Data-Driven Ecosystem Mapping

🚀 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐄𝐏𝐃 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞 𝟐𝟎𝟐𝟔: epd-database.com Where does your EPD end up once it’s published? Does it reach the tools that matter to your customers from the EPD provider you worked with? Which databases does your target Building LCA tool actually pull from? At Emidat, we get these kind of questions everyday and we believe transparency in the LCA data ecosystem is key - yet today, the journey from an EPD to a Building Level LCA tool is mostly a black box. Luckily, EPDs are public and state with which tool they were generated with and which program operator published them. Program operators and Building LCA Tools often publicly state where they send data to or get data from. We decided to aggregate this information and make all EPD Tool providers, (aggregator) databases, building LCA tools and how they interact transparent in one place. 🔥 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞 𝐢𝐬 𝐧𝐨𝐰 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 The EPD Database Landscape is a free, visual map of the global EPD data ecosystem. It traces the full chain: LCA / EPD Tool → Program Operator → Database → Aggregator → Building Level LCA Tool With 140+ entries already mapped, it gives manufacturers a clear picture of how the ecosystem connects, end to end. The use cases are straightforward but powerful: - Want your EPD to appear in Building Level LCA Tool X? Trace exactly which databases feed it, and which operators and LCA tools you need to work with to get there. - Already have an EPD? See where your data currently appears across the ecosystem, and where it’s missing. - Evaluating a new LCA tool or program operator? Understand how far your data will travel before you decide. Note that this is a living tool, based on historic data* - we update it continuously as the ecosystem evolves and welcome input and feedback if we missed something! #Emidat #EPD #LCA #Sustainability #Construction #BuiltEnvironment #CarbonTransparency #Digitalisation #CircularEconomy *We use public EPDs and public information from websites to compile this. If for example tool X is CURRENTLY verifying with program operator Y to get into database Z, this might not be visible yet!

Recently had the privilege of exploring groundbreaking research with Dr. W. Daniel Kissling and Team #Mambo on something I hadn't considered before: the ecological footprint of ungulates through their trails. Nature reserves introduce red deer, horses, and cattle as ecosystem engineers—their grazing, browsing, and trampling reshape landscapes fundamentally. But quantifying this impact? That's where it gets interesting. These herbivores create linear scars across reedbed habitats, trails that fragment vegetation, alter vertical structure, and crucially, impact breeding songbird populations and overall biodiversity. The challenge: traditional mapping methods fail here. Manual in-situ mapping is practically impossible in inaccessible reedbeds, and trails often remain invisible in 2D aerial imagery. Large-scale delineation becomes impractical. Enter airborne #LiDAR data. What caught my attention was the sophisticated methodology. The team's broader research initiative—processes ~500 million LiDAR points across study areas using an innovative approach. Pre-processing removes outliers and re-tiles data to manageable scales. Terrain filtering reconstructs precise Digital Terrain Models. Then comes the clever part: Laplacian-based smoothing differentiates DTMs through iterations, isolating topographic depressions where trails exist. But here's where it elevates beyond typical terrain analysis—3D tensor voting encodes linear features, aggregating fragmented trail segments into coherent networks. This distinguishes actual trails from noise in dense vegetation areas. The validation part is also rigorous. Over 9 million manually-labelled grid cells across 100 randomly-placed 30m plots in contrasting grazing regimes—ground truth at unprecedented scale. The insights: ✓ Airborne LiDAR successfully delineates ungulate trails in wetland environments ✓ Topographic signatures (depression depth) combined with linear feature encoding enable accurate extraction ✓ Quantifiable trail networks reveal ecosystem engineering impacts previously invisible ✓ Data-driven approach replaces subjective field assessments Dense trail areas create local planar features challenging for tensor voting Specialized point cloud processing knowledge required Computational demands scale with dataset complexity What struck me most: this bridges wildlife ecology with geospatial innovation. Understanding where deer walk becomes understanding how habitats fragment and species respond. It's ecosystem science powered by 3D data intelligence. #LiDAR #RemoteSensing #EcosystemEngineering #WildlifeConservation #PointCloud #WetlandEcology #GIS #DigitalTerrain #3DMapping #ConservationTechnology #EcosystemScience #Ungulates #DroneMapping #GeospatialInnovation Paper mentioned: Wang et al. 2025. DOI: 10.3389/frsen.2025.1599128 Github: https://lnkd.in/gYZegEaP

Quietly, something important just happened: the Ecosystem Integrity Index (EII) is now open source. Every team can now reliably set targets and report against a comparable, trustworthy measure of ecosystem health to their stakeholders. That might sound technical. It’s not. It’s a fundamental shift in ecosystem visibility. For years, one of the biggest constraints on nature-based finance hasn’t been intent or capital, it’s been measurement. We’ve had strong data on pressures (land use, climate, extraction). We’ve had deep insight into species responses. But we’ve lacked a unified, scalable, publicly available way to assess ecosystem condition itself. EII begins to change that. This release includes: – Global coverage at 300m resolution – Current-state ecosystem mapping – A Python API for accessible data use – Google Earth Engine integration for scalable analysis Why this matters: – You can’t price risk you can’t see. – You can’t invest at scale without comparability. – And you can’t govern what you can’t measure. Open, system-level metrics like this don’t just support better science, they unlock better markets, better decisions, and better incentive structures. Check it out: https://lnkd.in/gSPQcRwg

Imagine seeing our planet with eyes sharper and clearer than ever before—well, Google's DeepMind has just made this a reality with their groundbreaking release, AlphaEarth Foundations. This isn't just another map, it's a transformative leap in how we understand, protect, and engage with our environment. Unlike previous mapping technologies, AlphaEarth leverages advanced AI to generate an incredibly detailed, continuously updating portrait of Earth, capturing changes in land use, vegetation, water distribution, and urban growth with unprecedented precision. Why does this matter so much? For environmentalists, conservationists, and sustainability advocates, the implications are enormous. AlphaEarth allows us to track biodiversity hotspots, monitor ecosystems in near-real-time, and proactively respond to environmental threats with agility previously out of reach. Before AlphaEarth, satellite imagery and mapping were static snapshots, often outdated by months or even years. Now, decision-makers can access timely, actionable insights to protect habitats, manage natural resources sustainably, and tackle pressing global challenges such as deforestation and climate change. Startups and NGOs like MapBiomas (Brazil), the Global Ecosystems Atlas, the UN’s Food and Agriculture Organization, Harvard Forest, Oregon State University, the Spatial Informatics Group, and Stanford University are already leveraging AlphaEarth to enhance their critical conservation and research efforts. They’re using this powerful dataset to map deforestation, monitor ecosystem changes, and identify biodiversity priorities, driving real-world impact and sustainability. Beyond mere observation, AlphaEarth represents a powerful tool for change, enabling targeted conservation efforts, more informed policy-making, and strategic urban planning aligned with nature-based solutions. With the precision offered by DeepMind’s AI, the possibilities for enhancing biodiversity and ecosystem resilience are truly inspiring. This release gives me genuine hope, not just because of its technological prowess, but because it represents exactly how cutting-edge technology can and should serve our shared planetary goals. Check out more about this incredible advancement here: https://lnkd.in/ezgv9qJX Let's harness technology for a sustainable future—because our planet deserves nothing less. #AlphaEarth #Sustainability #Biodiversity #AIforGood #Conservation #EnvironmentalInnovation #DeepMind

ArchAI, established in 2020 and headquartered in London, UK, specializes in leveraging artificial intelligence to analyze historical and environmental data, primarily focusing on uncovering "lost habitats." Their flagship product, Lost Habitats, utilizes AI to process historical maps and contemporary environmental data, providing insights into the evolution of various UK habitats, including woodlands, wetlands, and pastures. This tool aids conservationists, land managers, and developers in identifying and restoring degraded ecosystems, supporting initiatives like Biodiversity Net Gain (BNG). The company has successfully collaborated with organizations such as the Forestry Commission, National Trust, Natural England, and the University of Southampton. These partnerships have facilitated projects that enhance habitat restoration and inform land management decisions across the UK. ArchAI's innovative approach has garnered recognition, with features in prominent media outlets like BBC and The Guardian. Their AI-driven solutions are transforming how historical data informs environmental conservation and land development strategies. ArchAI’s technology uses artificial intelligence to analyze historical maps and environmental data to uncover lost habitats. The platform primarily processes large datasets, such as old maps, aerial imagery, and contemporary environmental data, using machine learning models to extract key insights. Here’s how it works: 1️⃣Data Collection: The platform collects historical maps, archival records, and modern geospatial data, focusing on landscape features like woodlands, wetlands, and pastures. 2️⃣AI Processing: ArchAI uses machine learning algorithms to analyze these datasets. The algorithms identify patterns and changes over time, particularly in how landscapes have evolved. By comparing historical and modern maps, the AI identifies lost or degraded habitats that may have existed in the past. 3️⃣Geospatial Integration: ArchAI integrates its findings with GIS (Geographic Information Systems) tools, allowing users to visualize changes in the landscape, including areas that may have been restored or are in need of restoration. 4️⃣Insight Generation: The AI identifies key regions that require attention for conservation and restoration efforts, providing detailed insights for land managers, conservationists, and developers. ==**== Discover how ArchAI can revolutionize your conservation efforts. 🔗 Share your thoughts or experiences below! 💬 Comment, follow us for updates, and join the conversation on leveraging AI for a sustainable future!