ALOE http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=v87zNKO aloe-noreply@dfki.uni-kl.de Martin http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3 This feed contains recent activities on resource "Simulating urban greening before anyone picks up a shovel" in [ALOE] http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=v87zNKO ALOE http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=v87zNKO aloe-noreply@dfki.uni-kl.de Martin http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3 http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=v87zNKO:6:2026-01-12 21:05:32.0 January 12, 2026 9:05:00 PM CET: User <a href="http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3"><b>Martin</b></a> contributed resource <a href="http://aloe-project.de/AloeView/action/resourceDetailed?resourceId=v87zNKO"><b>Simulating urban greening before anyone picks up a shovel</b></a> with the following metadata:<br/><br/><i>associatedDate</i>: <b></b><br/><i>creator</i>: <b></b><br/><i>description</i>: <b>As cities get hotter, planners need good temperature data to figure out where to focus their cooling efforts. But there&rsquo;s a bit of a problem. Traditional machine learning models need tons of local data to make accurate predictions, and that data often doesn&rsquo;t exist for the cities that need it most. A new study from IBM Research tests whether geospatial foundation models (GFMs) can fill this gap.</b><br/><i>language</i>: <b></b><br/><i>license</i>: <b></b><br/><i>publisher</i>: <b></b><br/><i>resourceType</i>: <b>resource-bookmark</b><br/><i>rightsHolder</i>: <b></b><br/><i>tags</i>: <b>craftingfutures</b><br/><i>title</i>: <b>Simulating urban greening before anyone picks up a shovel</b><br/>