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helmets.html
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<!DOCTYPE html>
<html lang="en">
<head>
<title>Helmets Labeling Crops with Street2Sat</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css?family=Open+Sans:400,700,300" rel="stylesheet" type="text/css">
<link rel="icon" href="assets/NASA_Harvest_favicon.png">
<link rel="stylesheet" type="text/css" rel="noopener" target="_blank" href="main.css">
</head>
<body>
<!-- Header -->
<div id="navbar">
<a href="https://nasaharvest.github.io/">
<div id="logo">
<img src="assets/logo.png"/>
<div id="logotext">
<h1>NASA Harvest</h1>
<h3>Machine Learning</h3>
</div>
</div>
</a>
<div class="menu">
<a href="https://nasaharvest.github.io/">About</a>
<a href="https://nasaharvest.github.io/#sessions">Sessions</a>
<a href="https://nasaharvest.github.io/#profiles">Team</a>
</div>
</div>
<!-- Main -->
<div id="content50">
<h1>Helmets Labeling Crops using Street2Sat</h1>
<a href="#helmets">Helmets Data Collection</a>
|
<a href="#street2sat">Street2Sat Pipeline</a>
|
<a href="#publications">Publications</a>
|
<a href="#partners">Partners & Support</a>
<br>
<br>
<img src="assets/helmets-cover.png"/>
<p>
The Helmets Labeling Crops Project (“Helmets” for short) is developing
and applying innovative, scalable data collection approaches that can
inform machine learning (ML) tools to support higher-frequency crop-type
mapping. Our data processing platform, called Street2Sat, transforms
geo-tagged street-level images collected by Helmets GoPro cameras into a
set of labeled geo-referenced points on crop fields that can be paired with
satellite images for downstream tasks such as crop type mapping.
Checkout our <a href="https://github.com/nasaharvest/street2sat"> Street2Sat Github repository</a>.
<a href="mailto:[email protected]">[email protected]</a> to Request a Helmets Kit Application form.
</p>
<h1 id="helmets">Helmets Data Collection</h1>
<p>
To learn more about the data collection process check out the
<strong>Car and Motorbike Toolkit</strong> below:
<br>(use full screen for best view)
</p>
<iframe
src="https://docs.google.com/presentation/d/e/2PACX-1vQpP2f6dAcs_R0yrZ1dOpaKPQab0h-8BBLmwU-1cOfv462l6SMl5ng84A2HjBx4Qw/embed?start=false&loop=true&delayms=60000"
frameborder="0"
width="470"
height="650"
allowfullscreen="true"
mozallowfullscreen="true"
webkitallowfullscreen="true">
</iframe>
<h1 id="street2sat">Street2Sat Pipeline</h1>
<p>The goal of Street2Sat is to turn geo-referenced images acquired from roads
into geo-referenced labeled point samples with locations corresponding to
objects of interest in the images. The pipeline consists of data collection,
preprocessing, object detection, depth estimation, relocation, and
quality assessment/control (QA/QC).
</p>
<img src="assets/street2sat-pipeline.png"/>
<h1 id="publications">Publications</h1>
<p>
Paliyam, M., Nakalembe, C., Kerner, H. (2021). Street2Sat:
A Machine Learning Pipeline for Generating Ground-truth Geo-referenced
Labeled Datasets from Street-Level Images. <em>Proceedings of the
International Conference on Machine Learning (ICML) Workshops,
Tackling Climate Change with AI, </em>
<a href="https://www.climatechange.ai/papers/icml2021/74.html">link</a>
</p>
<p>
Manimurugan, S., Singaram, R., Nakalembe, C., and Kerner, H. (2022).
Geo-referencing crop labels from street-level images using Structure from Motion.
<em> Proceedings of the 73rd International Astronautical Congress (IAC), </em>
<a href="https://www.researchgate.net/publication/365360146_Geo-referencing_crop_labels_from_street-level_images_using_Structure_from_Motion">link.</a>
</p>
<h1 id="partners">Partners & Support</h1>
<p>
Helmets Labeling Crops Project is funded by <a href="https://www.lacunafund.org/">Lacuna Fund</a>.
The Lacuna Fund is an initiative co-founded by The Rockefeller Foundation, Google, and Canada’s
International Development Research Centre which aims to mobilize funding to support the development of high-quality
labeled datasets in low- and middle-income contexts. The first round of funding in the agricultural
AI for social good domain has now been awarded to several projects aimed at solving urgent regional
problems in African countries, working hand-in-hand with organizations across Africa. This effort is supported through
partnership with affiliate organizations across the NASA Harvest consortium:
</p>
<ul>
<li>The Regional Universities Forum for Capacity Building in Agriculture (RUFORUM)</li>
<li>The Regional Center for Mapping of Resources (RCMRD)</li>
<li>The Center for Earth Observation and Citizen Science (EOCS-IIASA)</li>
<li>Radiant Earth Foundation</li>
<li>The Eastern Africa Grain Council (EAGC)</li>
<li>Lutheran World Relief Mali (LWR)</li>
<li>Makerere University Kampala, Uganda (MUK)</li>
<li>Sokoine University of Agriculture Tanzania (SUA)</li>
</ul>
<img src="assets/LacunaFund.png" style="max-width: 600px"/>
</div>
</body>
</html>