Democratizing climate knowledge with tech

Detecting Floods Without Prior Knowledge: A Game-Changer for Resilient Infrastructure One of the biggest challenges in climate adaptation is timely flood detection—especially in places where monitoring infrastructure is sparse, or where floods occur in unexpected locations and times. Dev Patel’s recent work offers a powerful new methodology: detecting floods using satellite imagery and machine learning without needing to know where or when the flood occurred in advance. Instead of relying on pre-defined geographies or alerts, this approach trains algorithms to identify flood signatures—like rapid changes in surface water—in daily satellite data across large areas. It systematically detects flooding events in a way that is scalable, fast, and replicable. Why does this matter?    •   It democratizes flood monitoring—even regions without sensors or dense data networks can benefit.    •   It supports rapid response—aid agencies and governments can act faster when detection is automated.    •   It enables better planning—historic flood detection can improve risk maps, insurance models, and infrastructure design. For practitioners, the key takeaway is this: if you can access daily satellite imagery (e.g., from Planet or Sentinel), you can start building similar detection systems using open-source tools. Patel’s documentation offers clear steps—from preprocessing to validation—that others can build on. This is exactly the kind of methodological innovation that needs to scale globally. If you’re working on climate resilience, urban infrastructure, or disaster response—this is worth your time.

Charting the Future of Earth Observation: Technology Innovation for Climate Intelligence Executive Summary: This report, “Charting the Future of Earth Observation: Technology Innovation for Climate Intelligence", explores the critical role of #earthobservation (#EO) technologies in addressing #climatechange. As #climaterisks escalate, EO #innovations are transforming raw #data into actionable #climateinsights, enabling timely responses to disasters and long-term climate resilience planning. Key Findings: 1. Enhanced Sensing and Real-Time Data: - Advances in satellite sensors offer improved global coverage, resolution, and frequency of #datacollection. This enables faster detection of climate-related events like wildfires and floods - Real-time #analysis using #AI and #machinelearning (#ML) has drastically reduced data processing times. Previously, damage assessments took weeks; now they can be completed in hours, enhancing disaster response capabilities. 2. Evolution of Satellite Technology: - EO satellites are evolving in two directions: miniaturized small satellites and larger, more powerful platforms. Smaller satellites increase data access, while larger ones support advanced sensors for high-resolution monitoring. - New developments like the Landsat Next mission, planned for 2030, will further enhance sensing capabilities by collecting 2-3 times more spectral data than current missions 3. Climate Forecasting and Adaptation: - ML-based #climatemodels now offer high-resolution forecasting with increased speed and accuracy. For example, ML models can provide weather predictions up to 1,000 times faster than traditional models - These improvements help communities prepare for extreme weather, improving resilience through proactive planning. 4. Democratising Climate Data: - Efforts to make climate data accessible to a wider range of users, from #policymakers to #localcommunities, are underway. Tools like #augmentedreality (#AR), #virtualreality (#VR), and open data cubes make complex #EOdata more understandable and actionable Recommendations: - Increase Accessibility: Expand EO data access to vulnerable communities, ensuring climate insights are available for informed decision-making. - Invest in Technology Pipelines: Further #innovation in AI, ML, and satellite edge computing will enhance EO’s impact on #climateintelligence. In conclusion, the integration of #EOtechnologies with AI and #machinelearning is revolutionizing climate intelligence, providing #governments, #businesses, and communities with the tools needed to mitigate #climaterisks and enhance resilience. [ Researched and reported by World Economic Forum, and MIT Media Lab ]

The Extreme Weather Era: How AI is Helping Build Resilience? Extreme weather events like devastating droughts, hurricanes, and floods are becoming our new normal. With climate change intensifying these disasters, we urgently need better tools to prepare and respond. Traditional simulation methods for forecasting weather at a local, kilometer-scale level are too complex and computationally expensive. Plus, weather is a chaotic system with inherent uncertainties, requiring multiple forecasts or "ensembles" to predict probabilities. This is where AI will play a critical role. Cutting-edge AI models can now generate highly localized, kilometer-scale weather predictions far more quickly and efficiently than conventional methods. And their ability to produce "ensemble" forecasts gives us a clearer picture of potential outcomes and uncertainties. NVIDIA has pioneered this approach with their "Earth-2" platform - a powerful digital twin of our planet. It uses a state-of-the-art generative AI model called CorrDiff to create super-resolution images over 1,000 times faster and vastly more energy-efficiently than current numerical forecasting models. This "AI downscaling" technique is like the concept of super-resolution in image processing - generating finer-grained data from coarser inputs. And the probabilistic nature of generative AI allows for capturing multiple possible future scenarios, not just one deterministic prediction. From assessing climate risks for finance to optimizing energy production and distribution, and aiding disaster response efforts - AI downscaling could transform how we adapt to and build resilience against extreme weather impacts. At its core, innovations like Earth-2 democratize access to sophisticated climate science capabilities across businesses, governments, and society. As we navigate this era of intensifying climate extremes, harnessing AI will be crucial for developing data-driven strategies to create a more resilient world. --- I research and simplify climate change, energy, and decarbonization topics. If you find these insights valuable and informative, follow me, Lalit Patidar, for more content like this. Image Source: NVIDIA #climatechage #ai #weather #forecasting #simulation ##GenerativeAI

The most revolutionary thing about drones isn't the technology – it's who gets to use it Remember when environmental monitoring belonged only to those who could afford satellites or wait months for an orbital pass? That era is over. Drones have democratized data collection in ways that transform how communities interact with their environments. No longer must local groups wait for outside experts with expensive equipment to tell them what's happening in their own backyards. I've watched people in developing countries across the Global South capture high-quality environmental data on their own terms, when and where they need it. They're not passive recipients of environmental information – they're active creators of it. This shift represents something profound: the end of information inequality in environmental monitoring. Communities facing immediate threats from climate change, deforestation, or pollution can document these changes themselves rather than hoping someone with "proper credentials" notices their struggle. The most powerful aspect of drone technology isn't the sensors or the flight capabilities – it's the transfer of power from institutions to individuals who care most about their local ecosystems.

CNN recently wrote an article about FortyGuard titled: Urban areas are getting hotter. “A startup from one of the world’s hottest cities wants to help.” The title captures a lot, but here’s what it misses. From the start, we realized that detailed, actionable information about urban temperatures was largely nonexistent. The article rightly frames this as a “data distribution” problem, noting: “Although urban heat is gaining more attention, most weather stations haven’t been built to specifically address the issue, and they’re often located at an airport or atop a hillside – areas that aren’t representative of the temperature on a city sidewalk.” At FortyGuard one of our most important early decisions was to focus on the right data partnerships—long-term, high-value collaborations. In the AI space, there’s often a disconnect: some companies have data but no foundational model to make it meaningful, while others focus on building models without the right data. Both approaches miss the point. Without the right foundation, you can’t truly solve the problem. The article introduces a term we hadn’t used before—Urban Heat Tech. I love it. While many people think of FortyGuard as a solution for architects to design cooler cities, we see it as something much bigger: a technological leap toward democratizing temperature intelligence. Heat isn’t just a climate issue—it’s a technology challenge. It affects EV battery durability, energy efficiency, logistics, investments, and workplace safety standards. We’re building AI that delivers precision, affordability, and speed, enabling solutions for all of this and more. Our goal is not just to “measure heat” but to redefine how we address it across industries. The full article is here: https://lnkd.in/dxkyzbE5