<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>SAR on Deepak Nagaraj</title>
    <link>https://ndeepak.com/tags/sar/</link>
    <description>Recent content in SAR on Deepak Nagaraj</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en</language>
    <managingEditor>n.deepak@gmail.com (Deepak Nagaraj)</managingEditor>
    <webMaster>n.deepak@gmail.com (Deepak Nagaraj)</webMaster>
    <lastBuildDate>Fri, 07 May 2021 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://ndeepak.com/tags/sar/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Detecting irrigation with radar satellite and artificial intelligence</title>
      <link>https://ndeepak.com/posts/2021-05-07-sar-transfer-learning-irrigation/</link>
      <pubDate>Fri, 07 May 2021 00:00:00 +0000</pubDate><author>n.deepak@gmail.com (Deepak Nagaraj)</author>
      <guid>https://ndeepak.com/posts/2021-05-07-sar-transfer-learning-irrigation/</guid>
      <description>I&amp;rsquo;ve previously talked about detecting irrigated croplands in this blog. I used classical machine learning techniques in it, i.e. we decide what features to use, we fit a model or an ensemble of models, we predict. Modern machine learning, or what the press calls &amp;ldquo;Artificial Intelligence&amp;rdquo;, has gone way beyond it.&#xA;In this post, I want to talk about a different approach to detect irrigated croplands. This method gave 95% test-set accuracy on a random sample of 2000 pictures from California.</description>
    </item>
  </channel>
</rss>
