AI In Environmental Monitoring

🧠 GPT changed language. Clay might change the way we understand Earth. Clay is an open-source foundation model for Earth: trained on massive amounts of satellite imagery across location and time. It transforms the complexity of environmental data into powerful embeddings that can be used to: ✅ Identify land cover, crop types, or urban expansion ✅ Detect change like wildfires, floods, or deforestation ✅ Power downstream models for prediction, classification, and mapping ✅ Serve as a backbone for custom geospatial AI pipelines The result? A model that understands Earth the way LLMs understand language. Training models is tough, plus you need access to massive amounts of data. As foundational models start to get better, the data backbone being built by Cloud-Native Geospatial Forum (CNG) data and computing systems that can leverage these models like those we are working on at Wherobots can help bring these models to global scale. This is bigger than just another geospatial model. It’s a signal that foundation models are coming to remote sensing, and with them, a new paradigm: 🧠 Pre-trained models that can be adapted everywhere 📡 Build models with fewer labels 🌱 Tackle climate, agriculture, and environmental challenges with speed If you’re working in geospatial AI, Earth observation, or climate data: Clay is worth watching. And using. It's open source and live on Hugging Face and GitHub. The geospatial foundation model era is bound to be an exciting one. 🌎 I'm Matt and I talk about modern GIS, geospatial data engineering, and how spatial thinking is changing. 📬 Want more like this? Join 5k+ others learning from my newsletter → forrest.nyc

How AI is helping protect giraffes 🦒 For decades, tracking individual giraffes meant sketching spot patterns in notebooks or spending weeks poring over photographs. Now artificial intelligence has given conservationists in Tanzania a faster way, reports Abhishyant Kidangoor. The Wild Nature Institute, co-founded by biologists Monica Bond and Derek Lee, has teamed up with Microsoft’s AI for Good Lab to launch Project GIRAFFE, an open-source tool that uses algorithms to identify and re-identify individual animals by their markings. “It can now be done in minutes, and we can have the output the same day we collect the data,” says Lee. The technology matters because survival and reproduction rates, movements, and social behavior can only be measured if scientists know which giraffes they are seeing. Once-healthy populations have dwindled across Africa due to poaching and habitat loss. The Masai giraffe, Tanzania’s national animal, has declined by half in recent decades. Project GIRAFFE allows researchers to process millions of photographs gathered during annual surveys. The data help pinpoint strongholds for the species and reveal landscapes where giraffes are struggling. Protecting their habitat brings wider gains, since many other species share the same ecosystems. “A big part of our mission is to see that people and the giraffes are both thriving together,” says Lee. 📰 story: https://lnkd.in/gnArQm2U

Scientific synthesis is resource-intensive. Those who have contributed to IPCC or similar processes know this: the coordination, the iteration, the careful weighing of heterogeneous evidence. It is demanding work, and the pool of experts willing to undertake it is finite. Where might AI meaningfully assist without undermining what makes assessment trustworthy? We explored this question in a collaboration between Google DeepMind, the PIK - Potsdam Institute for Climate Impact Research, ETH Zürich, University of Zurich, University of Miami, Imperial College London, and Stripe. Thirteen climate scientists. The test case: AMOC stability, a domain with genuinely conflicting lines of evidence, where synthesis requires more than literature aggregation. AI proved useful for structure, retrieval, and consistency checking. It helped reduce coordination overhead. The efficiency gain was real: 79 papers synthesised through 104 revision cycles in under 47 person-hours. But the intellectual core remained human. Experts contributed 58% of the final content. Not stylistic refinement, but substantive judgment: reweighting conflicting evidence, inserting caveats, revising confidence levels where the AI had interpolated consensus that wasn't there. AI provided breadth. Humans provided judgment. This reflects a deeper distinction: synthesis is not review. An IPCC chapter does not summarise papers. It weighs conflicting evidence and produces calibrated uncertainty statements that exist in no single source. That operation remained human. Our work serves as starting point, not as a conclusion. Perspectives from colleagues welcome. Preprint: https://lnkd.in/dwsck3Gd Markus Leippold, Anna Koivuniemi #ScientificAssessment #ClimateScience #AIforScience

Tagging whales with drones is transforming marine research—and it’s a powerful example of how AI, robotics, and advanced sensing are accelerating conservation efforts. Fascinating? For decades, studying whales required researchers to approach animals by boat, a process that was expensive, time-consuming, and often limited by weather and ocean conditions. Today, drones are changing the game. Researchers can deploy tags, collect respiratory samples (“whale blow”), measure body condition, and monitor behavior from the air with far less disturbance. Some drone-based photogrammetry systems can measure whale size and body condition with centimeter-level accuracy, providing critical insights into health, pregnancy, nutrition, and population trends. Consider the scale of the challenge: 🐋 Blue whales can reach over 30 meters (100 feet) in length and weigh more than 180 metric tons. 🌊 Many whale species migrate 5,000–20,000 kilometers annually, making long-term monitoring incredibly difficult. 🚢 More than 80% of global trade travels by sea, increasing the importance of understanding interactions between marine life and shipping routes. 📊 AI-powered image analysis can process thousands of aerial images, identifying individual whales, estimating population sizes, and detecting behavioral changes far faster than traditional manual methods. 🌍 The ocean absorbs approximately 25–30% of human-generated CO₂ emissions, making healthy marine ecosystems increasingly important in the fight against climate change. The future is even more exciting. Imagine autonomous drone networks working alongside satellites, underwater acoustic sensors, autonomous surface vessels, and AI models that continuously analyze data streams from across the world’s oceans. Researchers could: ✅ Track migrations in near real time ✅ Detect health issues before populations decline ✅ Reduce ship strikes through predictive routing ✅ Monitor the effects of climate change on feeding grounds ✅ Build digital twins of marine ecosystems for simulation and planning This is where technology becomes more than innovation—it becomes a force multiplier for conservation. The same advances in AI, edge computing, sensors, and autonomous systems that are transforming industries are now helping scientists better understand and protect some of the largest animals ever to live on Earth. Every tag deployed, every image captured, and every AI model trained brings us closer to a future where technology and conservation work hand in hand to protect our oceans for generations to come. #AI #Drones #Whales #MarineBiology #OceanTech #Robotics #Conservation #ClimateChange #MachineLearning #AutonomousSystems #DigitalTransformation #Innovation #Sustainability #WildlifeConservation #BlueEconomy #FutureTech #DataScience #EnvironmentalScience #TechForGood #ResearchAndDevelopment

Flash flooding is becoming more frequent and less predictable across the U.S. In the Appalachian region, communities often get only a few hours of warning, putting lives, infrastructure, and local economies at risk. Through the #IBMImpactAccelerator, IBM is collaborating with the University of Illinois Urbana-Champaign Center for Secure Water to change that, with the project coordinated by Professor Ana Barros from the Civil and Environmental Engineering at Illinois department. Pairing Illinois’ hydrology and precipitation modeling with IBM technologies like watsonx.ai, IBM Cloud for Government, and Cloud Pak for Data, the team is improving rainfall prediction and flood forecasting in complex mountainous terrain. Two key innovations are emerging: 💡Enhanced Precipitation Forecasting, which uses AI to correct errors in leading weather models 💡Flood View, a tool that integrates this enhanced rainfall data with hydrology models, delivering earlier flash flood warnings through an interactive map, alerts, and local watershed insights Flood View is already supporting the U.S. National Park Service (NPS). NPS is using Flood View to strengthen disaster preparedness by planning road and park closures in advance and monitoring specific points of interest across the parks. With more reliable forecasts, extending lead time from roughly six hours to up to 48 hours, communities gain critical time to prepare, protect infrastructure and stay safe. Watch the full video to learn how AI, research, and public-sector collaboration are strengthening climate resilience in the U.S.: https://lnkd.in/eSCVq_VW

In the past few years, I have worked quite a lot on GreenTech and climate AI and have shared resources on the same. Today is one such day again!   Chile's Nahuelbuta mountain range is an awe-inspiring tapestry of biodiversity, teeming with unique species such as the Darwin's fox. However, this ecosystem constantly faces threats from human activities, wildfires, and encroachment. With less than 1,000 Darwin's foxes remaining, their existence is hanging by a thread. Enter the "Nature Guardian" initiative – a collaborative marvel involving Rainforest Connection (RFCx), deploying solar-powered devices enriched with AI to monitor and safeguard this invaluable ecosystem vigilantly. "Nature Guardian" is supported by Huawei’s #TECH4ALL, which is always committed to enable an inclusive and sustainable digital world. These ingenious devices have evolved into the unseen sentinels of this diverse landscape, ceaselessly engaged in environmental monitoring, tracking animal calls, and swiftly identifying threats like illegal logging and poaching. Meticulously positioned high in the treetops, they provide round-the-clock coverage, seamlessly linked to a cloud-based AI platform. One of the project's noteworthy facets lies in its AI analytics, expertly trained to recognize various animal species. This empowers researchers to scrutinize their distribution and behaviours, offering invaluable insights for adaptive conservation measures. What I also found interesting is the system's ability to issue real-time alerts via a mobile app if any threat is detected, enabling rapid responses to protect this delicate ecosystem. As of August 2021, five Nature Guardian devices and ten edge devices had been deployed, covering 30 km2 of Nahuelbuta forest. However, the project's vision doesn't halt here; it's expanding to Chiloe Island and the coastal regions of the Valdivian forest, where sightings of the elusive Darwin's fox have been reported. This endeavour underscores the power of collaboration among organisations like RFCx, Bioforest, Etica en los Bosques, the Ministry of the Environment for Chile, and Huawei. In an era where climate change and forest degradation pose significant challenges to ecosystems worldwide, their combined expertise in conservation and technology is contributing towards preserving Chile's unique biodiversity. #innovation #technology #artificialintelligence #greentech #techforgood

🚀 AlphaEarth Foundations (AEF) - New from Google DeepMind I keep looking out for interesting usecases of AI. Deepmind folks are at it again. 📄 Paper: AlphaEarth Foundations on arXiv (https://lnkd.in/giHUwe2d) --- 🌍 What is AlphaEarth Foundations? AEF is a foundation model for Earth observation that turns sparse and messy satellite, climate, LiDAR, and even text data into dense embeddings at 10 m² resolution. These embeddings provide a universal feature space for mapping and monitoring the planet, outperforming all previous approaches — reducing mapping errors by ~24% on average. And the best part? The embeddings are already available as annual global datasets (2017–2024) for free: 👉 Earth Engine Data Catalog: Google Satellite Embedding V1 Annual - https://lnkd.in/g6dcv4-M --- 🛠 Why does this matter? (weekend project ?) For places like Bengaluru, India (or any fast-changing city), AEF makes it possible to: - Track urban growth and land use change with very few ground samples. - Monitor lakes and wetlands for encroachment and seasonal changes. - Map flood risk by combining rainfall, elevation, and land cover. - Identify urban heat islands and vegetation loss. - Support peri-urban agriculture with low-shot crop type classification. - Study biodiversity shifts (tree species, invasive plants) by linking with GBIF/iNaturalist data. In short, it’s like having a plug-and-play geospatial backbone — ready to support everything from city planning to climate adaptation. --- 🔧 For the Geeks Want to try it out? You can get started in minutes using Earth Engine + Python: 📘 Earth Engine Python Quickstart Docs - https://lnkd.in/g9zBBPJv 🌐 This is a big step toward planetary-scale AI for environmental monitoring — making high-quality maps possible even when labels are scarce. --- Further reading : 1. https://lnkd.in/gsXU2BqS 2. https://lnkd.in/gxJpqS6b --- Authors: Christopher Brown, Michal Kazmierski, Valerie Pasquarella, William J. Rucklidge, Masha Samsikova, Chenhui Zhang, Evan Shelhamer, Estefania Lahera, Olivia Wiles, Simon Ilyushchenko, Noel Gorelick, Lihui Lydia Zhang, Sophia Alj, Emily Schechter, Sean Askay, Oliver Guinan, Rebecca Moore, Alexis Boukouvalas, Pushmeet Kohli.

Our recent study, led by Shahrzad Gholami from the Microsoft AI for Good Lab in collaboration with Derek Lee and Monica Bond from the WILD NATURE INSTITUTE , has just been published in Nature Scientific Reports The paper introduces GIRAFFE , Generalized Image-based Re-identification using AI for Fauna Feature Extraction , an AI-powered system for wildlife re-identification in conservation. Animal re-identification is a critical tool for conservation. It enables researchers to estimate population size, study movement and behavior, understand demographic trends, and evaluate whether conservation programs and policies are working. Historically, identifying individual animals has often required physical tags, collars, or time-consuming manual review of images. These approaches can be expensive, invasive, and difficult to scale. With GIRAFFE, we show that AI can re-identify individual giraffes using only photographs. By analyzing the unique spot patterns of each animal, the system can identify known individuals and help organize new individuals for expert review. This makes wildlife monitoring faster, more scalable, more cost-effective, and more accessible to conservation teams in the field. This is a powerful example of how AI can help protect biodiversity by giving conservationists better tools to understand and protect species in the wild. Paper: https://lnkd.in/dhHuQTk3

Over the past few days I’ve been reading the Stockholm Resilience Centre’s new report AI for a Planet Under Pressure.  It’s a very comprehensive examination of how AI can support sustainability research, climate science and planetary resilience. It’s an important contribution at exactly the moment when society needs clarity on both the opportunities and the risks of advanced digital technologies. A few reflections from me: AI is already accelerating scientific discovery The report shows that AI is helping researchers uncover patterns in climate, biodiversity, freshwater and urban systems that were previously too complex or computationally demanding to analyse. From high-resolution climate downscaling to modelling Earth system tipping points, AI is opening new windows into how the planet is changing and where intervention is most urgent.  This is not just for relevant for scientific discovery. Climate science is becoming more predictive, more integrated, and more accessible One of the strongest messages is that AI is enabling a shift from isolated models to integrated cross-system understanding. For example, foundation models trained on vast geophysical datasets can support multiple climate-related tasks, from cyclone tracking to air quality forecasts, with far lower computational costs. This has real implications for researchers and public agencies that previously lacked the resources to run such models. But the benefits will depend on how responsibly we apply these tools The report is refreshingly balanced. It highlights very real concerns: the environmental footprint of compute, bias in data and models, uneven global representation in AI research, and the risk of over-reliance on systems that may still contain blind spots. Crucially, it argues that AI must support, not substitute, scientific judgement and local knowledge. What I find most compelling is the call for an “AI for sustainability science” agenda. This means moving beyond pilots and experiments and investing in the infrastructures, skills and governance frameworks that allow AI to strengthen climate research while remaining aligned with planetary boundaries and social equity. In other words: more capability, yes but also more responsibility, transparency and inclusion. For those of us working at the intersection of digitalisation, sustainability and resilience, this report is a timely reminder: AI’s contribution to climate action won’t be measured by novelty, but by whether it helps societies anticipate risks, steward ecosystems, and make better collective decisions under pressure. Well worth a read! https://lnkd.in/eMheXkkx #AI #Sustainability #ClimateScience #DigitalTransformation #Resilience #Research #TechForGood

At #COP29 this year, climate adaptation, and the role of technology to support with early warning systems, adaptation and resiliency was high on the agenda. One area Google has been working on for a number of years is using AI to help forecast riverine floods, and I'm excited about our recent expansion: 🌎 Expanding coverage of our AI-powered riverine flood forecasting model to 100 countries (up from 80) in areas where 700m people live (up from 460m). 🔮 An improved flood forecasting model — which builds upon our breakthrough model — that has the same accuracy at a seven-day lead time as the previous model had at five days. 📖 Making our model forecasts available to researchers and partners via an upcoming API and our Google Runoff Reanalysis & Reforecast (GRRR) dataset. 👐 Providing researchers and experts with expanded coverage — based on “virtual gauges” for locations where data is scarce — via an upcoming API, the GRRR dataset, as well a new expert data layer on Flood Hub with close to 250,000 forecast points of our Flood Forecasting model, spread over 150 countries. 🕰️ Making historical datasets of our flood forecasting model available, to help researchers understand and potentially reduce the impact of devastating floods. Check out this blog from Yossi Matias for more information https://lnkd.in/ePYJQ-qN