https://xlstudio.org xl studio Thu, 21 Aug 2025 16:21:17 +0000 https://xlstudio.org en https://xlstudio.org/Home-1 Sun, 14 Mar 2021 01:11:46 +0000 xl studio https://xlstudio.org/Home-1 https://xlstudio.org/Archi-Agent Thu, 21 Aug 2025 16:21:17 +0000 xl studio https://xlstudio.org/Archi-Agent Research, Tools,&nbsp;Computation Archi-Agent MCP Agent Tools for Grasshopper Workflows <img width="3200" height="1800" width_o="3200" height_o="1800" data-src="https://freight.cargo.site/t/original/i/f8aa55d2819a421dc3c403abd901594110c901e7b4b70c94c1edb9217979d8be/LA-Agent_XunLiu.jpg" data-mid="237301843" border="0" src="https://freight.cargo.site/w/1000/i/f8aa55d2819a421dc3c403abd901594110c901e7b4b70c94c1edb9217979d8be/LA-Agent_XunLiu.jpg" /> TYPE: Research YEAR: 2025 Collaborators: Runjia Tian Parametric modeling has become central to contemporary architectural and landscape architectural design, offering procedural control over complex geometries. Yet, interacting with these systems typically requires specialized technical knowledge and manual manipulation of component networks, which can limit accessibility and slow down early-stage ideation. Our project introduces an MCP–Grasshopper integration that allows LLMs to act as intelligent design agents, transforming natural language instructions into structured parametric modeling operations. Designers can articulate spatial and conceptual goals in conversational form, with the AI interpreting, constructing, and modifying parametric definitions in real time. Students worked on the prototyping phase in Digital FUTURES 2025 summer workshop:http://digitalfutures.world/Data/List/ws2025 <img width="8000" height="4500" width_o="8000" height_o="4500" data-src="https://freight.cargo.site/t/original/i/746a7c39a5120e74962eb2659606c97a22af304a98cc53cc87c2122b6d88ed9a/MCP-Concept-04.jpg" data-mid="237301818" border="0" data-scale="79" src="https://freight.cargo.site/w/1000/i/746a7c39a5120e74962eb2659606c97a22af304a98cc53cc87c2122b6d88ed9a/MCP-Concept-04.jpg" /> Scope <img width="2924" height="3362" width_o="2924" height_o="3362" data-src="https://freight.cargo.site/t/original/i/de17ca57920ae48e6d0bd2f7a2e56db9cd33310a84f55ea293740aab31af4619/Workflow.png" data-mid="237301808" border="0" data-scale="86" src="https://freight.cargo.site/w/1000/i/de17ca57920ae48e6d0bd2f7a2e56db9cd33310a84f55ea293740aab31af4619/Workflow.png" /> Methodology Framework <img width="960" height="720" width_o="960" height_o="720" data-src="https://freight.cargo.site/t/original/i/6c6cf1f699ed9c9c644ef3031ac81902c45cd4d6df03ba27f218b9ba1950fca9/Presentation2.gif" data-mid="237569126" border="0" src="https://freight.cargo.site/w/960/i/6c6cf1f699ed9c9c644ef3031ac81902c45cd4d6df03ba27f218b9ba1950fca9/Presentation2.gif" />WIP Demo (more details comming soon) https://xlstudio.org/AI-powered-Planting-Design Mon, 10 Jun 2024 23:36:45 +0000 xl studio https://xlstudio.org/AI-powered-Planting-Design Research, Tools,&nbsp;Computation, Landscape Reinventing Planting Design A Generative AI Approach <img width="631px;" height="146px;" src="https://lh7-us.googleusercontent.com/slidesz/AGV_vUf1HlIpAp_LiieWQvo2VUIhrE3iD1Szg4PcKKAbar8OynB8htTaQAjQqhBT20ir5zAk1G3xkf0dNcvSt6iW6kfcPZAgxSaqs0qftH9sxOEx8wZsxOZ_WAt_MFH4HxQEunR2_gyGdFZhwLK9XIOAW41Bbamrado=s2048?key=fQF5LIQzIlGHUCmynfrOKg" style="width: 631px; height: 146px;"> TYPE: Research YEAR: 2023 Collaborators: Nandi Yang (SWA) Funding: Supported by Patrick T. Curran Fellowship In the field of landscape architecture, the integration of ecological principles with aesthetic design is a critical yet complex task. This research introduces an innovative AI-driven tool designed to revolutionize planting design by embedding ecological intelligence into the design process. This paper presents the methodology, including data synthesis, AI model training with generative adversarial networks (GANs), and integration into a practical design interface. The model is trained on multifaceted input data such as topography, soil, and climate data. The results indicate the AI model's effectiveness in generating planting layouts that balance ecological accuracy with aesthetic appeal. Significantly, this research extends the professional practice of landscape architecture. Full article can be found:&nbsp; https://gispoint.de/fileadmin/user_upload/paper_gis_open/DLA_2024/537752020.pdf <img width="1172" height="664" width_o="1172" height_o="664" data-src="https://freight.cargo.site/t/original/i/4bb4a156b541249c17ccec467b3f9dcc890088be549bb7ae3aceba83029a6171/Fig_1.png" data-mid="212655865" border="0" src="https://freight.cargo.site/w/1000/i/4bb4a156b541249c17ccec467b3f9dcc890088be549bb7ae3aceba83029a6171/Fig_1.png" /> Methodology Framework <img width="936" height="498" width_o="936" height_o="498" data-src="https://freight.cargo.site/t/original/i/60deef8861a7fc3b1a624b200382f87bb2746cb19804547eb641645c73debc94/Fig_4.png" data-mid="212657891" border="0" src="https://freight.cargo.site/w/936/i/60deef8861a7fc3b1a624b200382f87bb2746cb19804547eb641645c73debc94/Fig_4.png" /> Rhino/Grasshopper Interface. We prototyped our Interface called ECO GEN using HumanUI in Grasshopper <img width="2331" height="1509" width_o="2331" height_o="1509" data-src="https://freight.cargo.site/t/original/i/aa9dd410d44a9fc41f1bb2f93dac39ecbb90598346c0a90626074925e30d460a/Fig_3.jpg" data-mid="212655872" border="0" src="https://freight.cargo.site/w/1000/i/aa9dd410d44a9fc41f1bb2f93dac39ecbb90598346c0a90626074925e30d460a/Fig_3.jpg" /> Form finding, generative design integration <img width="960px;" height="579px;" src="https://lh7-us.googleusercontent.com/slidesz/AGV_vUcJkJ3FubFKh-D625Fat5Xzi7SrrtJXU6eyOkbBAHyadK-bDZ9FQe-hbrVHhNf-4FrPc0U4-tmlVR3Kyb9wHQUVwbxC9a6vjJWdDJUxwRmBUCTJVMeakIrqb-k5BIbp9Bo6HawV92f1TrShfHxEcs-Mv08E4dc-=s2048?key=fQF5LIQzIlGHUCmynfrOKg" style="width: 960px; height: 579px;"> <img width="458px;" height="458px;" src="https://lh7-us.googleusercontent.com/slidesz/AGV_vUc3z8e2UaueKlyIZsA8BFqkLi1Xzlv-xVAZZYNAifdOWFYCdijwhYJ5_F5LG1bJJD-8f7BxpIXp4nN_na8m3EyBV3YEonVcnguWAxPZMq7TWGdNlgAljfbnUTYC8R04GbFmCcCC81UDhe7mA6Y9bd-NtB3BxxMg=s2048?key=fQF5LIQzIlGHUCmynfrOKg" style="width: 458px; height: 458px;" data-scale="100"> <img width="2797" height="1951" width_o="2797" height_o="1951" data-src="https://freight.cargo.site/t/original/i/fa064937c21c3a0370c7b1470d663267387b0981f4a404e0748bf26363a8409b/Fig-6.png" data-mid="212655874" border="0" src="https://freight.cargo.site/w/1000/i/fa064937c21c3a0370c7b1470d663267387b0981f4a404e0748bf26363a8409b/Fig-6.png" /> Iterative Design Process and Final Plating Layout Design Visualization https://xlstudio.org/AI-for-Landscape-Representation Wed, 12 Jun 2024 16:54:42 +0000 xl studio https://xlstudio.org/AI-for-Landscape-Representation Research, Tools,&nbsp;Computation, Landscape AI for Landscape Representation <img width="2000" height="2000" width_o="2000" height_o="2000" data-src="https://freight.cargo.site/t/original/i/4d9f745987af48b9621054295d0fa5d75bab4e20ac368181f6d426a10cb35fab/fluvial_5.png" data-mid="212792293" border="0" src="https://freight.cargo.site/w/1000/i/4d9f745987af48b9621054295d0fa5d75bab4e20ac368181f6d426a10cb35fab/fluvial_5.png" /> TYPE: Research YEAR: 2024 WIP tutorials can be found:&nbsp;landscapearchitecture.ai https://xlstudio.org/The-Third-Simulation Sun, 14 Mar 2021 01:12:03 +0000 xl studio https://xlstudio.org/The-Third-Simulation Research, Tools,&nbsp;Computation, Landscape The Third Simulation Augmented Reality Fluvial Modeling Tool <img width="1600" height="736" width_o="1600" height_o="736" data-src="https://freight.cargo.site/t/original/i/3b087d358792742921d383cbfb50e8bd20691205b5549689a05970938da99e01/modifying.png" data-mid="101934479" border="0" src="https://freight.cargo.site/w/1000/i/3b087d358792742921d383cbfb50e8bd20691205b5549689a05970938da99e01/modifying.png" /> TYPE: Research YEAR: 2019 In recent design discourse in landscape architecture, ecological and environmental concerns have stimulated a shift from rigid formal practice to design of dynamic systems that are constantly in fluctuation. The digital revolution has provided landscape architects intelligent tools to challenge the determinacy of traditional static simulation and modeling method. However, it has also distanced physical material exploration from the standard protocols of the discipline. This research presents the use of small tangible hydromorphology table as new workflows and methods of design for landscape architectural interventions within hydrological systems. By integrating physical hydraulic simulation model with real-time computational fluvial simulation through augmented reality technologies, the new method enables landscape architects to design intuitively with tangible material process, while being informed by computational simulation results simultaneously. https://xlstudio.org/FluX Sun, 14 Mar 2021 01:11:54 +0000 xl studio https://xlstudio.org/FluX Research, Tools, Computation, Landscape FluX A Hydrodynamics Visualization and Modeling Tool <img width="1500" height="500" width_o="1500" height_o="500" data-src="https://freight.cargo.site/t/original/i/8849be70366fc553ea5d90c9888d35dace42f5d4a6f0e784bd72603a7cdc4aa2/logo.jpg" data-mid="101934411" border="0" src="https://freight.cargo.site/w/1000/i/8849be70366fc553ea5d90c9888d35dace42f5d4a6f0e784bd72603a7cdc4aa2/logo.jpg" /> TYPE: Research YEAR: 2016INFO: Developed in course GSD 6349 Mapping II : GeosimulationINSTRUCTOR: Robert Gerard Pietrusko PUBLICATION: Landscape Architecture Magazine Though there are lots of off-the-shelf tools available for fluvial modeling, none of the tools are developed specific for quick prototyping landforms. As for design purpose, we prefer quick prototyping process and direct visualization to high precision of every simulation numbers. Moreover, in order to fully integrate the numerical model into the physical model, it is necessary to customize our own tool, to ensure real-time feedback and adjustable parameters. FluX is a computational tool designed for landscape designer to visualize the fluid dynamics and also interactively model the flow in an existing site or their designed landform. It’s an interactive prototyping tool, that designers can select their intervention types based on the hydrological performance and directly draw their interventions and get the real-time feedback. It’s a geo-based modeling tool, that landscape designers can get the real-world dataset from GIS and weather data from website and designed based on real world data. It’s also a hydrodynamics-based evaluation tool. Based on fluid simulation, other agents like sedimentation and vegetation properties can be evaluated.For example, landscape designers can use it to make planting decisions. They can develop planting strategies based on the flow, by selecting the plants species, properties, where to seed them and how many to put. The seed propagating path is based on flow and succession of different species is based on their own properties and competition with each other. More information:https://github.com/xunliuDesign/FluXhttps://vimeo.com/212527838 <img width="1642" height="1822" width_o="1642" height_o="1822" data-src="https://freight.cargo.site/t/original/i/04b064cd264368906a66d243d807f4a41ea58fdc84c8d4a82554968064d122ee/2018126.jpg" data-mid="101953093" border="0" data-scale="77" src="https://freight.cargo.site/w/1000/i/04b064cd264368906a66d243d807f4a41ea58fdc84c8d4a82554968064d122ee/2018126.jpg" /> <img width="2400" height="3000" width_o="2400" height_o="3000" data-src="https://freight.cargo.site/t/original/i/6a9fe1c361e1262738a568cf9cbccbfcf2cf31992aaa08a4e478a69f9c24aebd/test_column_Single_sm.jpg" data-mid="101941986" border="0" src="https://freight.cargo.site/w/1000/i/6a9fe1c361e1262738a568cf9cbccbfcf2cf31992aaa08a4e478a69f9c24aebd/test_column_Single_sm.jpg" /> <img width="2400" height="2400" width_o="2400" height_o="2400" data-src="https://freight.cargo.site/t/original/i/3b4d97e4ebc303806218af653a2b80779a5fbc1635a4637c863e0ef999ab15af/test_column_Group_sm.jpg" data-mid="101942013" border="0" src="https://freight.cargo.site/w/1000/i/3b4d97e4ebc303806218af653a2b80779a5fbc1635a4637c863e0ef999ab15af/test_column_Group_sm.jpg" /> https://xlstudio.org/Code2Code Thu, 13 May 2021 23:38:53 +0000 xl studio https://xlstudio.org/Code2Code Computation, Tools, Research Code2Code NYC Digital Zoning Automation and Analysis <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/2504a931f0970219791dc4ccaed58c31c56a6f9579a52f61a41246fd5d562013/Code2Code.gif" data-mid="108404624" border="0" src="https://freight.cargo.site/w/1000/i/2504a931f0970219791dc4ccaed58c31c56a6f9579a52f61a41246fd5d562013/Code2Code.gif" /> TYPE: Professional YEAR: 2018-2019 The practice of city-wide zoning defines potentials of both building forms and public space, through a set of static prototypes (ie. zoning districts, lot types) and rules (ie, setbacks, incentives). “Code2Code” is a digital toolkit under development for New York City Department of City Planning, including automating 3D scenario of existing/proposed zoning code, enhancing the workflow of rezoning studies, and performancebased planning research.&nbsp;It translates zoning regulations into computational languages and automatically generates building massing based on GIS data. The tool has saved 90% time and cost per zoning study, resulting in cost savings in the hundreds of thousands of dollars in the agency. The new tools has significantly speed-up the traditional GIS-Modeling-Calculating- Remodeling process, thus the leap from quantitative to qualitative change enable more relational and integrated studies of the city. <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/e08f0d40b0bfb4336010dc86232c27a4287a7ea53f1bb4292b56feb5a1c45f2a/Code2Code_Concept.gif" data-mid="237567153" border="0" src="https://freight.cargo.site/w/1000/i/e08f0d40b0bfb4336010dc86232c27a4287a7ea53f1bb4292b56feb5a1c45f2a/Code2Code_Concept.gif" />Concept of “Code2Code” <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/c72fcba910095eb69942562abc8f6067f71eba1b21b3e8c9f8f6281a9f83ed0e/Code2Code_Workflow.gif" data-mid="237567156" border="0" src="https://freight.cargo.site/w/1000/i/c72fcba910095eb69942562abc8f6067f71eba1b21b3e8c9f8f6281a9f83ed0e/Code2Code_Workflow.gif" />Iterative Zoning-GIS-Modeling-Calcuating-Remodeling Process <img width="1228" height="1467" width_o="1228" height_o="1467" data-src="https://freight.cargo.site/t/original/i/0177741d99760e46f4d785cac45a44411d2bde2a3a3e365400419447bf670b4b/Screenshot-2025-08-27-193244.png" data-mid="237565552" border="0" src="https://freight.cargo.site/w/1000/i/0177741d99760e46f4d785cac45a44411d2bde2a3a3e365400419447bf670b4b/Screenshot-2025-08-27-193244.png" /> Interface of the Tool <img width="1960" height="1051" width_o="1960" height_o="1051" data-src="https://freight.cargo.site/t/original/i/2f5b829630382099af6af3ccb487c6c8650d797c115cb269ad88c48ded1a2613/Code2Code_Interface.png" data-mid="237567181" border="0" src="https://freight.cargo.site/w/1000/i/2f5b829630382099af6af3ccb487c6c8650d797c115cb269ad88c48ded1a2613/Code2Code_Interface.png" /> User Interface <img width="1465" height="1358" width_o="1465" height_o="1358" data-src="https://freight.cargo.site/t/original/i/b30a02fbafc7f7f9b7ad903416ff235dae89aead5b6f4ebb11ee405f4f4d08b2/Code2Code_GH-Screenshot.png" data-mid="237567158" border="0" data-scale="61" src="https://freight.cargo.site/w/1000/i/b30a02fbafc7f7f9b7ad903416ff235dae89aead5b6f4ebb11ee405f4f4d08b2/Code2Code_GH-Screenshot.png" /> Behind the scene <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/a71e61f8be290ba85a23b698481eaca39fa7068a80b1b762a843df1d4edba40c/Code2Code_demo1.gif" data-mid="237567192" border="0" src="https://freight.cargo.site/w/1000/i/a71e61f8be290ba85a23b698481eaca39fa7068a80b1b762a843df1d4edba40c/Code2Code_demo1.gif" /> Planners are able to connect the model to multiple GIS data source, this linkage allows the agency to “read” underlying GIS data through a 3D-model and to use that data to produce new models. Any modification can also be exported out to GIS. <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/fb3b23b1f126fcc7fe85783599c73f1bb37829f9de72f7d8278ee493a824a7e2/Code2Code_demo2.gif" data-mid="237567208" border="0" src="https://freight.cargo.site/w/1000/i/fb3b23b1f126fcc7fe85783599c73f1bb37829f9de72f7d8278ee493a824a7e2/Code2Code_demo2.gif" /> Instead of manually adjust all imbedded parameters, the planners can directly type in any proposed zoning district, to visualize different scenarios. A typical use is to visualize the increasement of massing when a district is upzoninged from a lower density district to a higher density zoning district. If the regulations for certain zoning district need to be adjusted, the tool also allows more subtle modifications of most of the detailed parameters, such as different FARmaximum building height limit, the slope of sky exposure plan, the combination of different use, etc.<img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/2b47d18ca390f3efc961d3ae8f9d92dfd459790ece01b08fc0c02bfb0944a1c8/Code2Code_demo3.gif" data-mid="237567211" border="0" src="https://freight.cargo.site/w/1000/i/2b47d18ca390f3efc961d3ae8f9d92dfd459790ece01b08fc0c02bfb0944a1c8/Code2Code_demo3.gif" /> This tool still allows designers to adjust massings manually, based on specific conditions and parameters that may be site specific or cannot be generated automatically. The designers can detect changes in the calculations simultaneously, so the manually modeling process is also more accurate and faster. <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/2504a931f0970219791dc4ccaed58c31c56a6f9579a52f61a41246fd5d562013/Code2Code.gif" data-mid="108404624" border="0" src="https://freight.cargo.site/w/1000/i/2504a931f0970219791dc4ccaed58c31c56a6f9579a52f61a41246fd5d562013/Code2Code.gif" /> It allows automation of generating buildings on multiple sites, which will significantly increase the efficiency, and allows city-wide analysis. In this project, for example, including manually adjusting, more than 200 sites are studied by 3 designers within two days, which would usually take more than one month for a team to do the modeling and analysis. <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/e4fcc9d61871ce90c0c995a21602aa96a1b31499a0b12b90945e39da7a60a988/Code2Code_demo5.gif" data-mid="237567215" border="0" src="https://freight.cargo.site/w/1000/i/e4fcc9d61871ce90c0c995a21602aa96a1b31499a0b12b90945e39da7a60a988/Code2Code_demo5.gif" /> Lastly, once a site is massed, The parametric tools automate the extraction of data from a model into an excel table and allow for designers to track the data of an individual massing in real time. This not only reduces time, but also effectively eliminates error in the data extraction process, since all of the data can be exported automatically rather than input by hand incrementally.&nbsp; Some demos of earlier versions: <img width="1280" height="720" width_o="1280" height_o="720" data-src="https://freight.cargo.site/t/original/i/76879863a1455ab8e2bf0422e842f3bf953671f079cc1799582fd24f9b00fe3d/01.gif" data-mid="237567233" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/76879863a1455ab8e2bf0422e842f3bf953671f079cc1799582fd24f9b00fe3d/01.gif" /><img width="1280" height="720" width_o="1280" height_o="720" data-src="https://freight.cargo.site/t/original/i/168cae672f00facef300911cd62e0ab31fc9e5dd6b4975a2316d06b4c770159e/02.gif" data-mid="237567303" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/168cae672f00facef300911cd62e0ab31fc9e5dd6b4975a2316d06b4c770159e/02.gif" /><img width="1280" height="720" width_o="1280" height_o="720" data-src="https://freight.cargo.site/t/original/i/24b5fc626bcba8cec2c9caeaa2e0fcd992c6526cc37ad2cb8528860a7fc2548f/03.gif" data-mid="237567420" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/24b5fc626bcba8cec2c9caeaa2e0fcd992c6526cc37ad2cb8528860a7fc2548f/03.gif" /><img width="1280" height="720" width_o="1280" height_o="720" data-src="https://freight.cargo.site/t/original/i/bdee7469e231f1f89be64b9edb58c217c313952b7343ced395028674a5fabca8/04.gif" data-mid="237567477" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/bdee7469e231f1f89be64b9edb58c217c313952b7343ced395028674a5fabca8/04.gif" /><img width="1280" height="720" width_o="1280" height_o="720" data-src="https://freight.cargo.site/t/original/i/f96e590f0f1c67c8b222be7ef26873f351bc2eb13ed9a59f7f915d0942e9b19f/05.gif" data-mid="237567625" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/f96e590f0f1c67c8b222be7ef26873f351bc2eb13ed9a59f7f915d0942e9b19f/05.gif" /><img width="1280" height="720" width_o="1280" height_o="720" data-src="https://freight.cargo.site/t/original/i/cf71a874803a860d0d13552f39236729ebde6cba84c26f059754fa8aa673178b/06.gif" data-mid="237567696" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/cf71a874803a860d0d13552f39236729ebde6cba84c26f059754fa8aa673178b/06.gif" /> https://xlstudio.org/FoodieXpress Thu, 13 May 2021 22:19:33 +0000 xl studio https://xlstudio.org/FoodieXpress Data Visualization FoodieXpress An AI-based Dashboard, Demand Prediction Platform, and Optimization Scenario Tool <img width="1854" height="932" width_o="1854" height_o="932" data-src="https://freight.cargo.site/t/original/i/4a597184903227a1374638eadb424bb318d31b3aea181f20dfd21e43faeae03b/FoodieXpress.gif" data-mid="108401951" border="0" src="https://freight.cargo.site/w/1000/i/4a597184903227a1374638eadb424bb318d31b3aea181f20dfd21e43faeae03b/FoodieXpress.gif" /> TYPE: Competition YEAR: 2020 COLLABORATOR: Waishan Qiu ROLE: Data Visualization, Mapping AWARD: Hack.asia Grand Prize FoodieXpress prototyped an AI-Enabled Business Intelligence Platform to augment the Pizza Hut management team’s capacity to adapt to the changing behavior of customers, namely the increasing demand for food delivery. It is A Dashboard, A Demand Prediction Device, and An Optimization Tool. More info: https://www.youtube.com/watch?v=ZevAFaGahlQ Visit this website for more details https://xlstudio.org/The-Endless-Liquid-Surfaces Sun, 14 Mar 2021 05:45:02 +0000 xl studio https://xlstudio.org/The-Endless-Liquid-Surfaces Landscape,&nbsp;Studio, GSD The Endless Liquid Surfaces Wild Future for Houston bayous <img width="2397" height="1199" width_o="2397" height_o="1199" data-src="https://freight.cargo.site/t/original/i/23c2195c1cc76fb65f79f9347610d3dedf4e797f4590623846ccf6e95134f7f6/Lerup_2.jpg" data-mid="101948296" border="0" src="https://freight.cargo.site/w/1000/i/23c2195c1cc76fb65f79f9347610d3dedf4e797f4590623846ccf6e95134f7f6/Lerup_2.jpg" /><img width="0" height="0" width_o="0" height_o="0" data-src="https://freight.cargo.site/t/original/i/6e36937f99849b54597bc93af1f48acd013746ef4fcbe66d3a9232bc16f4e7a9/Lerup_2.jpg" data-mid="101948297" border="0" src="https://freight.cargo.site/w/0/i/6e36937f99849b54597bc93af1f48acd013746ef4fcbe66d3a9232bc16f4e7a9/Lerup_2.jpg" /> TYPE:&nbsp; GSD Landscape Studio, Distinction YEAR: 2016 TEAM: Xun Liu, Ziwei ZhangINSTRUCTOR: Chris Reed PUBLICATION: GSD Platform 10, GSD website Lars Lerup describes Houston as “ The commingling of nature and machines, be there houses cars, or skyscrapers, set on a prairie,on this crudely gardened version thereof, results in a Houston that is fully neither nor tree.” Here we find the two dominantmachines are the highway system and the Bayou system. The natural flow runs in-between these two systems. But actually neither of these two systems are organically related to the flow. Firstly, Bayou fails to mitigate the flood situation, what’s morethe maintenance of the river bank is quite high. Secondly, From the map we can see that there are amount of the intersections between Bayou and highway, a great number of pier structuresare pinned down to the bayou directly. Actually the bridge structure in the flow is a huge engineering problem, because the flow will cause the bridge scour, which is hazardous to the stability of bridge. Houston is proposing to expanding the highway systems, which meaning more bridge crossing along the bayou. So we rethink Lerup’s drawing where he depicts two oceanic. <img width="7200" height="3600" width_o="7200" height_o="3600" data-src="https://freight.cargo.site/t/original/i/938236e197a3d93cef17f9c6e93467597287cc9035b7acdae1717453ab7ba4ce/photoes.jpg" data-mid="101948739" border="0" src="https://freight.cargo.site/w/1000/i/938236e197a3d93cef17f9c6e93467597287cc9035b7acdae1717453ab7ba4ce/photoes.jpg" /><img width="3600" height="3600" width_o="3600" height_o="3600" data-src="https://freight.cargo.site/t/original/i/7330e7f13fa3bcc0f61ef07b30abcee35e89f77afc6d4903dee4abdabb694b13/phasing.gif" data-mid="101948299" border="0" src="https://freight.cargo.site/w/1000/i/7330e7f13fa3bcc0f61ef07b30abcee35e89f77afc6d4903dee4abdabb694b13/phasing.gif" /> <img width="1200" height="600" width_o="1200" height_o="600" data-src="https://freight.cargo.site/t/original/i/4a41f0bfbfe7f612f077c866bc1cb733127dc5d664fa62614e465fe9482340d1/phasing.gif" data-mid="101948709" border="0" data-scale="77" src="https://freight.cargo.site/w/1000/i/4a41f0bfbfe7f612f077c866bc1cb733127dc5d664fa62614e465fe9482340d1/phasing.gif" /> https://xlstudio.org/Imaging-Landscapes Thu, 13 May 2021 23:23:58 +0000 xl studio https://xlstudio.org/Imaging-Landscapes Teaching, Computation Imaging Landscapes Digital FUTURES workshop: Computer Vision and Landscape Perception <img width="2500" height="1406" width_o="2500" height_o="1406" data-src="https://freight.cargo.site/t/original/i/717a34b086a691657bda8fa2994de5f7b984c53c434fe2eb8664292c93711b73/image47.jpg" data-mid="108402475" border="0" src="https://freight.cargo.site/w/1000/i/717a34b086a691657bda8fa2994de5f7b984c53c434fe2eb8664292c93711b73/image47.jpg" /> YEAR: 2020 TYPE: Teaching Design Workshop @ Digital FUTURES @Tongji University Co-instructed with Prof. Brad Cantrell &amp; Waishan Qiu The research on the visual impact of the human perceived landscape is limited by the lack of quantitative methods on landscape perceptions or difficulty in dealing with large amounts of image data. This one-week workshop applied computer vision and machine learning to develop an effective approach to quantify the subjective perception of landscapes and to apply to regional-scale studies. Taking Berlin as a case study, it examined the spatial formation, landscape elements composition, and landscape perception through data mining and data visualization of urban image data. The goal was to challenge the conventional perceptual study in the landscape design process. We hope this workshop could inspire more possibilities for novel quantitative analysis and an evidence-based design approach. This one-week workshop included a four-day session with theoretical lectures and technical workshops, handing the students basic knowledge of google street view API, SVI downloading, data preprocessing, Computer Vision( pspNet, Mask RCNN), and basic Machine Learning. Then a three-day working session allowed students to develop their own projects in sub-groups. The students included undergrads, graduate students, Ph.D. students, and lecturers from different backgrounds in landscape architecture, architecture, and urban planning from China and the U.S. More Info:&nbsp;https://www.bilibili.com/video/BV1H5411Y74g