mlsanityData to 14 Oct 2026

EscapeWildFire: Assisting People to Escape Wildfires in Real-Time

arXiv 2102.11558 ↗First posted 23 Feb 2021cs.CY cs.CVcited by 0

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Abstract (arXiv)

Over the past couple of decades, the number of wildfires and area of land burned around the world has been steadily increasing, partly due to climatic changes and global warming. Therefore, there is a high probability that more people will be exposed to and endangered by forest fires. Hence there is an urgent need to design pervasive systems that effectively assist people and guide them to safety during wildfires. This paper presents EscapeWildFire, a mobile application connected to a backend system which models and predicts wildfire geographical progression, assisting citizens to escape wildfires in real-time. A small pilot indicates the correctness of the system. The code is open-source; fire authorities around the world are encouraged to adopt this approach.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 24 references that are in the map. First posted on arXiv 23 Feb 2021.

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2 of 24 references.

References · 2

  1. 2018Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learningcited by 5
  2. 2019Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehiclescited by 1

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