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timlinux authored and github-actions[bot] committed Nov 5, 2024
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6 changes: 3 additions & 3 deletions content/community-blogs/geoai-key-developments-insights.md
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<p>While answers from knowledge graphs are intrinsically explainable, many other (Geo)AI approaches are built on AI approaches that result in black box models. </p>
<p>While answers from knowledge graphs are intrinsically explainable, many other (Geo)AI solutions are built on AI approaches that result in black box models.</p>



<p>Graph neural networks (GNNs) have become very popular in GeoAI (including in urban analytics and mobility <a href="https://arxiv.org/abs/2307.08461">[Jalali et al. 2023] </a><a href="https://journals.sagepub.com/doi/10.1177/23998083231204689">[Liu et al. 2024]</a>) but their black box nature limits their practical usefulness in domains where transparency and trustworthiness are crucial. To offered insights into how model predictions are made, <a href="https://journals.sagepub.com/doi/10.1177/23998083231204689">[Liu et al. 2024]</a> propose a spatially explicit GeoAI-based method that combines a graph convolutional network and a graph-based XAI method, called GNNExplainer to explore the correlation between urban objects.</p>
<p>Graph neural networks (GNNs) have become very popular in GeoAI (including in urban analytics and mobility <a href="https://arxiv.org/abs/2307.08461">[Jalali et al. 2023] </a><a href="https://journals.sagepub.com/doi/10.1177/23998083231204689">[Liu et al. 2024]</a>) but their black box nature limits their practical usefulness in domains where transparency and trustworthiness are crucial. To offer insights into how model predictions are made, <a href="https://journals.sagepub.com/doi/10.1177/23998083231204689">[Liu et al. 2024]</a> propose a spatially explicit GeoAI-based method that combines a graph convolutional network and a graph-based XAI method, called GNNExplainer to explore the correlation between urban objects.</p>



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<li><a href="https://arxiv.org/abs/2310.18660">Jakubik, J., Roy, S., Phillips, C., Fraccaro, P., Godwin, D., Zadrozny, B., &#8230; &amp; Edwards, B. Foundation models for generalist geospatial artificial intelligence. arXiv 2023. <em>arXiv preprint arXiv:2310.18660</em>.</a></li>
<li><a href="https://arxiv.org/abs/2310.18660">Jakubik, J., Roy, S., Phillips, C., Fraccaro, P., Godwin, D., Zadrozny, B., &#8230; &amp; Edwards, B. (2023) Foundation models for generalist geospatial artificial intelligence. <em>arXiv preprint arXiv:2310.18660</em>.</a></li>



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---
source: "flickr"
title: "Animación de vista de mapa 3D en QGIS-600p-hls"
image: "360p.mp4?s=eyJpIjo1Mzk3NTY1MjA3MSwiZSI6MTczMDc2Njk4MSwicyI6IjM1NDkxNjY4ZDVhYTlkYjQ5NGRjYzdlM2ZlZGUxZDNmNWJkMjA0Y2YiLCJ2IjoxfQ.mp4"
date: "2024-09-06"
link: "https://live.staticflickr.com/video/53975652071/d3bb774578/360p.mp4?s=eyJpIjo1Mzk3NTY1MjA3MSwiZSI6MTczMDc2Njk4MSwicyI6IjM1NDkxNjY4ZDVhYTlkYjQ5NGRjYzdlM2ZlZGUxZDNmNWJkMjA0Y2YiLCJ2IjoxfQ"
draft: "true"
showcase: "map"
---
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