Artificial Intelligence in Landscape Architecture | Landscape Journal

REFERENCES

↵
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
↵
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
↵
1. Abdullah, M. S.,
2. Kimble, C.,
3. Benest, I., &
4. Paige, R.
(2006). Knowledge-based systems: A re-evaluation. Journal of Knowledge Management.
↵
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
↵
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
↵
1. Akerkar, R., &
2. Sajja, P.
(2009). Knowledge-based systems. Jones & Bartlett Publishers.
↵
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
↵
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
↵
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
↵
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
↵
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.
↵
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
↵
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
↵
1. ASLA
. Landscape Architecture: A Solution to the Climate Crisis. (2022). asla.org. Retrieved February 2023, from https://www.asla.org/climateaction.aspx
↵
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
↵
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.
↵
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
↵
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
↵
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/
↵
1. Bryant, M.
(2021). Learning spatial design through interdisciplinary collaboration. Land, 10(7), 689. https://doi.org/10.3390/land10070689
OpenUrl
↵
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
↵
1. Bzdok, D.,
2. Krzywinski, M., &
3. Altman, N.
(2018). Machine learning: Supervised methods. Nature Methods, 15(1), 5.
OpenUrl
↵
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
↵
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
↵
1. Cantrell, B., &
2. Mekies, A.
(2018). Codify: Parametric and computational design in landscape architecture. Routledge.
↵
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.
↵
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
↵
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
↵
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
↵
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
↵
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
↵
1. Chen, C. H.
(2015). Handbook of pattern recognition and computer vision (5th ed.). World Scientific.
↵
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.
↵
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
↵
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
↵
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
↵
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/
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
1. Fernberg, P., &
2. Chamberlain, B.
(2021, August 19). I, Designer? Landscape Architecture Magazine, 111(8). https://landscapearchitecturemagazine.org/2021/08/19/i-designer/
↵
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
↵
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
OpenUrl GeoRef
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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/
↵
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
↵
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
↵
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
↵
1. Hurkxkens, I.,
2. Fahmi, F., &
3. Mirjan, A.
(2022). Robotic landscapes: Designing the unfinished. Park Books.
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
OpenUrl Abstract/FREE Full Text
↵
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
↵
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
↵
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
OpenUrl CrossRef
↵
1. Lindhult, M. S.
(1988). The road beyond CAD. Landscape Architecture, 78(5), 40–45. JSTOR.
OpenUrl
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
1. McCarthy, M. M., &
2. Portner, J.
(1980). The changing landscape: communication and information technologies. Landscape Architecture, 70(6), 602–611. JSTOR.
OpenUrl
↵
1. Miller, N.
(2017, August 1). Machine learning with LunchBoxML. Proving ground. https://provingground.io/2017/08/01/machine-learning-with-lunchboxml/
↵
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
↵
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
↵
1. Mirjalili, S., &
2. Dong, J. S.
(2020). Multi-objective optimization using artificial intelligence techniques. Springer.
↵
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
↵
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
↵
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
OpenUrl CrossRef
↵
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
OpenUrl CrossRef
1. New Landscape Declaration
. (2016). Landscape Architecture Foundation. https://www.lafoundation.org/take-action/new-landscape-declaration
↵
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
↵
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
↵
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
↵
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
↵
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
↵
1. Petrich, C.
(1986). Expert systems. Landscape Architecture, 76(3), 70–74.
OpenUrl
↵
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
↵
1. Prince, S. J. D.
(2012). Computer vision: Models, learning, and inference. Cambridge University Press.
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
OpenUrl CrossRef
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
1. Szeliski, R.
(2010). Computer vision: Algorithms and applications. Springer Science & Business Media.
↵
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/
↵
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
↵
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
↵
1. Tebyanian, N.
(2020). Application of machine learning for urban landscape design: A primer for landscape architects. https://doi.org/10.14627/537690023
↵
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
↵
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.
OpenUrl CrossRef Web of Science
↵
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
↵
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
OpenUrl CrossRef
↵
1. von Wodtke, M.
(1988). Integrating computer applications in higher education. Landscape Architecture, 78(5), 90–98. JSTOR.
OpenUrl
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
OpenUrl CrossRef Web of Science
↵
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
OpenUrl CrossRef Web of Science
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
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
↵
1. Zeiger, M.
(2019, February 12). Live and learn. Landscape Architecture Magazine, 109(12). https://landscapearchitecturemagazine.org/2019/02/12/live-and-learn/
↵
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
↵
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
↵
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
↵
1. Zhang, Z.
(2020). Cybernetic environment: A historical reflection on system, design, and machine Intelligence. 33–40. https://doi.org/10.14627/537690004
↵
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.
↵
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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