# SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning

arXiv: https://arxiv.org/abs/2507.00005 · HTML: https://mlsanity.com/paper/2507.00005/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Swarm Intelligence Based Algorithms: A Critical Analysis](https://mlsanity.com/paper/1403.7792/) (2014)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response](https://mlsanity.com/paper/2609.10433/) (2026)

## Abstract (arXiv)

Disaster response requires rapid, adaptive decision-making in chaotic environments. SwarmFusion, a novel hybrid framework, integrates particle swarm optimization with convolutional neural networks to optimize real-time resource allocation and path planning. By processing live satellite, drone, and sensor data, SwarmFusion enhances situational awareness and operational efficiency in flood and wildfire scenarios. Simulations using the DisasterSim2025 dataset demonstrate up to 40 percentage faster response times and 90 percentage survivor coverage compared to baseline methods. This scalable, data-driven approach offers a transformative solution for time-critical disaster management, with potential applications across diverse crisis scenarios.