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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2212.14532/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2212.14532/feed.xml" rel="self" type="application/rss+xml" /><item><title>Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning — mentioned by ritwikgupta</title><link>https://news.ycombinator.com/item?id=36988412</link><guid isPermaLink="false">urn:hackerpapers:mention:2212.14532:36988412</guid><pubDate>Thu, 03 Aug 2023 16:54:21 GMT</pubDate><description>&lt;p&gt;This simply is not true! NASA and IBM need to do further literature review and rely less on press releases. There are larger foundation models for geospatial imagery available. Our pre-training method, Scale-MAE [0], has 323M parameters, makes encoders robust to changes in satellite imagery resolution, and is therefore trained on satellite imagery of all resolutions. Work out of SI Analytics [1] presents a 2.4B parameter transformers for satellite imagery. [0] https://arxiv.org/abs/2212.14532 [1] https://arxiv.org/abs/2304.05215&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2212.14532/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
