, D.,
    2. Rasheed, M., &
    3. Gupta, A.
    (2022). Automated recording of human movement using an artificial intelligence identification and mapping system. Journal of Digital Landscape Architecture. https://doi.org/10.14627/537724007
  16. ↵
    1. Barbieri, L.,
    2. Wyngaard, J.,
    3. Galford, G. L.,
    4. Thomer, A.,
    5. Bittner, C., &
    6. Adair, C.
    (2018). Technology in agroecosystems: Integrative land-atmosphere monitoring with unmanned aerial systems. 2018, IN13B-08.
  17. ↵
    1. Bergier, I.,
    2. Silva, A. P. S.,
    3. Abreu, U. G. P. D.,
    4. Oliveira, L. O. F. D.,
    5. Tomazi, M.,
    6. Dias, F. R. T.,
    7. Urbanetz, C.,
    8. Nogueira, É., &
    9. Borges-Silva, J. C.
    (2019). Could bovine livestock intensification in Pantanal be neutral regarding enteric methane emissions? Science of the Total Environment, 655, 463–472. Scopus. https://doi.org/10.1016/j.scitotenv.2018.11.178
    OpenUrl
  18. ↵
    1. Bergmann, R.,
    2. Kolodner, J., &
    3. Plaza, E.
    (2005). Representation in case-based reasoning. The Knowledge Engineering Review, 20(3), 209–213. https://doi.org/10.1017/S0269888906000555
    OpenUrl
  19. ↵
    1. Brezar, Z.
    (2022, October 26). Using artificial intelligence in your design process [Online Magazine]. Landezine. https://landezine.com/using-artificial-intelligence-in-your-design-process/
  20. ↵
    1. Bryant, M.
    (2021). Learning spatial design through interdisciplinary collaboration. Land, 10(7), 689. https://doi.org/10.3390/land10070689
    OpenUrl
  21. ↵
    1. Burkhardt, J.,
    2. Chan, N. W.,
    3. Bollinger, B., &
    4. Gillingham, K. T.
    (2022). Conformity and Conservation: Evidence from Home Landscaping and Water Conservation. American Journal of Agricultural Economics, 104(1), 228–248. Scopus. https://doi.org/10.1111/ajae.12224
    OpenUrl
  22. ↵
    1. Bzdok, D.,
    2. Krzywinski, M., &
    3. Altman, N.
    (2018). Machine learning: Supervised methods. Nature Methods, 15(1), 5.
    OpenUrl
  23. ↵
    1. Cantrell, B.,
    2. Ellis, E.,
    3. Hill, K., &
    4. Martin, L.
    (2017). Ecology on autopilot. Landscape Architecture Magazine, 107(6). http://bt.royle.com/article/Ecology+On+Autopilot/2787563/409226/article.html
  24. ↵
    1. Cantrell, B.,
    2. Martin, L. J., &
    3. Ellis, E. C.
    (2017). Designing autonomy: Opportunities for new wildness in the anthropocene. Trends in Ecology & Evolution, 32(3), 156–166. https://doi.org/10.1016/j.tree.2016.12.004
    OpenUrl
  25. ↵
    1. Cantrell, B., &
    2. Mekies, A.
    (2018). Codify: Parametric and computational design in landscape architecture. Routledge.
  26. ↵
    1. Cantrell, B.,
    2. Zhang, Z., &
    3. Liu, X.
    (2021). Artificial intelligence and machine learning in landscape architecture. In Imdat As & Prithwish Basu (Eds.), The Routledge Companion to Artificial Intelligence in Architecture (pp. 232–249). Routledge.
  27. ↵
    1. César de Lima Araújo, H.,
    2. Silva Martins, F.,
    3. Tucunduva Philippi Cortese, T., &
    4. Locosselli, G. M.
    (2021). Artificial intelligence in urban forestry—A systematic review. Urban Forestry & Urban Greening, 66, 127410. https://doi.org/10.1016/j.ufug.2021.127410
    OpenUrl
  28. ↵
    1. Chai, C.,
    2. Song, Y., &
    3. Qin, Z.
    (2021). A thousand words express a common idea? Understanding international tourists’ reviews of Mt. Huangshan, China, through a deep learning approach. Land, 10(6). Scopus. https://doi.org/10.3390/land10060549
  29. ↵
    1. Chamberlain, B. C., &
    2. Meitner, M. J.
    (2009). Automating the visual resource management and harvest design process. Landscape and Urban Planning, 90(1), 86–94. https://doi.org/10.1016/j.landurbplan.2008.10.015
    OpenUrl
  30. ↵
    1. Chandak, Y.,
    2. Theocharous, G.,
    3. Kostas, J.,
    4. Jordan, S., &
    5. Thomas, P.
    (2019). Learning action representations for reinforcement learning. Proceedings of the 36th International Conference on Machine Learning, 941–950. https://proceedings.mlr.press/v97/chandak19a.html
  31. ↵
    1. Chaturvedi, V., &
    2. de Vries, W. T.
    (2021). Machine learning algorithms for urban land use planning: A review. Urban Science, 5(3), Article 3. https://doi.org/10.3390/urbansci5030068
    OpenUrl
  32. ↵
    1. Chen, C. H.
    (2015). Handbook of pattern recognition and computer vision (5th ed.). World Scientific.
  33. ↵
    1. Cherkauer, K. A.,
    2. Bowling, L. C.,
    3. Lee, C.,
    4. Singh, N.,
    5. Smith, S., &
    6. Zhu, Y.
    (2018). A multi-scale approach to improved simulation of agroecosystem response to climate in the Midwestern United States. 2018, H34H-09.
  34. ↵
    1. Chiabai, A.,
    2. Quiroga, S.,
    3. Martinez-Juarez, P.,
    4. Higgins, S., &
    5. Taylor, T.
    (2018). The nexus between climate change, ecosystem services and human health: Towards a conceptual framework. Science of The Total Environment, 635, 1191–1204. https://doi.org/10.1016/j.scitotenv.2018.03.323
    OpenUrl
  35. ↵
    1. Chowdhary, K. R.
    (2020). Natural language processing. In K. R. Chowdhary (Ed.), Fundamentals of artificial intelligence (pp. 603–649). Springer India. https://doi.org/10.1007/978-81-322-3972-7_19
  36. ↵
    1. Dong, J.,
    2. Guo, F.,
    3. Lin, M.,
    4. Zhang, H., &
    5. Zhu, P.
    (2022). Optimization of green infrastructure networks based on potential green roof integration in a high-density urban area—A case study of Beijing, China. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2022.155307
  37. ↵
    1. Dreith, B.
    (2022, November 16). How AI software will change architecture and design [Online Magazine]. Dezeen. https://www.dezeen.com/2022/11/16/ai-design-architecture-product/
  38. ↵
    1. Emaminejad, N., &
    2. Akhavian, R.
    (2022). Trustworthy AI and robotics: Implications for the AEC industry. Automation in Construction, 139, 104298. https://doi.org/10.1016/j.autcon.2022.104298
    OpenUrl
  39. ↵
    1. Ervin, S. M.
    (2018). Turing landscapes. In B. Cantrell & A. Mekies (Eds.), Codify (1st ed., pp. 89–114). Routledge. https://doi.org/10.4324/9781315647791-9
  40. ↵
    1. Ervin, S. M.
    (2020). A brief history and tentative taxonomy of digital landscape architecture. Journal of Digital Landscape Architecture, 2020(5), 2–11. Scopus. https://doi.org/10.14627/537690001
    OpenUrl
  41. ↵
    1. Eyvindson, K.,
    2. Rasinmäki, J., &
    3. Kangas, A.
    (2018). Evaluating a hierarchical approach to landscape-level harvest scheduling. Canadian Journal of Forest Research, 48(2), 208–215. Scopus. https://doi.org/10.1139/cjfr-2017-0298
    OpenUrl
  42. ↵
    1. Faisal, A.,
    2. Kamruzzaman, M.,
    3. Yigitcanlar, T., &
    4. Currie, G.
    (2019). Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy. Journal of Transport and Land Use, 12(1), 45–72.
    OpenUrl
  43. ↵
    1. Fengjing, L.,
    2. Dong, L., &
    3. Liwei, X.
    (2022). Assessing the Green View Index in Chinese cities: An example with data from eighty cities. Journal of Digital Landscape Architecture. https://doi.org/10.14627/537724028
  44. ↵
    1. Fernberg, P., &
    2. Chamberlain, B.
    (2021, August 19). I, Designer? Landscape Architecture Magazine, 111(8). https://landscapearchitecturemagazine.org/2021/08/19/i-designer/
  45. ↵
    1. Fernberg, P.,
    2. Sturla, P., &
    3. Chamberlain, B.
    (2021). Pursuing an AI ontology for landscape architecture. Journal of Digital Landscape Architecture, 2021(6), 452–460. Scopus. https://doi.org/10.14627/537705040
    OpenUrl
  46. ↵
    1. Frondorf, A. F.,
    2. McCarthy, M. M., &
    3. Rasmussen, W. O.
    (1978). Data-intensive spatial sampling and multiple hierarchical clustering: Methodological approaches toward cost/time efficiency in natural resource assessment. Landscape Planning, 5(1), 1–25. Scopus. https://doi.org/10.1016/0304-3924(78)90013-8
    OpenUrlGeoRef
  47. ↵
    1. Gärtner, S.,
    2. Reynolds, K. M.,
    3. Hessburg, P. F.,
    4. Hummel, S., &
    5. Twery, M.
    (2008). Decision support for evaluating landscape departure and prioritizing forest management activities in a changing environment. Forest Ecology and Management, 256(10), 1666–1676. Scopus. https://doi.org/10.1016/j.foreco.2008.05.053
    OpenUrl
  48. ↵
    1. Ghermandi, A.,
    2. Depietri, Y., &
    3. Sinclair, M.
    (2022). In the AI of the beholder: A comparative analysis of computer vision-assisted characterizations of human-nature interactions in urban green spaces. Landscape and Urban Planning, 217. Scopus. https://doi.org/10.1016/j.landurbplan.2021.104261
  49. ↵
    1. Goodwin, C. E. D.,
    2. Bütikofer, L.,
    3. Hatfield, J. H.,
    4. Evans, P. M.,
    5. Bullock, J. M.,
    6. Storkey, J.,
    7. Mead, A.,
    8. Richter, G. M.,
    9. Henrys, P. A.,
    10. Pywell, R. F., &
    11. Redhead, J. W.
    (2022). Multi-tier archetypes to characterise British landscapes, farmland and farming practices. Environmental Research Letters, 17(9), 095002. https://doi.org/10.1088/1748-9326/ac810e
    OpenUrl
  50. ↵
    1. Groot, J. C. J.,
    2. Yalew, S. G., &
    3. Rossing, W. A. H.
    (2018). Exploring ecosystem services trade-offs in agricultural landscapes with a multi-objective programming approach. Landscape and Urban Planning, 172, 29–36. Scopus. https://doi.org/10.1016/j.landurbplan.2017.12.008
    OpenUrl
  51. ↵
    1. Harmon, B. A.,
    2. Nam, H. Y.,
    3. Gilbert, H., &
    4. Iravani, N.
    (2022). Living typography: Robotically printing a living typeface. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–4. https://doi.org/10.1145/3491101.3519894
  52. ↵
    1. Harrouk, C.
    (2020, December 8). Spacemaker proposes AI-powered generative design to create more sustainable spaces and cities. ArchDaily. https://www.archdaily.com/952850/spacemaker-proposes-ai-powered-generative-design-to-create-more-sustainable-spaces-and-cities
  53. ↵
    1. Hastie, T.,
    2. Tibshirani, R., &
    3. Friedman, J.
    (2009). Unsupervised learning. In T. Hastie, R. Tibshirani, & J. Friedman (Eds.), The elements of statistical learning: Data mining, inference, and prediction (pp. 485–585). Springer. https://doi.org/10.1007/978-0-387-84858-7_14
  54. ↵
    1. Hickman, M.
    (2020, October 15). Sidewalk Labs launches Delve, a generative design tool for optimized urban development. The Architect’s Newspaper. https://www.archpaper.com/2020/10/sidewalk-labs-launches-delvegenerative-design-tool-for-optimized-urban-development/
  55. ↵
    1. Hogan, S. D.,
    2. Kelly, M.,
    3. Stark, B., &
    4. Chen, Y.
    (2017). Unmanned aerial systems for agriculture and natural resources. California Agriculture, 71(1), 5–14. https://doi.org/10.3733/ca.2017a0002
    OpenUrl
  56. ↵
    1. Huang, L.
    (2021). Design of landscape ecological environment monitoring system based on improved particle swarm optimization. Fresenius Environmental Bulletin, 30(6), 6207–6214. Scopus.
    OpenUrl
  57. ↵
    1. Hummel, S., &
    2. Cunningham, P.
    (2006). Estimating variation in a landscape simulation of forest structure. Forest Ecology and Management, 228(1–3), 135–144. Scopus. https://doi.org/10.1016/j.foreco.2006.02.034
    OpenUrl
  58. ↵
    1. Hurkxkens, I.,
    2. Fahmi, F., &
    3. Mirjan, A.
    (2022). Robotic landscapes: Designing the unfinished. Park Books.
  59. ↵
    1. Hurkxkens, I.,
    2. Mirjan, A.,
    3. Gramazio, F.,
    4. Kohler, M., &
    5. Girot, C.
    (2020). Robotic landscapes: Designing formation processes for large scale autonomous earth moving. In C. Gengnagel, O. Baverel, J. Burry, M. Ramsgaard Thomsen, & S. Weinzierl (Eds.), Impact: Design with all senses (pp. 69–81). Springer International Publishing. https://doi.org/10.1007/978-3-030-29829-6_6
  60. ↵
    1. Imran, H. M.,
    2. Akib, S., &
    3. Karim, M. R.
    (2013). Permeable pavement and stormwater management systems: A review. Environmental Technology, 34(18), 2649–2656. https://doi.org/10.1080/09593330.2013.782573
    OpenUrl
  61. ↵
    1. International Map of Robots in the Creative Industry
    . (n.d.). Association for Robots in Architecture. Retrieved July 15, 2022, from https://www.robotsinarchitecture.org/map-of-creative-robots
  62. ↵
    1. Jahani, A.,
    2. Kalantary, S., &
    3. Alitavoli, A.
    (2021). An application of artificial intelligence techniques in prediction of birds soundscape impact on tourists’ mental restoration in natural urban areas. Urban Forestry and Urban Greening, 61. Scopus. https://doi.org/10.1016/j.ufug.2021.127088
  63. ↵
    1. Johns, R. L.,
    2. Wermelinger, M.,
    3. Mascaro, R.,
    4. Jud, D.,
    5. Gramazio, F.,
    6. Kohler, M.,
    7. Chli, M., &
    8. Hutter, M.
    (2020). Autonomous dry stone. Construction Robotics, 4(3), 127–140. https://doi.org/10.1007/s41693-020-00037-6
    OpenUrl
  64. ↵
    1. Kälin, U.,
    2. Lang, N.,
    3. Hug, C.,
    4. Gessler, A., &
    5. Wegner, J. D.
    (2019). Defoliation estimation of forest trees from ground-level images. Remote Sensing of Environment, 223, 143–153. https://doi.org/10.1016/j.rse.2018.12.021
    OpenUrl
  65. ↵
    1. Kampichler, C., &
    2. Sierdsema, H.
    (2018). On the usefulness of prediction intervals for local species distribution model forecasts. Ecological Informatics, 47, 67–72. Scopus. https://doi.org/10.1016/j.ecoinf.2017.07.003
    OpenUrl
  66. ↵
    1. Kaya, A.,
    2. Bettinger, P.,
    3. Boston, K.,
    4. Akbulut, R.,
    5. Ucar, Z.,
    6. Siry, J.,
    7. Merry, K., &
    8. Cieszewski, C.
    (2016). Optimisation in forest management. Current Forestry Reports, 2(1), 1–17. https://doi.org/10.1007/s40725-016-0027-y
    OpenUrl
  67. ↵
    1. Khalilnezhad, S. T.
    (2022). Using Twitter as a means of understanding the impact of distance and park size on park visiting behavior (case study: London). Journal of Digital Landscape Architecture. https://doi.org/10.14627/537724015
  68. ↵
    1. Koma, S.,
    2. Yamabe, Y., &
    3. Tani, A.
    (2017). Research on urban landscape design using the interactive genetic algorithm and 3D images. Visualization in Engineering, 5(1). Scopus. https://doi.org/10.1186/s40327-016-0039-5
  69. ↵
    1. Kotsiantis, S. B.,
    2. Zaharakis, I., &
    3. Pintelas, P.
    (2007). Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160(1), 3–24.
    OpenUrl
  70. ↵
    1. Kullmann, K.
    (2016). Disciplinary convergence: Landscape architecture and the spatial design disciplines. Journal of Landscape Architecture, 11(1), 30–41. https://doi.org/10.1080/18626033.2016.1144668
    OpenUrl
  71. ↵
    1. LABOK Task Force
    . (2004). Landscape architecture body of knowledge study report. American Society of Landscape Architects. https://www.asla.org/uploadedfiles/cms/education/accreditation/labok_report_with_appendices.pdf
  72. ↵
    1. Langley, W. N.,
    2. Corry, R. C., &
    3. Brown, R. D.
    (2018). Core knowledge domains of landscape architecture. Landscape Journal, 37(1), 9–21. https://doi.org/10.3368/lj.37.1.9
    OpenUrlAbstract/FREE Full Text
  73. ↵
    1. Leippert, F.,
    2. Darmaun, M.,
    3. Bernoux, M., &
    4. Mpheshea, M.
    (2020). The potential of agroecology to build climate-resilient livelihoods and food systems. Food and Agricultural Organization and Biovision. https://doi.org/10.4060/cb0438en
  74. ↵
    1. Li, X.,
    2. Lin, J.,
    3. Chen, Y.,
    4. Liu, X., &
    5. Ai, B.
    (2013). Calibrating cellular automata based on landscape metrics by using genetic algorithms. International Journal of Geographical Information Science, 27(3), 594–613. https://doi.org/10.1080/13658816.2012.698391
    OpenUrl
  75. ↵
    1. Lin, Y.-P.,
    2. Verburg, P. H.,
    3. Chang, C.-R.,
    4. Chen, H.-Y., &
    5. Chen, M.-H.
    (2009). Developing and comparing optimal and empirical land-use models for the development of an urbanized watershed forest in Taiwan. Landscape and Urban Planning, 92(3–4), 242–254. Scopus. https://doi.org/10.1016/j.landurbplan.2009.05.003
    OpenUrlCrossRef
  76. ↵
    1. Lindhult, M. S.
    (1988). The road beyond CAD. Landscape Architecture, 78(5), 40–45. JSTOR.
    OpenUrl
  77. ↵
    1. Liu, G.,
    2. Han, S.,
    3. Zhao, X.,
    4. Nelson, J. D.,
    5. Wang, H., &
    6. Wang, W.
    (2006). Optimisation algorithms for spatially constrained forest planning. Ecological Modelling, 194(4), 421–428. Scopus. https://doi.org/10.1016/j.ecolmodel.2005.10.028
    OpenUrl
  78. ↵
    1. Liu, W.-Y., &
    2. Lin, C.-C.
    (2015). Spatial forest resource planning using a cultural algorithm with problem-specific information. Environmental Modelling and Software, 71, 126–137. Scopus. https://doi.org/10.1016/j.envsoft.2015.06.002
    OpenUrl
  79. ↵
    1. Liu, X., &
    2. Tian, R.
    (2022). RiverGAN: Fluvial landform generation based on physical simulations and generative adversarial network. Journal of Digital Landscape Architecture. https://doi.org/10.14627/537724011
  80. ↵
    1. Lorilla, R. S.,
    2. Poirazidis, K.,
    3. Detsis, V.,
    4. Kalogirou, S., &
    5. Chalkias, C.
    (2020). Socio-ecological determinants of multiple ecosystem services on the Mediterranean landscapes of the Ionian Islands (Greece). Ecological Modelling, 422. Scopus. https://doi.org/10.1016/j.ecolmodel.2020.108994
  81. ↵
    1. Malone, T. W.,
    2. Rus, D., &
    3. Laubacher, R.
    (2020). Artificial intelligence and the future of work (p. 39) [Research Brief]. Massachusetts Institiute of Technology. https://workofthefuture.mit.edu/wp-content/uploads/2020/12/2020-Research-Brief-Malone-Rus-Laubacher2.pdf
  82. ↵
    1. Mata, J.,
    2. de Miguel, I.,
    3. Durán, R. J.,
    4. Merayo, N.,
    5. Singh, S. K.,
    6. Jukan, A., &
    7. Chamania, M.
    (2018). Artificial intelligence (AI) methods in optical networks: A comprehensive survey. Optical Switching and Networking, 28, 43–57. https://doi.org/10.1016/j.osn.2017.12.006
    OpenUrl
  83. ↵
    1. McCarthy, M. M., &
    2. Portner, J.
    (1980). The changing landscape: communication and information technologies. Landscape Architecture, 70(6), 602–611. JSTOR.
    OpenUrl
  84. ↵
    1. Miller, N.
    (2017, August 1). Machine learning with LunchBoxML. Proving ground. https://provingground.io/2017/08/01/machine-learning-with-lunchboxml/
  85. ↵
    1. Minařík, R., &
    2. Langhammer, J.
    (2016). Use of a multispectral UAV photogrammetry for detection and tracking of forest disturbance dynamics. ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41B8, 711–718. https://doi.org/10.5194/isprs-archives-XLI-B8-711-2016
    OpenUrl
  86. ↵
    1. Miranda, A.,
    2. Carrasco, J.,
    3. González, M.,
    4. Pais, C.,
    5. Lara, A.,
    6. Altamirano, A.,
    7. Weintraub, A., &
    8. Syphard, A. D.
    (2020). Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires. Environmental Research Letters, 15(9). Scopus. https://doi.org/10.1088/1748-9326/ab9be5
  87. ↵
    1. Mirjalili, S., &
    2. Dong, J. S.
    (2020). Multi-objective optimization using artificial intelligence techniques. Springer.
  88. ↵
    1. Mohanty, S. P.,
    2. Hughes, D. P., &
    3. Salathé, M.
    (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7. https://doi.org/10.3389/fpls.2016.01419
  89. ↵
    1. Monge, J. C.
    (2022, July 13). Midjourney AI is now publicly accessible—Don’t miss it. MLearning.Ai. https://medium.com/mlearning-ai/midjourney-ai-is-now-publicly-accessible-dont-miss-it-c4c6bb77c375
  90. ↵
    1. Mouchet, M. A.,
    2. Lamarque, P.,
    3. Martín-López, B.,
    4. Crouzat, E.,
    5. Gos, P.,
    6. Byczek, C., &
    7. Lavorel, S.
    (2014). An interdisciplinary methodological guide for quantifying associations between ecosystem services. Global Environmental Change, 28(1), 298–308. Scopus. https://doi.org/10.1016/j.gloenvcha.2014.07.012
    OpenUrlCrossRef
  91. ↵
    1. Naderi, J. R., &
    2. Raman, B.
    (2005). Capturing impressions of pedestrian landscapes used for healing purposes with decision tree learning. Landscape and Urban Planning, 73(2–3), 155–166. Scopus. https://doi.org/10.1016/j.landurbplan.2004.11.012
    OpenUrlCrossRef
    1. New Landscape Declaration
    . (2016). Landscape Architecture Foundation. https://www.lafoundation.org/take-action/new-landscape-declaration
  92. ↵
    1. Ngarega, B. K.,
    2. Masocha, V. F., &
    3. Schneider, H.
    (2021). Forecasting the effects of bioclimatic characteristics and climate change on the potential distribution of Colophospermum mopane in southern Africa using Maximum Entropy (Maxent). Ecological Informatics, 65. Scopus. https://doi.org/10.1016/j.ecoinf.2021.101419
  93. ↵
    1. Nitoslawski, S. A.,
    2. Galle, N. J.,
    3. Van Den Bosch, C. K., &
    4. Steenberg, J. W. N.
    (2019). Smarter ecosystems for smarter cities? A review of trends, technologies, and turning points for smart urban forestry. Sustainable Cities and Society, 51, 101770. https://doi.org/10.1016/j.scs.2019.101770
    OpenUrl
  94. ↵
    1. Novikov, A. I., &
    2. Ersson, B. T.
    (2019). Aerial seeding of forests in Russia: A selected literature analysis. IOP Conference Series: Earth and Environmental Science, 226, 012051. https://doi.org/10.1088/1755-1315/226/1/012051
    OpenUrl
  95. ↵
    1. Ogrin, D.
    (1994). Landscape architecture and its articulation into landscape planning and landscape design. Landscape and Urban Planning, 30(3), 131–137. https://doi.org/10.1016/0169-2046(94)90052-3
    OpenUrl
  96. ↵
    1. Peng, J.,
    2. Zhao, S.,
    3. Dong, J.,
    4. Liu, Y.,
    5. Meersmans, J.,
    6. Li, H., &
    7. Wu, J.
    (2019). Applying ant colony algorithm to identify ecological security patterns in megacities. Environmental Modelling and Software, 117, 214–222. Scopus. https://doi.org/10.1016/j.envsoft.2019.03.017
    OpenUrl
  97. ↵
    1. Petrich, C.
    (1986). Expert systems. Landscape Architecture, 76(3), 70–74.
    OpenUrl
  98. ↵
    1. Polk, M.
    (2015). Transdisciplinary co-production: Designing and testing a transdisciplinary research framework for societal problem solving. Futures, 65, 110–122. https://doi.org/10.1016/j.futures.2014.11.001
    OpenUrl
  99. ↵
    1. Prince, S. J. D.
    (2012). Computer vision: Models, learning, and inference. Cambridge University Press.
  100. ↵
    1. Public Health Agency Canada
    . (2020, June 4). Challenges and opportunities for public health made possible by advances in natural language processing, CCDR 46(6) [Research]. https://www.canada.ca/en/public-health/services/reports-publications/canada-communicable-disease-report-ccdr/monthly-issue/2020-46/issue-6-june-4-2020/natural-language-processing-subfield-artificial-intelligence.html
  101. ↵
    1. Queiroz, C.,
    2. Meacham, M.,
    3. Richter, K.,
    4. Norström, A. V.,
    5. Andersson, E.,
    6. Norberg, J., &
    7. Peterson, G.
    (2015). Mapping bundles of ecosystem services reveals distinct types of multi-functionality within a Swedish landscape. Ambio, 44(1), 89–101. Scopus. https://doi.org/10.1007/s13280-014-0601-0
    OpenUrl
  102. ↵
    1. Raman, T. A.,
    2. Kollar, J., &
    3. Penman, S.
    (2022). SASAKI: Filling the design gap—Urban impressions with AI. In I. As, P. Basu, & P. Talwar (Eds.), Artificial intelligence in urban planning and design (pp. 339–362). Elsevier. https://doi.org/10.1016/B978-0-12-823941-4.00002-0
  103. ↵
    1. Rutenbar, R. A.
    (1989). Simulated annealing algorithms: An overview. IEEE Circuits and Devices Magazine, 5(1), 19–26. https://doi.org/10.1109/101.17235
    OpenUrl
  104. ↵
    1. Sai, M. S.,
    2. Kumar, K., &
    3. Prakash, B.
    (2020). Design, analysis and development of a flying wing UAV for aerial seeding and 3D mapping. In BBVL. Deepak, D. Parhi, & P. C. Jena (Eds.), Innovative product design and intelligent manufacturing systems (pp. 1023–1033). Springer. https://doi.org/10.1007/978-981-15-2696-1_99
  105. ↵
    1. Schlickman, E.
    (2020). Assessing automation: Methodological insights from experimenting with computer vision for public life research. Journal of Landscape Architecture, 15(3), 48–59. https://doi.org/10.1080/18626033.2020.1886515
    OpenUrl
  106. ↵
    1. Shanthala Devi, B. S.,
    2. Murthy, M. S. R.,
    3. Debnath, B., &
    4. Jha, C. S.
    (2013). Forest patch connectivity diagnostics and prioritization using graph theory. Ecological Modelling, 251, 279–287. Scopus. https://doi.org/10.1016/j.ecolmodel.2012.12.022
    OpenUrlCrossRef
  107. ↵
    1. Shapira, A.,
    2. Shoshany, M., &
    3. Nir-Goldenberg, S.
    (2013). Combining analytical hierarchy process and agglomerative hierarchical clustering in search of expert consensus in green corridors development management. Environmental Management, 52(1), 123–135. Scopus. https://doi.org/10.1007/s00267-013-0064-2
    OpenUrl
  108. ↵
    1. Slager, C. T. J., &
    2. de Vries, B.
    (2013). Landscape generator: Method to generate landscape configurations for spatial plan-making. Computers, Environment and Urban Systems, 39, 1–11. Scopus. https://doi.org/10.1016/j.compenvurbsys.2013.01.007
    OpenUrl
  109. ↵
    1. Song, Y.,
    2. Ning, H.,
    3. Ye, X.,
    4. Chandana, D., &
    5. Wang, S.
    (2022). Analyze the usage of urban greenways through social media images and computer vision. Environment and Planning B: Urban Analytics and City Science, 239980832110646. https://doi.org/10.1177/23998083211064624
  110. ↵
    1. Song, Y.,
    2. Wang, R.,
    3. Fernandez, J., &
    4. Li, D.
    (2021). Investigating sense of place of the Las Vegas Strip using online reviews and machine learning approaches. Landscape and Urban Planning, 205. Scopus. https://doi.org/10.1016/j.landurbplan.2020.103956
  111. ↵
    1. Souza, J. T. de,
    2. Francisco, A. C. de,
    3. Piekarski, C. M., &
    4. Prado, G. F. do.
    (2019). Data mining and machine learning to promote smart cities: A systematic review from 2000 to 2018. Sustainability, 11(4), Article 4. https://doi.org/10.3390/su11041077
    OpenUrl
  112. ↵
    1. Stamou, Z.,
    2. Xystrakis, F., &
    3. Koutsias, N.
    (2016). The role of fire as a long-term landscape modifier: Evidence from long-term fire observations (1922–2000) in Greece. Applied Geography, 74, 47–55. Scopus. https://doi.org/10.1016/j.apgeog.2016.07.005
    OpenUrl
  113. ↵
    1. Suppakittpaisarn, P.,
    2. Lu, Y.,
    3. Jiang, B., &
    4. Slavenas, M.
    (2022). How do computers see landscapes? Comparisons of eye-level greenery assessments between computer and human perceptions. Landscape and Urban Planning, 227, 104547. https://doi.org/10.1016/j.landurbplan.2022.104547
    OpenUrl
  114. ↵
    1. Sutton, R. S.
    (1992). Introduction: The challenge of reinforcement learning. In R. S. Sutton (Ed.), Reinforcement learning (pp. 1–3). Springer U.S. https://doi.org/10.1007/978-1-4615-3618-5_1
  115. ↵
    1. Szeliski, R.
    (2010). Computer vision: Algorithms and applications. Springer Science & Business Media.
  116. ↵
    1. Tack, S.
    (2021, June 23). NVIDIA Canvas app launches in Beta. NVIDIA Blog. https://blogs.nvidia.com/blog/2021/06/23/studio-canvas-app/
  117. ↵
    1. Talaviya, T.,
    2. Shah, D.,
    3. Patel, N.,
    4. Yagnik, H., &
    5. Shah, M.
    (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73. https://doi.org/10.1016/j.aiia.2020.04.002
    OpenUrl
  118. ↵
    1. Tarca, A. L.,
    2. Carey, V. J.,
    3. Chen, X.,
    4. Romero, R., &
    5. Drăghici, S.
    (2007). Machine learning and its applications to biology. PLOS Computational Biology, 3(6), e116. https://doi.org/10.1371/journal.pcbi.0030116
    OpenUrl
  119. ↵
    1. Tebyanian, N.
    (2020). Application of machine learning for urban landscape design: A primer for landscape architects. https://doi.org/10.14627/537690023
  120. ↵
    1. Thomson, J.,
    2. Regan, T. J.,
    3. Hollings, T.,
    4. Amos, N.,
    5. Geary, W. L.,
    6. Parkes, D.,
    7. Hauser, C. E., &
    8. White, M.
    (2020). Spatial conservation action planning in heterogenous landscapes. Biological Conservation, 250. Scopus. https://doi.org/10.1016/j.biocon.2020.108735
  121. ↵
    1. Van Assche, K.,
    2. Beunen, R.,
    3. Duineveld, M., &
    4. de Jong, H.
    (2013). Co-evolutions of planning and design: Risks and benefits of design perspectives in planning systems. Planning Theory, 12(2), 177–198.
    OpenUrlCrossRefWeb of Science
  122. ↵
    1. van Strien, M. J., &
    2. Grêt-Regamey, A.
    (2022). Unsupervised deep learning of landscape typologies from remote sensing images and other continuous spatial data. Environmental Modelling & Software, 155(C). https://doi.org/10.1016/j.envsoft.2022.105462
  123. ↵
    1. von Haaren, C.,
    2. Warren-Kretzschmar, B.,
    3. Milos, C., &
    4. Werthmann, C.
    (2014). Opportunities for design approaches in landscape planning. Landscape and Urban Planning, 130, 159–170. https://doi.org/10.1016/j.landurbplan.2014.06.012
    OpenUrlCrossRef
  124. ↵
    1. von Wodtke, M.
    (1988). Integrating computer applications in higher education. Landscape Architecture, 78(5), 90–98. JSTOR.
    OpenUrl
  125. ↵
    1. Vovchenko, N.,
    2. Novikov, A.,
    3. Sokolov, S., &
    4. Tishchenko, E.
    (2020). A proposed technology to ensure high-precision aerial seeding of certified seeds. IOP Conference Series: Earth and Environmental Science, 595, 012066. https://doi.org/10.1088/1755-1315/595/1/012066
    OpenUrl
  126. ↵
    1. Vrontis, D.,
    2. Christofi, M.,
    3. Pereira, V.,
    4. Tarba, S.,
    5. Makrides, A., &
    6. Trichina, E.
    (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237–1266.
    OpenUrl
  127. ↵
    1. Wael, S.,
    2. Elshater, A., &
    3. Afifi, S.
    (2022). Mapping user experiences around transit stops using computer vision technology: Action priorities from Cairo. Sustainability, 14(17), Article 17. https://doi.org/10.3390/su141711008
    OpenUrl
  128. ↵
    1. Wang, X.
    (2021). Optimization design of green building landscape space environment based on VR virtual technology. Journal of Physics: Conference Series, 1852(3). Scopus. https://doi.org/10.1088/1742-6596/1852/3/032035
  129. ↵
    1. Wang, Z.,
    2. Zhu, Z.,
    3. Xu, M., &
    4. Qureshi, S.
    (2021). Fine-grained assessment of greenspace satisfaction at regional scale using content analysis of social media and machine learning. Science of the Total Environment, 776. Scopus. https://doi.org/10.1016/j.scitotenv.2021.145908
  130. ↵
    1. Wang, Z.-H.,
    2. Zhao, X.,
    3. Yang, J., &
    4. Song, J.
    (2016). Cooling and energy saving potentials of shade trees and urban lawns in a desert city. Applied Energy, 161, 437–444. Scopus. https://doi.org/10.1016/j.apenergy.2015.10.047
    OpenUrl
  131. ↵
    1. Wartmann, F. M.,
    2. Koblet, O., &
    3. Purves, R. S.
    (2021). Assessing experienced tranquillity through natural language processing and landscape ecology measures. Landscape Ecology, 36 (8), 2347–2365. Scopus. https://doi.org/10.1007/s10980-020-01181-8
    OpenUrl
  132. ↵
    1. Wasif, M.
    (2011). Design and implementation of autonomous Lawn-Mower Robot controller. 2011 7th International Conference on Emerging Technologies, 1–5. https://doi.org/10.1109/ICET.2011.6048466
  133. ↵
    1. Westort, C., &
    2. Shen, Z.
    (2017). Robot in the garden: Preliminary experiments programming an on-site robot ball assistant to the landscape architect. Journal of Digital Landscape Architecture, 2017(2), 223–234. Scopus. https://doi.org/10.14627/537629023
    OpenUrl
  134. ↵
    1. Westphal, M. I.,
    2. Field, S. A., &
    3. Possingham, H. P.
    (2007). Optimizing landscape configuration: A case study of woodland birds in the Mount Lofty Ranges, South Australia. Landscape and Urban Planning, 81(1–2), 56–66. Scopus. https://doi.org/10.1016/j.landurbplan.2006.10.015
    OpenUrlCrossRefWeb of Science
  135. ↵
    1. Wu, N., &
    2. Silva, E. A.
    (2010). Artificial intelligence solutions for urban land dynamics: A review. Journal of Planning Literature, 24(3), 246–265. https://doi.org/10.1177/0885412210361571
    OpenUrlCrossRefWeb of Science
  136. ↵
    1. Yang, B., &
    2. Xu, Y.
    (2021). Applications of deep-learning approaches in horticultural research: A review. Horticulture Research, 8, 123. https://doi.org/10.1038/s41438-021-00560-9
    OpenUrl
  137. ↵
    1. Yang, C., &
    2. Liu, T.
    (2022). Social media data in urban design and landscape research: A Comprehensive literature review. Land, 11(10), 1796. https://doi.org/10.3390/land11101796
    OpenUrl
  138. ↵
    1. Yang, J.,
    2. Fricker, P., &
    3. Jung, A.
    (2022). From intuition to reasoning: Analyzing correlative attributes of walkability in urban environments with machine learning. Journal of Digital Landscape Architecture. https://doi.org/10.14627/537724008
  139. ↵
    1. Yang, L.,
    2. Iwami, M.,
    3. Chen, Y.,
    4. Wu, M., &
    5. van Dam, K. H.
    (2022). Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. Progress in Planning, 100657. https://doi.org/10.1016/j.progress.2022.100657
  140. ↵
    1. Yigitcanlar, T.,
    2. Desouza, K. C.,
    3. Butler, L., &
    4. Roozkhosh, F.
    (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), Article 6. https://doi.org/10.3390/en13061473
    OpenUrl
  141. ↵
    1. Yue, L.,
    2. Chen, W.,
    3. Li, X.,
    4. Zuo, W., &
    5. Yin, M.
    (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2), 617–663. https://doi.org/10.1007/s10115-018-1236-4
    OpenUrl
  142. ↵
    1. Zank, B.,
    2. Bagstad, K. J.,
    3. Voigt, B., &
    4. Villa, F.
    (2016). Modeling the effects of urban expansion on natural capital stocks and ecosystem service flows: A case study in the Puget Sound, Washington, USA. Landscape and Urban Planning, 149, 31–42. Scopus. https://doi.org/10.1016/j.landurbplan.2016.01.004
    OpenUrl
  143. ↵
    1. Zeiger, M.
    (2019, February 12). Live and learn. Landscape Architecture Magazine, 109(12). https://landscapearchitecturemagazine.org/2019/02/12/live-and-learn/
  144. ↵
    1. Zema, D. A.,
    2. Lucas-Borja, M. E.,
    3. Fotia, L.,
    4. Rosaci, D.,
    5. Sarnè, G. M. L., &
    6. Zimbone, S. M.
    (2020). Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network. Computers and Electronics in Agriculture, 170. Scopus. https://doi.org/10.1016/j.compag.2020.105280
  145. ↵
    1. Zhang, L., &
    2. Liu, B.
    (2017). Sentiment analysis and opinion mining. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (pp. 1152–1161). Springer U.S. https://doi.org/10.1007/978-1-4899-7687-1_907
  146. ↵
    1. Zhang, R.,
    2. Zhao, Y.,
    3. Kong, J.,
    4. Cheng, C.,
    5. Liu, X., &
    6. Zhang, C.
    (2021). Intelligent recognition method of decorative open-work windows with sustainable application for suzhou traditional private gardens in china. Sustainability (Switzerland), 13(15). Scopus. https://doi.org/10.3390/su13158439
  147. ↵
    1. Zhang, Z.
    (2020). Cybernetic environment: A historical reflection on system, design, and machine Intelligence. 33–40. https://doi.org/10.14627/537690004
  148. ↵
    1. Zhang, Z., &
    2. Bowes, B.
    (2019). The future of artificial intelligence (AI) and machine learning (ML) in landscape design: A case study in Coastal Virginia, USA. Journal of Digital Landscape Architecture, 2–9.
  149. ↵
    1. Zhu, Y., &
    2. Yan, E.
    (2015). Dynamic subfield analysis of disciplines: An examination of the trading impact and knowledge diffusion patterns of computer science. Scientometrics, 104(1), 335–359. https://doi.org/10.1007/s11192-015-1594-6
    OpenUrl
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Artificial Intelligence in Landscape Architecture
Phillip Fernberg, Brent Chamberlain
Landscape Journal May 2023, 42 (1) 13-35; DOI: 10.3368/lj.42.1.13

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Artificial Intelligence in Landscape Architecture
Phillip Fernberg, Brent Chamberlain
Landscape Journal May 2023, 42 (1) 13-35; DOI: 10.3368/lj.42.1.13
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    • INTRODUCTION
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