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A new AI model could help track and adapt to climate change - IBM Research
<!DOCTYPE html><html lang="en-US"><head><meta charSet="utf-8"/><meta name="twitter:label1" content="Written by"/><meta name="twitter:data1" content="Sriram Raghavan"/><meta name="twitter:label2" content="Est. reading time"/><meta name="twitter:data2" content="4 minutes"/><title>A new AI model could help track and adapt to climate change - IBM Research</title><meta name="description" content="From IBM’s collaboration with NASA, this model converts satellite data into maps of floods, fires, and other land changes that hint at our planet’s future."/><meta name="robots" content="index,follow"/><meta name="viewport" content="width=device-width,initial-scale=1"/><link rel="canonical" href="https://research.ibm.com/blog/geospatial-models-nasa-ai"/><link rel="icon" href="//www.ibm.com/favicon.ico"/><meta name="dcterms.date" content="2021-02-09"/><meta name="dcterms.rights" content="© Copyright IBM Corp. 2021"/><meta name="geo.country" content="US"/><meta name="google-site-verification" content="O1nsbg1J1iAeYJK6HneffI0_RiLebmSPxfs5ESYNnwI"/><script type="application/ld+json">{"@type":"Article","author":[{"@type":"Person","name":"Sriram Raghavan","url":"https://research.ibm.com/blog?author=sriram-raghavan"},{"@type":"Person","name":"Christina Shim","url":"https://research.ibm.com/blog?author=christina-shim"}],"publisher":{"@type":"Organization","name":"IBM","url":"https://ibm.com/","logo":{"@type":"ImageObject","url":"https://research.ibm.com/ibm-logo.svg","width":"576","height":"576"}},"headline":"Earth’s climate is changing. IBM’s new geospatial foundation model could help track and adapt to a new landscape","datePublished":"2023-05-09T10:00:00.000Z","dateModified":"2023-07-05T18:17:17.807Z","description":"Built from IBM’s collaboration with NASA, the watsonx.ai model is designed to convert satellite data into high-resolution maps of floods, fires, and other landscape changes to reveal our planet’s past and hint at its future.","image":{"@type":"ImageObject","url":"https://research-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud/15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif","width":"1830","height":"1029"},"@context":"https://schema.org","url":"https://research.ibm.com/blog/geospatial-models-nasa-ai","mainEntityOfPage":{"@type":"WebPage","@id":"https://research.ibm.com/"}}</script><meta property="og:title" content="A new AI model could help track and adapt to climate change"/><meta property="og:type" content="article"/><meta property="og:url" 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class="_879tr aVLxf"><header class="KHAZu _LX6c"><div class="bEKAI"><div class="_4k_Q1"><a class="xHBSN" name="pageStart" style="--top:0px;--top-offset:0rem"></a></div><span class="_2xxN"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M26,4h-4V2h-2v2h-8V2h-2v2H6C4.9,4,4,4.9,4,6v20c0,1.1,0.9,2,2,2h20c1.1,0,2-0.9,2-2V6C28,4.9,27.1,4,26,4z M26,26H6V12h20 V26z M26,10H6V6h4v2h2V6h8v2h2V6h4V10z"></path></svg><time dateTime="2023-05-09T10:00:00.000Z">09 May 2023</time></span><span class="_8qSvG"><div class="_5Cpod"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M19 10H26V12H19zM19 15H26V17H19zM19 20H26V22H19z"></path><path d="M28,5H4A2.002,2.002,0,0,0,2,7V25a2.0023,2.0023,0,0,0,2,2H28a2.0027,2.0027,0,0,0,2-2V7A2.0023,2.0023,0,0,0,28,5ZM4,7H15V25H4ZM17,25V7H28l.002,18Z"></path></svg>News</div></span><span class="SnZqp"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M16,30A14,14,0,1,1,30,16,14,14,0,0,1,16,30ZM16,4A12,12,0,1,0,28,16,12,12,0,0,0,16,4Z"></path><path d="M20.59 22L15 16.41 15 7 17 7 17 15.58 22 20.59 20.59 22z"></path></svg>4<!-- --> minute read</span></div><h1 class="pWztW">Earth’s climate is changing. IBM’s new geospatial foundation model could help track and adapt to a new landscape</h1><div class="_86szE RUxl8"><p class="eBWTD EihHw DMxM7">Built from IBM’s collaboration with NASA, the watsonx.ai model is designed to convert satellite data into high-resolution maps of floods, fires, and other landscape changes to reveal our planet’s past and hint at its future.</p></div><figure class="plBab"><div class="Q4G3p"><span style="box-sizing:border-box;display:block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:absolute;top:0;left:0;bottom:0;right:0"><img alt="15-RYP---Hurricane-Ida-Flooding-2021-16x9-small-v2.gif" sizes="(min-width: 99rem) calc((99rem - 50rem) * 0.6875 + 30rem), (min-width: 82rem) calc((100vw - 50rem) * 0.6875 + 30rem), (min-width: 66rem) calc((100vw - 49rem) * 0.75 + 33rem), (min-width: 42rem) calc((100vw - 27rem) * 1 + 21rem), calc((100vw - 5rem) * 1 + 3rem)" srcSet="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=16&q=75 16w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=32&q=75 32w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=48&q=75 48w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=64&q=75 64w, 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https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=1200&q=75 1200w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=1920&q=75 1920w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=2048&q=75 2048w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=3840&q=75 3840w" src="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2F15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif&w=3840&q=75" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover"/></span></div><figcaption>NASA satellite imagery of Hurricane Ida flooding in 2021.</figcaption></figure></header><div class="twuQz"><div class="tgS3k"><div class="_86szE u4OVN"><p class="eBWTD EihHw DMxM7">Built from IBM’s collaboration with NASA, the watsonx.ai model is designed to convert satellite data into high-resolution maps of floods, fires, and other landscape changes to reveal our planet’s past and hint at its future.</p></div></div><div class="KfgL5 Xx_eB"><div class="tgS3k"><p class="eBWTD EihHw">Nearly a <a href="https://earthobservatory.nasa.gov/images/148866/research-shows-more-people-living-in-floodplains" class="bx--link _7_4F7 bx--link--inline">quarter of the world’s population</a> now lives in a flood zone, and that number is expected to climb as rising seas and heavier storms triggered by a changing climate put more people at risk. The ability to accurately map flooding events can be key to not only protecting people and property now but steering development to less-risky areas in the future.</p></div><div class="tgS3k"><p class="eBWTD">A new geospatial foundation model unveiled today by IBM is designed to enable first steps toward this goal by converting NASA’s satellite observations into customized maps of natural disasters and other environmental changes. The model, part of IBM’s watsonx.ai geospatial offering, is planned to be available in preview to IBM clients through (EIS) <a href="https://www.ibm.com/products/environmental-intelligence-suite" class="bx--link _7_4F7 bx--link--inline">IBM Environmental Intelligence Suite</a> during the second half of this year. Potential applications include helping to estimate climate-related risks to crops, buildings, and other infrastructure, valuing and monitoring forests for carbon-offset programs, and developing predictive models to help enterprises create strategies to mitigate and adapt to climate change.</p></div><div class="tgS3k"><p class="eBWTD">As part of a Space Act Agreement with NASA, IBM <a href="https://research.ibm.com/blog/ibm-nasa-foundation-models" class="bx--link _7_4F7 bx--link--inline">set out</a> just four months ago to build the first-ever foundation model for analyzing geospatial data. Foundation models <a href="https://research.ibm.com/blog/what-are-foundation-models" class="bx--link _7_4F7 bx--link--inline">have revolutionized</a> natural language processing (NLP) by allowing developers to train one model on raw text, and with extra training, customize the model for other NLP tasks. Previously, users had to train a new model for each task, which required extensive data curation and compute. Rather than train a foundation model on words, IBM Research taught a model to understand satellite images. Researchers pre-trained NASA’s <a href="https://hls.gsfc.nasa.gov/" class="bx--link _7_4F7 bx--link--inline">Harmonized Landsat Sentinel-2</a> (HLS-2) data. The HLS data provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard the European Union’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement helps enable global observations of the land every two to three days at 30 meter spatial resolution.</p></div><div class="tgS3k"><p class="eBWTD">They then fed the model hand-labeled examples to teach it to recognize things like the extent of historic floods and fire burn scars, as well as changes in land-use and forest biomass.</p></div><div class="tgS3k"><p class="eBWTD">Using the model is designed to be as simple as selecting a region, a mapping task, and a set of dates. For example, if a user types “Port-de-Lanne, France” into the search bar and selects the dates December 13-15, 2019, the model highlights in pink how far the flood waters extended. Users can overlay other datasets to see where crops or buildings were inundated. These visualizations can help with future planning during similar disaster scenarios: they provide information that could help mitigate flood impacts, inform insurance and risk management decisions, plan infrastructure, respond to disasters, and protect the environment.</p></div><div class="tgS3k"><p class="eBWTD">IBM built the model on a <a href="https://arxiv.org/pdf/2111.06377.pdf" class="bx--link _7_4F7 bx--link--inline">masked autoencoder</a> for processing video and adapted it to satellite footage. To teach the model to understand sequences of images unfolding through time, researchers blanked out parts of each image and had the model reconstruct it. The more images it reconstructed, the better it became at understanding how they related to each other. They then fine-tuned the model for specific tasks like classifying and segmenting images. This fine-tuning workflow was based on <a href="https://pytorch.org/" class="bx--link _7_4F7 bx--link--inline">PyTorch</a> with an enhanced segmentation library that allowed researchers to work with spatiotemporal data.</p></div><div class="tgS3k"><p class="eBWTD">To improve the model’s efficiency, researchers also shrank the size of satellite images, allowing them to process the data in smaller chunks and get away with fewer GPUs. They then trained the model using over 5,000 GPU hours on <a href="https://research.ibm.com/blog/AI-supercomputer-Vela-GPU-cluster" class="bx--link _7_4F7 bx--link--inline">IBM Research’s Vela supercomputer.</a></p></div><div class="tgS3k"><p class="eBWTD">Early results look promising. In tests, researchers saw a 15% improvement in accuracy compared to state-of-the-art deep learning models for mapping <a href="https://ieeexplore.ieee.org/document/10020911" class="bx--link _7_4F7 bx--link--inline">floods</a> and burn scars from <a href="https://arxiv.org/abs/1706.05587" class="bx--link _7_4F7 bx--link--inline">fires</a>, using half as much labeled data. IBM estimates this model could speed up geospatial analysis by three to four times, and help reduce the amount of data cleaning and labeling required in training a traditional deep-learning model.</p></div><div class="nj7Qi"><figure class="saf3g nYXXU"><div class="kPuAT"><div class="pipB_"><div id="" class="OOUqj"></div></div></div></figure></div><div class="tgS3k"><p class="eBWTD">We see how this technology can be applicable to businesses as they look for easier and faster ways to analyze and draw insights from climate data. For example, disaster response teams could use a solution like this to help prepare for a fire impacting residential housing. Or, this solution can help a large consumer goods company to better understand macro trends like climate change, severe weather or geopolitical risk that impact where they are currently buying their raw materials from and where they might want to consider purchasing those resources in the future. It can also help a large agribusiness to better measure, track and mitigate the impact of their farming practices on the local environments and surrounding communities by better understanding soil degradation, water conservation activities or how to reduce pollution caused by run-off from fields to local bodies of water.</p></div><div class="tgS3k"><p class="eBWTD">IBM is also involved in additional projects around geospatial mapping, working with <a href="https://www.ibm.com/blog/how-ibm-and-esri-are-working-together-to-map-a-more-sustainable-future/" class="bx--link _7_4F7 bx--link--inline">Esri</a> and other companies to help organizations uncover a richer spatial context of a business’s assets and operations.</p></div><div class="tgS3k"><h2 class="ec9zY m3roq"><a class="xHBSN" name="-try-out-the-latest-in-geospatial-ai" style="--top:0px;--top-offset:0rem"></a>Try out the latest in geospatial AI</h2></div><div class="tgS3k"><p class="eBWTD">The preview version of the base geospatial foundation model and suite of fine-tuned models, all running on watsonx.ai, is planned to be available through <a href="https://www.ibm.com/products/environmental-intelligence-suite" class="bx--link _7_4F7 bx--link--inline">IBM EIS</a>.<sup class="t9X_p"><a href="/blog/geospatial-models-nasa-ai#-fn-1" class="bx--link _7_4F7 bx--link--inline" id="-fnref-1" data-footnote-ref="" aria-describedby="-references-header">1</a></sup> We believe this release will be invaluable for data scientists, developers, researchers, and students.</p></div><div class="tgS3k"><p class="eBWTD">The preview trial makes these two sets of tools available within EIS. It includes APIs for inference fine-tuned models to drive developer engagement. We also offer sample solutions that apply these models, create triggers, and leverage work queues to drive a downstream operational system.</p></div><div class="tgS3k"><p class="eBWTD DMxM7">If you’re interested in joining the waitlist to test out the preview version within IBM EIS, <a href="https://www.ibm.com/account/reg/us-en/signup?formid=urx-52142" class="bx--link _7_4F7 bx--link--inline">click here</a>.</p></div></div><div class="Torc3"><div class="_1UAwl"><a class="_3TJyT AABvn" href="https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-53237"><div class="i3ES9 IyxDB">Subscribe to our Future Forward newsletter and stay up to date on the latest research news<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="32" height="32" viewBox="0 0 32 32" aria-hidden="true" class="Ptkl_"><path d="M18 6L16.57 7.393 24.15 15 4 15 4 17 24.15 17 16.57 24.573 18 26 28 16 18 6z"></path></svg></div><div class="_7vYZG 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id="references" class="TQy4z izDYk"><h2 class="DGVSD" id="references-header">References<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M16 22L6 12 7.4 10.6 16 19.2 24.6 10.6 26 12z"></path></svg></h2><div class="QKy36"><ol class="_1Dpdk hWcbR _1aPn q61x7 sDqmS"><li id="-fn-1"> <p class="eBWTD EihHw DMxM7">IBM’s plans, directions, and intentions may change or be withdrawn at any time at IBM’s discretion without notice. Information about potential future products and improvements is provided to give a general idea of IBM’s goals and objectives and should not be used in making a purchase decision. IBM is not obligated to provide any material, code, or functionality based on this information. This statement replaces all prior statements on this topic. <a href="/blog/geospatial-models-nasa-ai#-fnref-1" class="bx--link data-footnote-backref _7_4F7 bx--link--inline" data-footnote-backref="" aria-label="Back to reference 1">↩</a></p> </li></ol></div></section></div></div><footer><div class="Hw_u8 YqPtO"><div class="_3dg1Y"><div class="ABazV"><article class="px54D dQjMV"><h2 class="aIhdd"><a class="QEnIV" href="/blog/the-short-nov-26"><span class="yYIUz">Accelerating discoveries with new AI tools, powerful chips — and turkey</span><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="32" height="32" viewBox="0 0 32 32" aria-hidden="true" class="Hubs8"><path d="M18 6L16.57 7.393 24.15 15 4 15 4 17 24.15 17 16.57 24.573 18 26 28 16 18 6z"></path></svg></a></h2><div class="_1wKk7"><span 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d="M30,28.58l-3.11-3.11a6,6,0,1,0-1.42,1.42L28.58,30ZM22,26a4,4,0,1,1,4-4A4,4,0,0,1,22,26Z"></path></svg>Explainer</div><div class="fmKT7"><div class=""><div class="hr37V">Mike Murphy, Peter Hess, and Kim Martineau</div><div><time dateTime="2024-11-26T13:00:00.000Z">26 Nov 2024</time></div></div></div><ul class="XBsQU tIdYx H_THJ pFSjT _2bQNq sOAT8" style="--gap-sm:0.5rem;--gap-md:0.5rem;--gap-lg:0.5rem;--gap-xlg:0.5rem;--gap-max:0.5rem"><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="post-3925--tag-5" href="/artificial-intelligence"><span title="AI">AI</span></a></li><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="post-3925--tag-8" href="/topics/ai-hardware"><span title="AI Hardware">AI Hardware</span></a></li><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="post-3925--tag-36" href="/topics/exploratory-science"><span title="Exploratory Science">Exploratory Science</span></a></li></ul></article><article class="px54D dQjMV"><h2 class="aIhdd"><a class="QEnIV" href="/blog/patcid-tool-for-accelerating-materials-discovery"><span class="yYIUz">A new tool for accelerating the discovery of new materials</span><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="32" height="32" viewBox="0 0 32 32" aria-hidden="true" class="Hubs8"><path d="M18 6L16.57 7.393 24.15 15 4 15 4 17 24.15 17 16.57 24.573 18 26 28 16 18 6z"></path></svg></a></h2><div class="_1wKk7"><span style="box-sizing:border-box;display:block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:absolute;top:0;left:0;bottom:0;right:0"><img alt="On the left is an unlabeled molecular structure diagram. On the right is a stack of three patent documents, and the top one has several molecular structures, including the one shown on the left. That one is highlighted in green." src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover"/><noscript><img alt="On the left is an unlabeled molecular structure diagram. On the right is a stack of three patent documents, and the top one has several molecular structures, including the one shown on the left. That one is highlighted in green." loading="lazy" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover" sizes="(min-width: 99rem) calc((99rem - 50rem) * 0.25 + 9rem), (min-width: 82rem) calc((100vw - 50rem) * 0.25 + 9rem), (min-width: 66rem) calc((100vw - 49rem) * 0.5 + 21rem), (min-width: 42rem) calc((100vw - 27rem) * 0.5 + 9rem), calc((100vw - 5rem) * 1 + 3rem)" srcSet="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FAutomated_materials_discovery_f_55d81a2877.png&w=16&q=85 16w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FAutomated_materials_discovery_f_55d81a2877.png&w=32&q=85 32w, 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src="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FAutomated_materials_discovery_f_55d81a2877.png&w=3840&q=85"/></noscript></span></div><div class="_5Cpod _6qhNt"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M22 24H18V22h4V18h2v4A2.0021 2.0021 0 0122 24zM10 14H8V10a2.0022 2.0022 0 012-2h4v2H10z"></path><path d="M28,8H24V4a2.0023,2.0023,0,0,0-2-2H4A2.0023,2.0023,0,0,0,2,4V22a2.0023,2.0023,0,0,0,2,2H8v4a2.0023,2.0023,0,0,0,2,2H28a2.0023,2.0023,0,0,0,2-2V10A2.0023,2.0023,0,0,0,28,8Zm0,20H10V24h4V22H10V18H8v4H4V4H22V8H18v2h4v4h2V10h4Z"></path></svg>Research</div><div class="fmKT7"><div class=""><div class="hr37V">Peter Hess</div><div><time dateTime="2024-11-20T12:30:00.000Z">20 Nov 2024</time></div></div></div><ul class="XBsQU tIdYx H_THJ pFSjT _2bQNq sOAT8" style="--gap-sm:0.5rem;--gap-md:0.5rem;--gap-lg:0.5rem;--gap-xlg:0.5rem;--gap-max:0.5rem"><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="post-3921--tag-90" href="/topics/accelerated-discovery"><span title="Accelerated Discovery">Accelerated Discovery</span></a></li><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="post-3921--tag-5" href="/artificial-intelligence"><span title="AI">AI</span></a></li><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="post-3921--tag-78" href="/topics/materials-discovery"><span title="Materials Discovery">Materials Discovery</span></a></li><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="post-3921--tag-6" href="/topics/science"><span title="Science">Science</span></a></li></ul></article><article class="px54D dQjMV"><h2 class="aIhdd"><a class="QEnIV" href="/blog/Granite-adapter-experiments"><span class="yYIUz">IBM Granite has new experimental features for developers to test</span><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="32" height="32" viewBox="0 0 32 32" aria-hidden="true" class="Hubs8"><path d="M18 6L16.57 7.393 24.15 15 4 15 4 17 24.15 17 16.57 24.573 18 26 28 16 18 6z"></path></svg></a></h2><div class="_1wKk7"><span style="box-sizing:border-box;display:block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:absolute;top:0;left:0;bottom:0;right:0"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover"/><noscript><img loading="lazy" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover" sizes="(min-width: 99rem) calc((99rem - 50rem) * 0.25 + 9rem), (min-width: 82rem) calc((100vw - 50rem) * 0.25 + 9rem), (min-width: 66rem) calc((100vw - 49rem) * 0.5 + 21rem), (min-width: 42rem) calc((100vw - 27rem) * 0.5 + 9rem), calc((100vw - 5rem) * 1 + 3rem)" srcSet="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FGranite_Keynote_cover_4_2x_7dcae38026.png&w=16&q=85 16w, 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src="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FGranite_Keynote_cover_4_2x_7dcae38026.png&w=3840&q=85"/></noscript></span></div><div class="_5Cpod _6qhNt"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M19 10H26V12H19zM19 15H26V17H19zM19 20H26V22H19z"></path><path d="M28,5H4A2.002,2.002,0,0,0,2,7V25a2.0023,2.0023,0,0,0,2,2H28a2.0027,2.0027,0,0,0,2-2V7A2.0023,2.0023,0,0,0,28,5ZM4,7H15V25H4ZM17,25V7H28l.002,18Z"></path></svg>News</div><div class="fmKT7"><div class=""><div class="hr37V">Kim Martineau</div><div><time dateTime="2024-11-19T17:30:00.000Z">19 Nov 2024</time></div></div></div><ul class="XBsQU tIdYx H_THJ pFSjT _2bQNq sOAT8" style="--gap-sm:0.5rem;--gap-md:0.5rem;--gap-lg:0.5rem;--gap-xlg:0.5rem;--gap-max:0.5rem"><li class="iHbnT vRTEX l8Im1 er023 EOn_C 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type="application/json">{"props":{"pageProps":{"initialApolloState":{"UploadFile:3594":{"__typename":"UploadFile","id":"3594","alternativeText":"15-RYP---Hurricane-Ida-Flooding-2021-16x9-small-v2.gif","url":"https://research-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud/15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_v2_302ab259b2.gif","width":1830,"height":1029,"caption":"NASA satellite imagery of Hurricane Ida flooding in 2021."},"UploadFile:3593":{"__typename":"UploadFile","alternativeText":"15-RYP---Hurricane-Ida-Flooding-2021-16x9-small-still.gif","height":1029,"id":"3593","url":"https://research-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud/15_RYP_Hurricane_Ida_Flooding_2021_16x9_small_still_eede6ee17f.gif","width":1830},"ComponentSharedSeo:950":{"__typename":"ComponentSharedSeo","canonicalURL":null,"metaDescription":"From IBM’s collaboration with NASA, this model converts satellite data into maps of floods, fires, and other land changes that hint at our planet’s future.","id":"950","metaImage":{"__ref":"UploadFile:3593"},"metaRobots":null,"metaSocial":[],"metaTitle":"A new AI model could help track and adapt to climate change"},"BlogCta:2":{"__typename":"BlogCta","id":"2","text":"Subscribe to our Future Forward newsletter and stay up to date on the latest research news","url":"https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-53237","button_text":"Subscribe to our newsletter"},"Tag:5":{"__typename":"Tag","id":"5","name":"AI","topic":{"__typename":"FocusArea","slug":"artificial-intelligence"}},"Tag:167":{"__typename":"Tag","id":"167","name":"Foundation Models","topic":{"__typename":"Team","slug":"foundation-models"}},"BlogAuthor:1386":{"__typename":"BlogAuthor","id":"1386","name":"Sriram Raghavan","is_ibm":true,"ibmer":{"__typename":"IbmerValue","node":{"__typename":"Ibmer","slug":"sriram-raghavan"}},"slug":"sriram-raghavan"},"BlogAuthor:1392":{"__typename":"BlogAuthor","id":"1392","name":"Christina Shim","is_ibm":true,"ibmer":{"__typename":"IbmerValue","node":null},"slug":"christina-shim"},"BlogCategory:1":{"__typename":"BlogCategory","id":"1","name":"News","slug":"news"},"BlogPost:1612":{"__typename":"BlogPost","id":"1612","title":"Earth’s climate is changing. IBM’s new geospatial foundation model could help track and adapt to a new landscape","slug":"geospatial-models-nasa-ai","read_duration":null,"publish_at":"2023-05-09T10:00:00.000Z","cover_background":null,"updatedAt":"2023-07-05T18:17:17.807Z","cover_image":{"__ref":"UploadFile:3594"},"cover_image_reduced_motion":null,"cover_video":null,"cover_show_at_top":false,"cover_show_caption":true,"cover_is_full_width":false,"cover_video_self_hosted":null,"lead_paragraph":"Built from IBM’s collaboration with NASA, the watsonx.ai model is designed to convert satellite data into high-resolution maps of floods, fires, and other landscape changes to reveal our planet’s past and hint at its future.","summary":null,"content":"Nearly a [quarter of the world’s population](https://earthobservatory.nasa.gov/images/148866/research-shows-more-people-living-in-floodplains) now lives in a flood zone, and that number is expected to climb as rising seas and heavier storms triggered by a changing climate put more people at risk. The ability to accurately map flooding events can be key to not only protecting people and property now but steering development to less-risky areas in the future.\n\nA new geospatial foundation model unveiled today by IBM is designed to enable first steps toward this goal by converting NASA’s satellite observations into customized maps of natural disasters and other environmental changes. The model, part of IBM’s watsonx.ai geospatial offering, is planned to be available in preview to IBM clients through (EIS) [IBM Environmental Intelligence Suite](https://www.ibm.com/products/environmental-intelligence-suite) during the second half of this year. Potential applications include helping to estimate climate-related risks to crops, buildings, and other infrastructure, valuing and monitoring forests for carbon-offset programs, and developing predictive models to help enterprises create strategies to mitigate and adapt to climate change.\n\nAs part of a Space Act Agreement with NASA, IBM [set out](https://research.ibm.com/blog/ibm-nasa-foundation-models) just four months ago to build the first-ever foundation model for analyzing geospatial data. Foundation models [have revolutionized](https://research.ibm.com/blog/what-are-foundation-models) natural language processing (NLP) by allowing developers to train one model on raw text, and with extra training, customize the model for other NLP tasks. Previously, users had to train a new model for each task, which required extensive data curation and compute.\n \nRather than train a foundation model on words, IBM Research taught a model to understand satellite images. Researchers pre-trained NASA’s [Harmonized Landsat Sentinel-2](https://hls.gsfc.nasa.gov/) (HLS-2) data. The HLS data provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard the European Union’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement helps enable global observations of the land every two to three days at 30 meter spatial resolution.\n\nThey then fed the model hand-labeled examples to teach it to recognize things like the extent of historic floods and fire burn scars, as well as changes in land-use and forest biomass. \n\nUsing the model is designed to be as simple as selecting a region, a mapping task, and a set of dates. For example, if a user types “Port-de-Lanne, France” into the search bar and selects the dates December 13-15, 2019, the model highlights in pink how far the flood waters extended. Users can overlay other datasets to see where crops or buildings were inundated. These visualizations can help with future planning during similar disaster scenarios: they provide information that could help mitigate flood impacts, inform insurance and risk management decisions, plan infrastructure, respond to disasters, and protect the environment.\n\nIBM built the model on a [masked autoencoder](https://arxiv.org/pdf/2111.06377.pdf) for processing video and adapted it to satellite footage. To teach the model to understand sequences of images unfolding through time, researchers blanked out parts of each image and had the model reconstruct it. The more images it reconstructed, the better it became at understanding how they related to each other. They then fine-tuned the model for specific tasks like classifying and segmenting images. This fine-tuning workflow was based on [PyTorch](https://pytorch.org/) with an enhanced segmentation library that allowed researchers to work with spatiotemporal data.\n\nTo improve the model’s efficiency, researchers also shrank the size of satellite images, allowing them to process the data in smaller chunks and get away with fewer GPUs. They then trained the model using over 5,000 GPU hours on [IBM Research’s Vela supercomputer.](https://research.ibm.com/blog/AI-supercomputer-Vela-GPU-cluster) \n\nEarly results look promising. In tests, researchers saw a 15% improvement in accuracy compared to state-of-the-art deep learning models for mapping [floods](https://ieeexplore.ieee.org/document/10020911) and burn scars from [fires](https://arxiv.org/abs/1706.05587), using half as much labeled data. IBM estimates this model could speed up geospatial analysis by three to four times, and help reduce the amount of data cleaning and labeling required in training a traditional deep-learning model.\n\n::youtube[]{id=9bU9eJxFwWc}\n\nWe see how this technology can be applicable to businesses as they look for easier and faster ways to analyze and draw insights from climate data. For example, disaster response teams could use a solution like this to help prepare for a fire impacting residential housing. Or, this solution can help a large consumer goods company to better understand macro trends like climate change, severe weather or geopolitical risk that impact where they are currently buying their raw materials from and where they might want to consider purchasing those resources in the future. It can also help a large agribusiness to better measure, track and mitigate the impact of their farming practices on the local environments and surrounding communities by better understanding soil degradation, water conservation activities or how to reduce pollution caused by run-off from fields to local bodies of water.\n\nIBM is also involved in additional projects around geospatial mapping, working with [Esri](https://www.ibm.com/blog/how-ibm-and-esri-are-working-together-to-map-a-more-sustainable-future/) and other companies to help organizations uncover a richer spatial context of a business’s assets and operations.\n\n## Try out the latest in geospatial AI\n\nThe preview version of the base geospatial foundation model and suite of fine-tuned models, all running on watsonx.ai, is planned to be available through [IBM EIS](https://www.ibm.com/products/environmental-intelligence-suite).[^1] We believe this release will be invaluable for data scientists, developers, researchers, and students. \n\nThe preview trial makes these two sets of tools available within EIS. It includes APIs for inference fine-tuned models to drive developer engagement. We also offer sample solutions that apply these models, create triggers, and leverage work queues to drive a downstream operational system. \n\nIf you’re interested in joining the waitlist to test out the preview version within IBM EIS, [click here](https://www.ibm.com/account/reg/us-en/signup?formid=urx-52142).\n\n[^1]: IBM’s plans, directions, and intentions may change or be withdrawn at any time at IBM’s discretion without notice. Information about potential future products and improvements is provided to give a general idea of IBM’s goals and objectives and should not be used in making a purchase decision. IBM is not obligated to provide any material, code, or functionality based on this information. 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