Landscape Design Software

In lighting design, layering is essential - not just for illumination, but for creating a functional and visually appealing space. By combining different layers of light, we can enhance both the atmosphere and usability of a room. 🔹Ambient Lighting The foundational layer. It provides general, overall illumination that allows us to navigate a space. Think ceiling fixtures, soft wall sconces, or indirect lighting that sets a comfortable baseline. 🔹Task Lighting Focused and practical. This is lighting meant to support activities—reading, cooking, working. Under-cabinet lighting, desk lamps, and pendants above counters all fall into this category. 🔹Accent Lighting Accent lighting adds depth by highlighting specific features like artwork or architectural details. It creates contrast and draws attention to focal points. 🔹Decorative Lighting The jewelry of a space. Chandeliers, sculptural pendants, and unique wall lights can make a visual statement while also serving functional roles. A well-placed decorative pendant, for example, might double as task lighting over a dining table or a kitchen island. Why Use Lighting Layers? Lighting layers are important because they create a balanced, adaptable space. They not only improve the functionality of a room, making sure it’s lit properly for various tasks, but also prevent spaces from feeling flat or monotonous. By layering different types of lighting, you can add dimension, emphasize key design features, and enhance the overall user experience. This approach makes design more flexible and visually dynamic. #Lighting #LightingDesign #LightingLayers

Are you still wasting time collecting site data from 5 different portals? Building footprints from one source. Wind patterns from another. Topography? Probably buried in a PDF somewhere. It’s 2025, and with AI tools around, we shouldn’t be spending hours stitching datasets together just to start a design. I use Aino to cut through the noise and get clean, reliable data fast. Here’s what makes it work so well for site studies: 👉 Building footprints and building use mapped in seconds 👉 Adjustable building heights visualised in a gradient 👉 Real-time wind movement overlays 👉 Street network identified and simplified 👉 Topography with contour clarity 👉 Open spaces sorted into categories My favourite features: ✅ Traffic Heatmaps ↳ See where bottlenecks occur and plan circulation with confidence. ✅ Clip and Export ↳ Crop any area and export in PNG, SVG, PDF, or DXF for design workflows. With Aino, you spend less time on data chaos and more time designing with clarity. Want to see how it works in real projects? I’ve added a short tutorial video below.

🚀 Fʀᴏᴍ Pᴏɪɴᴛ Cʟᴏᴜᴅꜱ ᴛᴏ Pʀᴇᴄɪꜱɪᴏɴ: Cᴏʀʀɪᴅᴏʀ Mᴀᴘᴘɪɴɢ ᴡɪᴛʜ AʀᴄGIS Pʀᴏ 🌐 Ever wondered how point clouds can be transformed into actionable insights for corridor analytics? Let me take you through a workflow starting from an unclassified Point Cloud! 🛰️✨ Besides using out-of-the-box tools for ground and building classification you also can use deep learning models to classify point clouds. In this case, LiDAR point clouds were additionally classified into Powerlines using a 𝐩𝐫𝐞-𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐄𝐬𝐫𝐢 𝐋𝐢𝐯𝐢𝐧𝐠 𝐀𝐭𝐥𝐚𝐬. The classified Point Cloud then was used to derive raster information such as elevation layers and also features, such as line features for the powerlines (Have a look at the complete workflow in the Storymap (LINK in Comments). This paved the way for leveraging the 3D Analyst tools in ArcGIS Pro. From corridor mapping to spatial analytics, ArcGIS Pro became the hub where data turned into decisions! 🌟 This approach not only simplifies the complexities of point cloud data but also enables accurate and efficient corridor mapping planning, environmental, crucial for infrastructure studies, and beyond. 🛤️🌿 💡 By combining AI with advanced GIS tools, we’re redefining the way we analyze and visualize our world in 3D. 🧠📊 Have a look at the Storymap for the complete workflow! Link in the Comments! 🤝 Let's spark a conversation! How are you leveraging Point Clouds in ArcGIS? Share your insights, challenges, and success stories below. Let's amplify our collective GIS capabilities! 💬💡 💡 #IMGIS25 #LandCover #3DAnalytics #Geospatial #LiDAR #Innovation #CorridorMapping #DeepLearning #Sustainability #Forests #ArcGISPro #Esri #GIS #SpatialAnalysis #ArcGIS #remotesensing #EsriDeutschland #EarthObservation #EsriLivingAtlas #DataVisualization #esrivoices🔍 🚀 🌱

Unlocking the Power of GeoAI: From Raw Geospatial Data to Actionable Insights GeoAI is fundamentally changing the way we work with geospatial data. Today, artificial intelligence is not just a research topic, but a practical tool that helps us turn massive amounts of aerial imagery and lidar data into real, actionable information. By combining neural networks with proven photogrammetry and rule-based quality assurance, we can now extract detailed land cover maps, analyze urban surfaces, and even simulate urban climate with a level of precision that was unthinkable just a few years ago. One of the most exciting aspects is how GeoAI enables us to move beyond traditional mapping. With AI-powered segmentation, we can distinguish even the smallest features in urban environments and keep our data up to date. Thanks to TrueOrthos and advanced photogrammetric workflows, geometric distortions are a thing of the past, so data from different times and sensors can be perfectly aligned. This is essential for reliable change detection and multi-source analysis. But the possibilities go even further. Automated analysis of sealed and unsealed surfaces helps cities identify where to prioritize “desealing” for climate resilience. Parcel indexing allows us to aggregate key indicators like green space, building area, or solar installations at any scale, supporting truly data-driven decisions in urban planning and environmental monitoring. And with urban climate simulation, we can combine pixel-precise land cover data with 3D voxel models and CFD to visualize the effects of new trees, green roofs, or lighter pavements, before any construction begins. Even lidar point cloud classification benefits from GeoAI. By combining AI with rule-based checks and external data sources, we achieve robust, scalable, and quality-assured 3D mapping, reducing manual effort and increasing reliability, even in complex or changing environments. GeoAI is already a productive, scalable approach that is shaping the sustainable, data-driven development of our cities and landscapes. With annual updates and hybrid workflows, we ensure that results are not only precise and up to date, but also trusted and actionable. If you want to learn how to turn your geospatial data into valuable information using GeoAI, just reach out or send me a message. Let’s move from data to information, using GeoAI. 💡 Comment | Like | Share   👉 Follow me (Dr. Uwe Bacher) for more Information on exciting topics from the world of geospatial #GeoAI #Geospatial #AerialImagery #Lidar #UrbanPlanning #AI #SmartCities

Scanning above and below ground whilst creating advanced 3D models at Highways UK. I caught up with James Tindall from Castle Surveys Ltd to talk about its real showstopper. Their fully wrapped mobile mapping unit, equipped with Leica Geosystems part of Hexagon mobile mapping and ground penetrating radar solutions. James: “Our TRK mobile mapping system captures data at highway speeds, up to 70 miles an hour, making it perfect for topographical surveys, asset management, vegetation encroachment, and pavement analysis. "In conjunction with the Stream UP ground penetrating radar, we’re now able to capture above and below ground utility information simultaneously.” What’s equally important is what happens next, the processing. For that, Castle Surveys has chosen TopoDOT, as James explained: “We wanted a solution that could give us everything we needed, with no compromise. TopoDOT lets us extract, assess and verify our data in one place. It’s the reassurance that what we hand over to clients is completely accurate.” To find out more, I spoke with Filipe Pinto from TopoDOT, who explained how their software turns raw data into actionable insights. “TopoDOT empowers any LiDAR user from mobile mapping to UAV and static scanning to transform complex point clouds into vector data for decision-making. "Users can extract features like kerbs, signage, and pavement condition, calculate volumes, assess bridge clearances, and even identify potholes automatically.” And it doesn’t stop there. Filipe added: “Our collaboration platform means clients don’t need CAD or GIS software. They can view and query LiDAR derived data through a simple web link, making it accessible to designers, engineers and maintenance teams alike.” It’s great to see how Leica Geosystems cutting-edge capture technology, Castle Surveys’ surveying expertise, and TopoDOT’s powerful processing tools have come together at Highways UK. And we look forward to visiting the Castle Surveys team to learn how they put everything together. #surveying #highwaysUK #highways #mobilemapping #infrastructure #pointclouds 

What if you could fly through someone’s brain — and actually watch it think in real time? 🧠 This stunning 3D visualization makes that possible. It shows live brain activity mapped from EEG (electroencephalography) signals onto a realistic 3D model of the human brain. Each color represents a different brainwave frequency — from calm alpha and focused beta, to fast, high-energy gamma rhythms. The golden lines trace the brain’s white matter pathways, and the moving light pulses represent information flowing between regions — the brain communicating with itself in real time. How it’s built The process begins with MRI scans to create a high-resolution 3D model of the brain, skull, and scalp. Then, DTI (Diffusion Tensor Imaging) maps the brain’s wiring — the white matter tracts that connect its regions. Next comes EEG recording, captured using a 64-channel mobile EEG cap. Advanced software pipelines like BCILAB and SIFT clean the data, remove noise, and use mathematical modeling to “source-localize” brain activity — estimating where in the brain each signal originates. They also analyze information flow using a technique called Granger causality, revealing which brain regions are influencing others at any given moment. From Data to Experience All of this is brought to life in Unity, a 3D engine usually used for games. Here, the brain becomes a fully navigable world — you can literally fly through it using a controller and watch live signals flicker and flow. It’s data turned into experience — a fusion of neuroscience, art, and technology that lets us see the living mind at work. Why it matters By merging EEG, MRI, and DTI, researchers can study how the brain’s networks communicate, and how this connectivity changes in conditions like epilepsy, depression, or neurodegenerative diseases. This work also pushes forward brain-computer interface research — paving the way for future technologies that help restore movement, communication, or sensation through brain signals alone. Every flicker of light here represents a thought, a signal, a decision — the brain in motion. 🎥 Video Credits: Dr. Gary Hatlen

Lighting standards exist for meeting rooms. Wonder why video can still look terrible? ANSI/IES/AVIXA RP-38-17 is the joint standard between the Illuminating Engineering Society and AVIXA specifically for videoconferencing lighting. It provides measurable lighting performance criteria to optimize lighting for cameras, improving images transmitted to remote sites. The standard requires around 500 lux on participants' faces for optimal video quality. Many meeting rooms only actually deliver 150-300 lux. The gap is massive. And it shows. When you're designing hybrid meeting spaces, poor lighting degrades even the most expensive camera systems. Your expensive PTZ camera can't compensate for fundamentally inadequate illumination. The standard wasn't written for videoconferencing rooms in isolation. Meeting rooms have become videoconferencing rooms by default. Most meetings now have remote participants. Every room needs to perform on camera. Yet we're still designing meeting room lighting as if it's 2019. The ANSI/IES/AVIXA standard covers spaces with 3-25 participants, but its principles apply to any space where people appear on camera. Which is most meeting rooms now. CIBSE guidelines also recommend 500 lux for meeting rooms with video conferencing considerations. The standard exists. The guidance is clear. The question is: why aren't we measuring and implementing it? Usable meters cost from under £50. The difference between 200 lux and 500 lux can be the difference between participants looking washed out or professional on camera. Your hybrid meeting experience depends on lighting design as much as your AV kit. Lighting falls within Environment in GJC’s EASE framework for meeting space design. EASE = Environment, Audio, Screens, Equity, our methodology that makes hybrid workspace design systematic instead of accidental. Please see link in Comments section below. What's your experience? Are you measuring lux levels in your meeting rooms, or just hoping the lighting works?   #microsoftteamsrooms #avtweeps #EASEmethodology #hybridmeetings #avusergroup #ltsmg #schoms  #avixa

I work in VFX and only recently understood the full concept of Gaussian Splats. I had seen the demos and nodded along when people referred to it as "the next photogrammetry." However, if you had asked me to explain what they are, their place in a real pipeline, or their capabilities, I would have struggled. So, I took the time to understand it, and here’s the simplified version I wish I had received earlier A Gaussian Splat consists of millions of tiny semi-transparent ellipsoids in 3D space. You input photos, train a model, and receive a scene in return. Phones serve as capture devices, and it can render at over 100 FPS in real time. Key tools like Nuke 17 now include native support, Houdini 21 offers a tech preview, V-Ray 7 can ray-trace splats, and OpenUSD 26.03 has introduced a first-class schema. Notably, Framestore utilized 4D Gaussian splatting for approximately 40 final-pixel shots in Superman last year. What it can achieve: - Rapid and cost-effective capture of real environments - Rendering at game-engine speeds - Integration into compositing without the need to recreate the world in CG What it cannot do: - Relight scenes (yet) - Provide a clean mesh (yet) - Render with AOVs, as the lighting is baked into the data. This is the trade-off. Thus, it does not replace photogrammetry or CG environments; rather, it serves as a new tool for scenarios requiring photoreal capture and real-time playback, with the understanding that relighting flexibility is sacrificed. For fellow VFX artists who have been quietly nodding along: you are not behind. The foundational paper is from 2023, and most production tools have been released in the past six months. Now is the time to learn it before it becomes a part of your next project. What new technology have you been struggling with?

AI didn’t replace our rendering workflow it refined it. This is my first full application of Nano Banana Pro on one of our interior projects at WT Decoration Design. The improvement in realism, warmth, and material accuracy was far beyond what I expected. To achieve the final result, I combined a complete hybrid pipeline: Tools Used 3ds Max + Corona Renderer – Base lighting, geometry, and raw materials Nano Banana Pro – Material enhancement, soft lighting corrections, texture realism Magnific AI – Fine-detail upscaling without altering the original design Photoshop – Final grading, color balance, and magazine-level mood Reference: Pottery Barn Middle East to match their signature furniture aesthetic and styling Why this workflow works Each tool contributes its strength: Corona delivers clean raw lighting and solid structure. Nano Banana Pro elevates micro-details, nuances in fabrics, and warmth. Magnific AI sharpens realism without inventing new textures. Photoshop blends everything into a cohesive, editorial-quality final frame. The result? A highly realistic interior shot that closely mirrors the Pottery Barn reference from wood tones to fabric depth to soft daylight behavior. The Future of Rendering = 3D Expertise + AI Precision + Artistic Direction Not one tool replaces another. It’s the combination that creates results at this level. What do you think of the transformation? Which version do you prefer — Original, or Nano Banana Pro? Happy to hear your thoughts 👇