A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://www.nature.com/articles/s41598-021-86650-z.pdf?error=cookies_not_supported&code=bd28d7ed-c424-418e-9ca5-b12194c34cab">the original URL</a>. The file type is <code>application/pdf</code>.
Towards global flood mapping onboard low cost satellites with machine learning
<span title="2021-03-31">2021</span>
<i title="Springer Science and Business Media LLC">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a>
</i>
Preserved Fulltext
AbstractSpaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers on the ground. Very large constellations of small, nano satellites— 'CubeSats' are a promising solution to reduce revisit time in disaster areas from days to hours. However, data transmission to ground receivers is limited by constraints on power and bandwidth of CubeSats. Onboard processing offers a solution to decrease the amount of data to transmit by reducing large
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-021-86650-z">doi:10.1038/s41598-021-86650-z</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33790368">pmid:33790368</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5nk3hwsu2ve4plejnvddlvyrbu">fatcat:5nk3hwsu2ve4plejnvddlvyrbu</a>
</span>
more »
... nsor images to smaller data products. The ESA's recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning accelerator and the software to run custom applications. This work demonstrates a flood segmentation algorithm that produces flood masks to be transmitted instead of the raw images, while running efficiently on the accelerator aboard the PhiSat-1. Our models are trained on WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations, which we make available in a common format. We test the system on independent locations, demonstrating that it produces fast and accurate segmentation masks on the hardware accelerator, acting as a proof of concept for this approach.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718115243/https://www.nature.com/articles/s41598-021-86650-z.pdf?error=cookies_not_supported&code=bd28d7ed-c424-418e-9ca5-b12194c34cab" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/19/f1/19f11f5fa06476293e3695c2ea7391278509f62d.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-021-86650-z">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="unlock alternate icon" style="background-color: #fb971f;"></i>
Publisher / doi.org
</button>
</a>