"}],"short_description":"UC Louvain analysts used ArcGIS CityEngine for a Python-automated visibility analyses to determine suitable locations for urban wind turbines.","flexible_content":[{"acf_fc_layout":"image","image":{"ID":2701332,"id":2701332,"title":"Fig #1: CityEngine Viewshed Python automation result on imported multipatch model","filename":"Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319.png","filesize":806459,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig1-cityengine-viewshed-python-automation-result_noedges","alt":"Fig #1: CityEngine Viewshed Python automation result on imported multipatch","author":"360672","description":"","caption":"Fig #1: CityEngine Viewshed Python automation result on imported multipatch model","name":"fig1-cityengine-viewshed-python-automation-result_noedges","status":"inherit","uploaded_to":2684532,"date":"2025-02-25 11:52:45","modified":"2025-02-25 13:45:21","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1724,"height":856,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319-464x230.png","medium-width":464,"medium-height":230,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319-768x381.png","medium_large-width":768,"medium_large-height":381,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319.png","large-width":1724,"large-height":856,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319-1536x763.png","1536x1536-width":1536,"1536x1536-height":763,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319.png","2048x2048-width":1724,"2048x2048-height":856,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319-826x410.png","card_image-width":826,"card_image-height":410,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig1-CityEngine-Viewshed-Python-Automation-Result_NoEdges-e1740491093319.png","wide_image-width":1724,"wide_image-height":856}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"As cities grow, so does the need for sustainable energy. Did you know that there are compact wind turbines that can silently operate on rooftops providing power back to the energy grid while blending into the urban skyline? \u00a0These small but mighty turbines can help offset energy waste while generating clean energy, however, finding the right locations to install them can be tricky.<\/p>\n
Dr. Ioannis Tsionas<\/a> (co-author of this blog and Senior Associate Researcher at LAB, UC Louvain) has provided the following project details to share how it can be done using Python in ArcGIS CityEngine.<\/p>\n The UC Louvain research institute for Landscape, Architecture, and the Built Environment (LAB)<\/a> \u00a0joined the Wind Energy for Brussels (WEB)<\/a> project from 2022 to 2024. The project explored the feasibility of installing Small Urban Wind Turbines (SUWT)<\/strong> in the Northern Quarter Central Business District of Brussels. This area features high-rise buildings ideal for catching strong winds while having space to support turbine installation and maintenance.<\/p>\n Recognizing this potential, UC Louvain analysts partnered with engineers from BuildWind<\/a> to develop a data-driven approach. With co-financing from Innoviris<\/a> (a public organization that supports research and innovation in Brussels) all stakeholders committed to making a meaningful and sustainable impact in the city\u2019s downtown core.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2686492,"id":2686492,"title":"Fig #2A\/B: Study Area Northern Quarter in Brussels Belgium","filename":"Fig2.png","filesize":9954382,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig2-18","alt":"Fig #2A\/B: Study Area Northern Quarter in Brussels Belgium","author":"360672","description":"","caption":"Fig #2A\/B: Study Area Northern Quarter in Brussels Belgium","name":"fig2-18","status":"inherit","uploaded_to":2684532,"date":"2025-02-18 19:36:47","modified":"2025-02-19 12:24:11","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":3592,"height":1632,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2.png","medium-width":464,"medium-height":211,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2.png","medium_large-width":768,"medium_large-height":349,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2.png","large-width":1920,"large-height":872,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2-1536x698.png","1536x1536-width":1536,"1536x1536-height":698,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2-2048x930.png","2048x2048-width":2048,"2048x2048-height":930,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2-826x375.png","card_image-width":826,"card_image-height":375,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig2-1920x872.png","wide_image-width":1920,"wide_image-height":872}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Integrating SUWTs in urban areas offers many advantages. They generate clean energy that is consumed locally which, reduces dependence on grid infrastructure and they support city resilience in case of power shortages. As technology advances, the systems have also gotten smaller, quieter, and easier to connect to the grid, improving cost-effectiveness and accessibility.<\/p>\n However, before installation, SUWTs must be justified based on net energy gain including grey energy impacts. This means that they need to consume less energy and greenhouse gases from assembly and manufacturing to decommissioning (cradle to grave) than the energy they replace.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2684542,"id":2684542,"title":"Fig #3: SUWT Model for Installation","filename":"fig7_draft.png","filesize":75218,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig7_draft","alt":"Fig #3: SUWT Model for Installation","author":"360672","description":"","caption":"Fig #3: SUWT Model for Installation","name":"fig7_draft","status":"inherit","uploaded_to":2684532,"date":"2025-02-17 15:45:06","modified":"2025-02-18 19:39:29","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":865,"height":520,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft.png","medium-width":434,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft.png","medium_large-width":768,"medium_large-height":462,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft.png","large-width":865,"large-height":520,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft.png","1536x1536-width":865,"1536x1536-height":520,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft.png","2048x2048-width":865,"2048x2048-height":520,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft-774x465.png","card_image-width":774,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/fig7_draft.png","wide_image-width":865,"wide_image-height":520}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" SUWTs have a lifespan of 15 to 25 years. To evaluate energy potential, WEB project analysts first assessed wind resource availability and power production efficiency. They built simulations with Computational Fluid Dynamics (CFD) enhanced with machine learning to provide cost-effective wind resource estimations. Meanwhile, a hybrid Life Cycle Analysis (LCA) examined energy requirements for manufacturing, transport, installation, maintenance, and decommissioning. This approach determined the return on investment in energy and emissions for each proposed candidate location.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2686512,"id":2686512,"title":"Fig #4: Harvested energy estimation and proposed SUWT locations spaced across flat rooftop","filename":"Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","filesize":97784,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-4-harvested-energy-estimation-and-proposed-suwt-locations-spaced-across-flat-rooftop","alt":"Fig #4: Harvested energy estimation and proposed SUWT locations spaced across flat rooftop","author":"360672","description":"","caption":"Fig #4: Harvested energy estimation and proposed SUWT locations spaced across flat rooftop","name":"fig-4-harvested-energy-estimation-and-proposed-suwt-locations-spaced-across-flat-rooftop","status":"inherit","uploaded_to":2684532,"date":"2025-02-18 19:42:40","modified":"2025-02-18 19:43:12","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":619,"height":408,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","medium-width":396,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","medium_large-width":619,"medium_large-height":408,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","large-width":619,"large-height":408,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","1536x1536-width":619,"1536x1536-height":408,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","2048x2048-width":619,"2048x2048-height":408,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","card_image-width":619,"card_image-height":408,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-4-Harvested-energy-estimation-and-proposed-SUWT-locations-spaced-across-flat-rooftop.png","wide_image-width":619,"wide_image-height":408}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Installing SUWTs in urban environments comes with challenges. Public concerns can include:<\/p>\n UC Louvain analysts (including the co-author of this blog)<\/em>, focused on noise and visual pollution as key factors in gaining public acceptance. The noise impact analysis was more straightforward since most SUWT specifications report decibel (dB) levels as “low.” \u00a0The hummm<\/em> of a small generator and the soft whooshing<\/em> of the small blades on these turbines would not overwhelm the atmospheric sounds of the surrounding urban environment. Additionally, distance attenuation from high-rise rooftops and the pre-existing building systems would further mask the sound of these proposed SUWTs.<\/p>\n Ultimately, understanding the visual impact of turbines on the Northern Quarter proved more complex – requiring further study. Analysts found that in this case, aesthetic subjectivity and perception would often contend with project goals. For example, a turbine near a monument might be seen as intrusive, while one on a modern building would symbolize a commitment to green energy.<\/p>\n To further explore these impact studies, check out these peer reviewed articles (Tsionas, Llaguno and Stephan, 2025<\/a>)<\/p>\n To evaluate turbine visibility, ArcGIS Pro<\/a> helped to centralize and process 2D and 3D data sources. Initially, building footprints were extruded as low-poly massing models. Later, the team received and processed aerial LiDAR<\/a> and terrestrial laser scan (TLS) data to refine roof geometries. This would provide a digital twin of the existing roof structures for better visibility results. In this blog, the Finance Tower (a prominent building in the study area) will demonstrate how all buildings and their associated candidate locations were analyzed for visibility in the Northern Quarter.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2686552,"id":2686552,"title":"Fig #5A: Simple roof geometry of The Finance Tower (example buildling)","filename":"Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","filesize":38854,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-5a-simple-roof-geometry-of-finance-tower-example-buildling","alt":"Fig #5A: Simple roof geometry of The Finance Tower (example buildling)","author":"360672","description":"","caption":"Fig #5A: Simple roof geometry of The Finance Tower (example buildling)","name":"fig-5a-simple-roof-geometry-of-finance-tower-example-buildling","status":"inherit","uploaded_to":2684532,"date":"2025-02-18 19:50:50","modified":"2025-02-19 16:48:28","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1047,"height":431,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","medium-width":464,"medium-height":191,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","medium_large-width":768,"medium_large-height":316,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","large-width":1047,"large-height":431,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","1536x1536-width":1047,"1536x1536-height":431,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","2048x2048-width":1047,"2048x2048-height":431,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling-826x340.png","card_image-width":826,"card_image-height":340,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5A-Simple-roof-geometry-of-Finance-Tower-example-buildling.png","wide_image-width":1047,"wide_image-height":431}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":2686542,"id":2686542,"title":"Fig #5B: Refined roof geometry of The Finance Tower (example buildling) as a multipatch","filename":"Fig-5B-Refined-roof-geometry-of-Finance-Tower-example-buildling.png","filesize":305336,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-5B-Refined-roof-geometry-of-Finance-Tower-example-buildling.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-5b-refined-roof-geometry-of-finance-tower-example-buildling","alt":"Fig #5B: Refined roof geometry of The Finance Tower (example buildling) as a multipatch","author":"360672","description":"","caption":"Fig #5B: Refined roof geometry of The Finance Tower (example buildling) as a multipatch","name":"fig-5b-refined-roof-geometry-of-finance-tower-example-buildling","status":"inherit","uploaded_to":2684532,"date":"2025-02-18 19:50:01","modified":"2025-02-25 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Using this data, 2,079 candidate locations were selected in wind-exposed areas without disrupting pre-existing infrastructure. Analysts then placed 22,512 observation points in public spaces at 5- to 10-meter intervals. With the 3D Analyst extension Intervisibility tool<\/a> in ArcGIS Pro, the team began the analysis, seeking which locations (undesirably) had high-visibility.<\/p>\n A custom toolbox created with ModelBuilder<\/a> in ArcGIS Pro enabled detailed, user-defined assessments to test the and alter the placement of these candidates and observer points. Analysts tested views from roads, parks, rivers, and landmarks, ensuring a structured and reusable geoprocessing solution.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2686582,"id":2686582,"title":"Fig #6: Visibility analysis toolbox with custom model builder designs","filename":"Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","filesize":152228,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-6-visibility-analysis-toolbox-with-custom-model-builder-designs","alt":"Fig #6: Visibility analysis toolbox with custom model builder designs","author":"360672","description":"","caption":"Fig #6: Visibility analysis toolbox with custom model builder designs","name":"fig-6-visibility-analysis-toolbox-with-custom-model-builder-designs","status":"inherit","uploaded_to":2684532,"date":"2025-02-18 19:55:13","modified":"2025-02-18 19:55:35","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":667,"height":554,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","medium-width":314,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","medium_large-width":667,"medium_large-height":554,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","large-width":667,"large-height":554,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","1536x1536-width":667,"1536x1536-height":554,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","2048x2048-width":667,"2048x2048-height":554,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs-560x465.png","card_image-width":560,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-6-Visibility-analysis-toolbox-with-custom-model-builder-designs.png","wide_image-width":667,"wide_image-height":554}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Being able to manipulate density and observer height parameters allowed for a more flexible analysis whilst considering the view from different public perspectives. For realistic results, a varying observer height was set to accommodate for physical differences (e.g. child vs. adult). Analysts also accounted for varying elevations from the ground up including views from trains, cars, boats, building floors, terraces and flight paths.<\/p>\n This project initially didn\u2019t plan to use ArcGIS CityEngine<\/a> – but sometimes the right tool finds you!<\/p>\n In the larger suite of Esri products, you have many tools that you can use to customize workflows. Sometimes, it\u2019s just about dipping into your creative problem-solving skills as you progress towards a goal.<\/p>\n Although ArcGIS Pro provided a solid foundation for data and analysis, there were some limitations for this specific use case. Analysts found that SUWTs were only represented as points, resulting in simple yes\/no visibility results when using the intervisibility tool. This led to false negatives when accounting for partial visibility.<\/p>\n Additionally, since objects appear smaller and harder to see with distance, analysts realized they couldn\u2019t directly account for this using the intervisibility tool.<\/p>\n At this stage, the team pivoted to CityEngine, allowing them to measure visual prominence using solid angles. These measurements (steradians) could then be normalized as a percentage of the Field of View (FOV) to better align with human visual perception.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2689092,"id":2689092,"title":"Fig #7: CityEngine viewshed preview of The Finance Tower (example building) with steradian measurements","filename":"Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","filesize":95147,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-7a-cityengine-viewshed-preview-with-steradian-measurements","alt":"Fig #7: CityEngine viewshed preview of The Finance Tower (example building) with steradian measurements","author":"360672","description":"","caption":"Fig #7: CityEngine viewshed preview of The Finance Tower (example building) with steradian measurements","name":"fig-7a-cityengine-viewshed-preview-with-steradian-measurements","status":"inherit","uploaded_to":2684532,"date":"2025-02-19 11:37:17","modified":"2025-02-19 16:47:53","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1324,"height":834,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","medium-width":414,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","medium_large-width":768,"medium_large-height":484,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","large-width":1324,"large-height":834,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","1536x1536-width":1324,"1536x1536-height":834,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","2048x2048-width":1324,"2048x2048-height":834,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements-738x465.png","card_image-width":738,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7A-CityEngine-viewshed-preview-with-steradian-measurements.png","wide_image-width":1324,"wide_image-height":834}},"image_position":"center","orientation":"vertical","hyperlink":""},{"acf_fc_layout":"sidebar","content":" Solid Angles<\/strong> \u2013 a precise measurement accounting for both viewing distance and partial visibility. They are measured in steradians (sr).<\/p>\n <\/p>\n Steradians (sr)<\/strong> – a unit of measure for solid angles in three-dimensional space. It is used to quantify the extent of an object’s projection onto a sphere, similar to how radians measure angles in two-dimensional space.<\/p>\n <\/p>\n Learn more about Visibility Analysis<\/a> in ArcGIS CityEngine<\/em><\/p>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Center","spotlight_image":false},{"acf_fc_layout":"content","content":" As the observer\u2019s distance increases, the area covered by each FOV grows significantly. For example, at 20 meters, the binocular view covers 4,800 m\u00b2, but at 1,000 meters, it expands to 12,000,000 m\u00b2.<\/p>\n <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2686612,"id":2686612,"title":"Fig #8: Horizontal and vertical angles of selected Field of Views (FOVs)","filename":"Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs.png","filesize":945377,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-7-horizontal-and-vertical-angles-of-selected-field-of-views-fovs","alt":"Fig #8: Horizontal and vertical angles of selected Field of Views (FOVs)","author":"360672","description":"","caption":"Fig #8: Horizontal and vertical angles of selected Field of Views (FOVs)","name":"fig-7-horizontal-and-vertical-angles-of-selected-field-of-views-fovs","status":"inherit","uploaded_to":2684532,"date":"2025-02-18 19:58:40","modified":"2025-02-19 12:22:36","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":3785,"height":1620,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs.png","medium-width":464,"medium-height":199,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs.png","medium_large-width":768,"medium_large-height":329,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs.png","large-width":1920,"large-height":822,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs-1536x657.png","1536x1536-width":1536,"1536x1536-height":657,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs-2048x877.png","2048x2048-width":2048,"2048x2048-height":877,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs-826x354.png","card_image-width":826,"card_image-height":354,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-7-Horizontal-and-vertical-angles-of-selected-Field-of-VIews-FOVs-1920x822.png","wide_image-width":1920,"wide_image-height":822}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" A full 360\u00b0 spherical view equals 12.57 steradians, but humans can only see a portion of this. To measure visibility accurately in CityEngine, analysts calculated how much of the view was occupied by wind turbines at different distances.<\/p>\n"},{"acf_fc_layout":"content","content":" Python scripting capabilities in CityEngine streamlined the analysis. With so many install locations and observation points across the study area, automation was essential.<\/p>\n Using Python, the project analysts could more efficiently create viewsheds according to a desired FOV and systematically extract the metrics into comma-separated value (*.csv) files. Those CSV files could then be reimported into ArcGIS Pro and geocoded as point data on a map. Attributes were otherwise related<\/a> to the original observation points to visualize the one-to-many relationship between them. Check out the CityEngine Python Reference<\/a> manual to automate workflows on your next project!<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2687132,"id":2687132,"title":"Fig #9: CityEngine layout with Python Console showing The Finance Tower (example building)","filename":"Fig-8-CityEngine-layout-with-Python-Console-1.png","filesize":1378439,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-8-cityengine-layout-with-python-console-2","alt":"Fig #9: CityEngine layout with Python Console showing The Finance Tower (example building)","author":"360672","description":"","caption":"Fig #9: CityEngine layout with Python Console showing The Finance Tower (example building)","name":"fig-8-cityengine-layout-with-python-console-2","status":"inherit","uploaded_to":2684532,"date":"2025-02-18 21:33:37","modified":"2025-02-19 16:46:46","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2022,"height":1175,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1.png","medium-width":449,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1.png","medium_large-width":768,"medium_large-height":446,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1.png","large-width":1859,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1-1536x893.png","1536x1536-width":1536,"1536x1536-height":893,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1.png","2048x2048-width":2022,"2048x2048-height":1175,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1-800x465.png","card_image-width":800,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-8-CityEngine-layout-with-Python-Console-1-1859x1080.png","wide_image-width":1859,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" <\/p>\n"},{"acf_fc_layout":"sidebar","content":" Tip:<\/strong> A multipatch<\/a> feature is a GIS object that stores a collection of patches to represent the boundary of a 3D object as a single row in a database. Patches store texture, color, transparency, and geometric information representing parts of a feature. The geometric information stored in a patch may be triangles, triangle fans, triangle strips, or rings.<\/p>\n <\/p>\n In ArcGIS CityEngine, imported multipatches using shapefile<\/a> or file geodatabase<\/a> formats appear as triangulated shapes. If you wish to hide the edges of those shapes in CityEngine create a simple CGA rule<\/a> (Init – – > X.<\/strong>). Save this rule, assign it to the multipatch layer and then hide Shapes<\/strong> in the Visibility Settings<\/a> of the 3D View.<\/p>\n","image_reference":{"ID":2701502,"id":2701502,"title":"Identity Rule","filename":"IdentityRule.png","filesize":5633,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/IdentityRule.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/identityrule","alt":"Identity Rule","author":"360672","description":"","caption":"Identity Rule","name":"identityrule","status":"inherit","uploaded_to":2684532,"date":"2025-02-25 14:10:52","modified":"2025-02-25 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#10: CityEngine Viewshed Python automation result of a single SUWT on the Finance Tower (example building)","filename":"Fig10-CityEngine-Viewshed-Python-automation-result-of-a-single-SUWT-on-the-Finance-Tower-Example-Building.png","filesize":519604,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig10-CityEngine-Viewshed-Python-automation-result-of-a-single-SUWT-on-the-Finance-Tower-Example-Building.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig10-cityengine-viewshed-python-automation-result-of-a-single-suwt-on-the-finance-tower-example-building","alt":"Fig #10: CityEngine Viewshed Python automation result of a single SUWT on the Finance Tower (example building)","author":"360672","description":"","caption":"Fig #10: CityEngine Viewshed Python automation result of a single SUWT on the Finance Tower (example building)","name":"fig10-cityengine-viewshed-python-automation-result-of-a-single-suwt-on-the-finance-tower-example-building","status":"inherit","uploaded_to":2684532,"date":"2025-02-25 12:03:14","modified":"2025-02-25 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It’s always great when technical professionals can share their knowledge and offer a helping hand or inspire a spark of creativity to continue the original work. With this in mind, UC Louvain analysts have provided a Python (*.py) script to share with the ArcGIS CityEngine Community – that\u2019s you!<\/em><\/p>\n The script exports the analysis to a CSV file with visibility metrics and viewshed information including the viewing distance and the FOV. The visibility metrics are reported by layer, in sterads X 1000 to enable readability.\u00a0 View\/observer points are selected as viewshed layers in the legend and target objects are in the layers with the targetFilter expression which is hard-coded. The script itself is publicly available on Github<\/a> for anyone that needs it or desires to improve it.<\/p>\n"},{"acf_fc_layout":"content","content":" Using Python to automate the analysis made it much easier to evaluate each SUWT location. The Finance Tower – standing at 141 meters tall was a key test site to validate the analysis. Its rooftop is exposed to high winds and features structures like edge protection, antennas, and mechanical equipment – elements that could help conceal the turbines.<\/p>\n For example, when placing a SUWT at the center of the Finance Tower\u2019s roof, a simple roof model showed eastward visibility. However, the refined roof geometry introduced more obstructions, ultimately blocking visibility to the east. Each SUWT location was tested using Python to ensure accurate results. Check out Fig #11A\/B to compare the sightlines, where colors indicate distance (dark = short, light = far).<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2689132,"id":2689132,"title":"Fig #11A\/B: Visibility differences impacted by roof articulation of The Finance Tower (example building) model","filename":"Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model.png","filesize":4290455,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-11ab-visibility-differences-impacted-by-roof-articulation-of-the-finance-tower-example-building-model","alt":"Fig #11A\/B: Visibility differences impacted by roof articulation of The Finance Tower (example building) model","author":"360672","description":"","caption":"Fig #11A\/B: Visibility differences impacted by roof articulation of The Finance Tower (example building) model","name":"fig-11ab-visibility-differences-impacted-by-roof-articulation-of-the-finance-tower-example-building-model","status":"inherit","uploaded_to":2684532,"date":"2025-02-19 12:19:02","modified":"2025-02-19 16:47:18","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2907,"height":1040,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model.png","medium-width":464,"medium-height":166,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model.png","medium_large-width":768,"medium_large-height":275,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model.png","large-width":1920,"large-height":687,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model-1536x550.png","1536x1536-width":1536,"1536x1536-height":550,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model-2048x733.png","2048x2048-width":2048,"2048x2048-height":733,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model-826x296.png","card_image-width":826,"card_image-height":296,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-11AB-Visibility-differences-impacted-by-roof-articulation-of-the-Finance-Tower-example-building-model-1920x687.png","wide_image-width":1920,"wide_image-height":687}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Continuing to use the Finance Tower as an example, let\u2019s take a look at the map below (Fig#12). You\u2019ll notice that the size of the red circles indicates the degree of visibility and the X\u2019s mean that no visibility was reported (the desired outcome). Now that this analysis is automated, adjustments can be made quickly to try and increase the number of SUWTs hidden from view.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2689262,"id":2689262,"title":"Fig #12: Map of one-to-many visibility analysis results for The Finance Tower (example building)","filename":"Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1.png","filesize":2876979,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/fig-12-map-of-one-to-many-visibility-analysis-results-for-finance-tower-example-building-2","alt":"Fig #12: Map of one-to-many visibility analysis results for The Finance Tower (example building)","author":"360672","description":"","caption":"Fig #12: Map of one-to-many visibility analysis results for The Finance Tower (example building)","name":"fig-12-map-of-one-to-many-visibility-analysis-results-for-finance-tower-example-building-2","status":"inherit","uploaded_to":2684532,"date":"2025-02-19 16:00:40","modified":"2025-02-19 16:08:10","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1322,"height":1867,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1.png","medium-width":185,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1.png","medium_large-width":768,"medium_large-height":1085,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1.png","large-width":765,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1-1088x1536.png","1536x1536-width":1088,"1536x1536-height":1536,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1.png","2048x2048-width":1322,"2048x2048-height":1867,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1-329x465.png","card_image-width":329,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/Fig-12-Map-of-one-to-many-visibility-analysis-results-for-Finance-Tower-example-building-1-765x1080.png","wide_image-width":765,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Analysis in 3D\u00a0 can be demanding on graphics and processing efficiency. For this effort, analyzing visibility required significant processing power. Sessions handled 300 to 500 observation points at a time. The team had to upgrade to a 12th Gen Intel Core i7-12700H with 32GB RAM which outperformed the older Intel Core i5-6500 with 8GB RAM that they started with. It’s a good idea to keep this in mind if embarking on a similar task. To help, you can also check out the Minimum System Requirements<\/a> and Optimization Recommendations for Graphics Cards<\/a> before starting your work.<\/p>\n Urban environments are complex. Gaining public acceptance for energy infrastructure requires balancing quantitative data with subjective perceptions. As with this project, success may also depend on precise modeling, automation, and strong stakeholder collaboration.<\/p>\n By integrating CityEngine with other technologies and data types, you can refine analyses, improve stakeholder collaboration, enhance transparency and develop effective strategies for real-world implementation. Ready to explore? Try CityEngine\u2019s free 21-day trial<\/a> and visit the CityEngine Community Board<\/a> to ask questions.<\/p>\n"},{"acf_fc_layout":"content","content":" For more insights, explore additional project results at BuildWind<\/a> and UCLouvain<\/a>\u00a0<\/em><\/p>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Center","spotlight_image":false}],"related_articles":"","show_article_image":false,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BlogCard_VisibilityAnalysisUCLouvain.png","wide_image":false},"yoast_head":"\nProject Overview<\/h2>\n
Investigating Urban Wind Energy Potential<\/h2>\n
Potential Challenges<\/h2>\n
\n
Rooftop Refinement in ArcGIS Pro<\/h2>\n
Creating Candidate Locations and Observation Points<\/h2>\n
Enhancing Accuracy with ArcGIS CityEngine<\/h2>\n
<\/h2>\n
Field of View (FOV) Coverage by Distance<\/h2>\n
\n
Python in CityEngine<\/h2>\n
Workflow Overview<\/h2>\n
\n
<\/h2>\n
Sharing the Work<\/h2>\n
The Finance Tower – Example Building Results<\/h2>\n
Key Findings from the Enhanced Analysis<\/h2>\n
\n
Computational Efficiency<\/h2>\n
Measuring Success<\/h2>\n
Co-Author<\/h2>\n"},{"acf_fc_layout":"image","image":{"ID":2684562,"id":2684562,"title":"Ioannis Tsionas Bio","filename":"BioForBlog.png","filesize":157026,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/city-engine\/3d-gis\/uc-louvain-3d-visibility-analysis-for-small-urban-wind-turbines-brussels\/bioforblog","alt":"Ioannis Tsionas PhD","author":"360672","description":"","caption":"","name":"bioforblog","status":"inherit","uploaded_to":2684532,"date":"2025-02-17 15:58:59","modified":"2025-02-17 16:00:01","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2080,"height":457,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog.png","medium-width":464,"medium-height":102,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog.png","medium_large-width":768,"medium_large-height":169,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog.png","large-width":1920,"large-height":422,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog-1536x337.png","1536x1536-width":1536,"1536x1536-height":337,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog-2048x450.png","2048x2048-width":2048,"2048x2048-height":450,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog-826x181.png","card_image-width":826,"card_image-height":181,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/02\/BioForBlog-1920x422.png","wide_image-width":1920,"wide_image-height":422}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"sidebar","content":"