<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Urban Infrastructure on Aaron Appelle</title><link>https://aaronappelle.github.io/tags/urban-infrastructure/</link><description>Recent content in Urban Infrastructure on Aaron Appelle</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Wed, 12 Feb 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://aaronappelle.github.io/tags/urban-infrastructure/index.xml" rel="self" type="application/rss+xml"/><item><title>Automated and Scalable Footstep Vibration-Based Pedestrian Localization in Built Environments Using Deep Learning</title><link>https://aaronappelle.github.io/research/jcce/</link><pubDate>Wed, 12 Feb 2025 00:00:00 +0000</pubDate><guid>https://aaronappelle.github.io/research/jcce/</guid><description>This paper introduces a novel deep-learning-based footstep localization system using ground vibrations measured using geophones. Published in the Journal of Computing in Civil Engineering, 2024.</description></item><item><title>Pedestrian Footstep Localization Using a Deep Convolutional Network for Time Difference of Arrival Estimation</title><link>https://aaronappelle.github.io/research/spie/</link><pubDate>Thu, 09 May 2024 00:00:00 +0000</pubDate><guid>https://aaronappelle.github.io/research/spie/</guid><description>This paper presents a resource-constrained localization system that uses geophones to map pedestrian locations in outdoor spaces. Published in SPIE Proceedings, 2024.</description></item></channel></rss>