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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Developing an AI-based Urban Grammar</title>
<meta name="description" content="A framework for easily creating beautiful presentations using HTML">
<meta name="author" content="Urban Grammar AI Team">
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="../revealjs/dist/reset.css">
<link rel="stylesheet" href="../revealjs/dist/reveal.css">
<link rel="stylesheet" href="../revealjs/dist/theme/ugai.css" id="theme">
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<link rel="stylesheet" href="../revealjs/plugin/highlight/monokai.css" id="highlight-theme">
</head>
<body>
<!-- BODY -->
<div class="reveal">
<div class="slides">
<section id="section" class="slide level1">
<h1></h1>
<CENTER>
<h4>
<em>Detecting urban typology from multispectral satellite imagery using neural networks</em>
</h4>
</CENTER>
<table>
<col width="50%">
<col width="50%">
<tr>
<td>
<CENTER>
<a href="https://martinfleischmann.net/"><b>Martin Fleischmann</b></a>
</CENTER>
</td>
<td>
<CENTER>
<a href="https://darribas.org"><b>Dani Arribas-Bel</b></a>
</CENTER>
</td>
</tr>
<tr>
<td>
<CENTER>
<a href="https://uk.linkedin.com/in/murraydata"><b>John Murray</b></a>
</CENTER>
</td>
<td>
<CENTER>
<a href="https://www.alex-singleton.com/"><b>Alex Singleton</b></a>
</CENTER>
</td>
</tr>
</table>
<table>
<col width="33%">
<col width="33%">
<col width="33%">
<tr>
<td>
<img src="../fig/pr/logo_liv.png" style="width:300px;vertical-align:middle;box-shadow:none">
</td>
<td>
<img src="../fig/pr/logo_ati.png" style="width:300px;vertical-align:middle;box-shadow:none">
</td>
<td>
<img src="../fig/pr/logo_gdsl.png" style="width:300px;vertical-align:middle;box-shadow:none">
</td>
</tr>
</table>
</section>
<section id="section-1" class="slide level1">
<h1></h1>
<section id="the-data" class="level2">
<h2>The data</h2>
<center>
A quick overview of data used in the recent publications in Urban Morphology.
</center>
</section>
<section id="section-2" class="level2">
<h2></h2>
<p><img src="../fig/isuf/fu_et_al_2022.png" style="vertical-align:middle;box-shadow:none"> <span class="pie">Fu et al. (2022)</span></p>
</section>
<section id="section-3" class="level2">
<h2></h2>
<p><img src="../fig/isuf/kantarek_et_al_2022.png" style="vertical-align:middle;box-shadow:none"> <span class="pie">Kantarek et al. (2022)</span></p>
</section>
<section id="section-4" class="level2">
<h2></h2>
<p><img src="../fig/isuf/guo_and_ding_2021.png" style="vertical-align:middle;box-shadow:none"> <span class="pie">Guo and Ding (2021)</span></p>
</section>
<section id="section-5" class="level2">
<h2></h2>
<p><img src="../fig/isuf/li_zhang_2021.png" style="vertical-align:middle;box-shadow:none"> <span class="pie">Li and Zhang (2021)</span></p>
</section>
</section>
<section id="section-6" class="slide level1">
<h1></h1>
<section id="the-issue" class="level2">
<h2>The issue</h2>
<ul style="list-style: none">
<li class="fragment">
availability
</li>
<li class="fragment">
reliability
</li>
<li class="fragment">
processing demands
</li>
</ul>
</section>
<section id="the-issue-1" class="level2">
<h2>The issue</h2>
<center>
Urban morphology is bounded by the data availability and the ability to extract morphological information out of it.
</center>
</section>
</section>
<section id="section-7" class="slide level1">
<h1></h1>
<section id="the-solution" class="level2">
<h2>The solution (?)</h2>
</section>
<section id="section-8" class="level2">
<h2> 🛰</h2>
</section>
<section id="section-9" class="level2" data-transition="none" data-background-image="../fig/gisruk/hires.png" data-background-size="cover">
<h2 data-transition="none" data-background-image="../fig/gisruk/hires.png" data-background-size="cover"></h2>
</section>
<section id="sentinel-2" class="level2">
<h2>Sentinel 2</h2>
<CENTER>
<img src="../fig/gisruk/s2.png" style="vertical-align:middle;box-shadow:none">
</CENTER>
</section>
</section>
<section id="section-10" class="slide level1">
<h1></h1>
<section id="morphology-and-imagery" class="level2">
<h2>Morphology and imagery</h2>
<ul style="list-style: none">
<li class="fragment">
supervised methods
</li>
<li class="fragment">
<strong>un</strong>supervised methods
</li>
</ul>
</section>
</section>
<section id="section-11" class="slide level1">
<h1></h1>
<section id="predicting-spatial-signatures" class="level2">
<h2>Predicting Spatial Signatures</h2>
<center>
supervised learning
</center>
</section>
<section id="spatial-signatures" class="level2">
<h2>Spatial Signatures</h2>
</section>
<section id="section-12" class="level2" data-transition="none">
<h2 data-transition="none"></h2>
<CENTER>
<em>A characterisation of space based on form and function designed to understand urban environments</em>
</CENTER>
</section>
<section id="section-13" class="level2">
<h2></h2>
<iframe src="https://urbangrammarai.xyz/great-britain/#" width="1400px" height="600px" style="border: none;">
</iframe>
</section>
<section id="section-14" class="level2" data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt.png" data-background-size="cover">
<h2 data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt.png" data-background-size="cover"></h2>
</section>
<section id="section-15" class="level2" data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt_countryside.png" data-background-size="cover">
<h2 data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt_countryside.png" data-background-size="cover"></h2>
</section>
<section id="section-16" class="level2" data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt_periphery.png" data-background-size="cover">
<h2 data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt_periphery.png" data-background-size="cover"></h2>
</section>
<section id="section-17" class="level2" data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt_urban.png" data-background-size="cover">
<h2 data-transition="none" data-background-image="../fig/empirical/signatures_scottish_belt_urban.png" data-background-size="cover"></h2>
</section>
<section id="what-do-we-want-to-do" class="level2">
<h2>What do we want to do?</h2>
<CENTER class="fragment">
train a neural network
</CENTER>
<CENTER class="fragment">
understand the role of geography
</CENTER>
</section>
<section id="chip-size-effect" class="level2">
<h2>Chip size effect</h2>
</section>
<section id="section-18" class="level2" data-transition="none" data-background-image="../fig/gisruk/chips_80.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/gisruk/chips_80.png" data-background-size="contain"></h2>
<p><span class="pie">80x80m: 13760 chips, which is 74 % of maximum</span></p>
</section>
<section id="section-19" class="level2" data-transition="none" data-background-image="../fig/gisruk/chips_160.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/gisruk/chips_160.png" data-background-size="contain"></h2>
<p><span class="pie">160x160m: 2718 chips within, which is 57 % of maximum</span></p>
</section>
<section id="section-20" class="level2" data-transition="none" data-background-image="../fig/gisruk/chips_320.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/gisruk/chips_320.png" data-background-size="contain"></h2>
<p><span class="pie">320x320m: 423 chips within, which is 35 % of maximum</span></p>
</section>
<section id="section-21" class="level2" data-transition="none" data-background-image="../fig/gisruk/chips_640.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/gisruk/chips_640.png" data-background-size="contain"></h2>
<p><span class="pie">640x640m: 38 chips within, which is 13 % of maximum</span></p>
</section>
<section id="section-22" class="level2" data-transition="none" data-background-image="../fig/gisruk/chips_320_all.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/gisruk/chips_320_all.png" data-background-size="contain"></h2>
<p><span class="pie">320x320m, chips capturing the proportion (100% of maximum)</span></p>
</section>
<section id="section-23" class="level2" data-transition="none" data-background-image="../fig/isuf/mor_all.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/isuf/mor_all.png" data-background-size="contain"></h2>
<p><span class="pie">Predicted class (320x320m)</span></p>
</section>
<section id="section-24" class="level2" data-transition="none" data-background-image="../fig/gisruk/mor_country.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/gisruk/mor_country.png" data-background-size="contain"></h2>
<p><span class="pie">Wild countryside (320x320m)</span></p>
</section>
<section id="section-25" class="level2" data-transition="none" data-background-image="../fig/gisruk/mor_urban.png" data-background-size="contain">
<h2 data-transition="none" data-background-image="../fig/gisruk/mor_urban.png" data-background-size="contain"></h2>
<p><span class="pie">Urbanity (320x320m)</span></p>
</section>
<section id="still-work-in-progress" class="level2">
<h2>Still work in progress</h2>
</section>
</section>
<section id="section-26" class="slide level1">
<h1></h1>
<section id="estimating-generalized-measures-of-local-neighbourhood-context" class="level2">
<h2>Estimating generalized measures of local neighbourhood context</h2>
<center>
unsupervised learning
</center>
</section>
<section id="section-27" class="level2">
<h2></h2>
<CENTER>
<img src="../fig/isuf/sent2.jpg" style="vertical-align:middle;box-shadow:none">
</CENTER>
</section>
<section id="section-28" class="level2">
<h2></h2>
<CENTER>
<img src="../fig/isuf/cae.jpg" style="vertical-align:middle;box-shadow:none">
</CENTER>
</section>
<section id="section-29" class="level2">
<h2></h2>
<CENTER>
<img src="../fig/isuf/clusters.jpg" style="vertical-align:middle;box-shadow:none">
</CENTER>
</section>
</section>
<section id="the-takeaway" class="slide level1">
<h1>The takeaway</h1>
<ul style="list-style: none">
<li class="fragment">
We are limited by the data we are used to
</li>
<li class="fragment">
Can satellite imagery and AI resolve it?
</li>
<li class="fragment">
Probably not. Not all of it and not yet.
</li>
</ul>
</section>
<section id="section-30" class="slide level1">
<h1></h1>
<CENTER>
<h4>
<em>Detecting urban typology from multispectral satellite imagery using neural networks</em>
</h4>
</CENTER>
<table>
<col width="50%">
<col width="50%">
<tr>
<td>
<CENTER>
<a href="https://martinfleischmann.net/"><b>Martin Fleischmann</b></a>
</CENTER>
</td>
<td>
<CENTER>
<a href="https://darribas.org"><b>Dani Arribas-Bel</b></a>
</CENTER>
</td>
</tr>
<tr>
<td>
<CENTER>
<a href="https://uk.linkedin.com/in/murraydata"><b>John Murray</b></a>
</CENTER>
</td>
<td>
<CENTER>
<a href="https://www.alex-singleton.com/"><b>Alex Singleton</b></a>
</CENTER>
</td>
</tr>
</table>
<table>
<col width="33%">
<col width="33%">
<col width="33%">
<tr>
<td>
<img src="../fig/pr/logo_liv.png" style="width:300px;vertical-align:middle;box-shadow:none">
</td>
<td>
<img src="../fig/pr/logo_ati.png" style="width:300px;vertical-align:middle;box-shadow:none">
</td>
<td>
<img src="../fig/pr/logo_gdsl.png" style="width:300px;vertical-align:middle;box-shadow:none">
</td>
</tr>
</table>
</section>
</div>
</div>
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