# Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks

arXiv: https://arxiv.org/abs/2506.04285 · HTML: https://mlsanity.com/paper/2506.04285/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 76 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Symbolic Discovery of Optimization Algorithms](https://mlsanity.com/paper/2302.06675/) (2023)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery](https://mlsanity.com/paper/1812.01756/) (2018)
- [Looking for change? Roll the Dice and demand Attention](https://mlsanity.com/paper/2009.02062/) (2020)
- [Fast model inference and training on-board of Satellites](https://mlsanity.com/paper/2307.08700/) (2023)
- [On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery](https://mlsanity.com/paper/2106.15281/) (2021)
- [Memristive Reservoirs Learn to Learn](https://mlsanity.com/paper/2306.12676/) (2023)

## Built on by (most cited first, 4 of 4)

- [Self-Organising Memristive Networks as Physical Learning Systems](https://mlsanity.com/paper/2509.00747/) (2025)
- [Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data](https://mlsanity.com/paper/2604.18606/) (2026)
- [Event-Based Vision in Space: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2606.01280/) (2026)
- [On-board Remote-Sensing Foundation Models for Unsupervised Change Detection of Disaster Events](https://mlsanity.com/paper/2606.27018/) (2026)

## Abstract (arXiv)

Earth observations from low Earth orbit satellites provide vital information for decision makers to better manage time-sensitive events such as natural disasters. For the data to be most effective for first responders, low latency is required between data capture and its arrival to decision makers. A major bottleneck is in the bandwidth-limited downlinking of the data from satellites to ground stations. One approach to overcome this challenge is to process at least some of the data on-board and prioritise pertinent data to be downlinked. In this work we propose a Physics Aware Neuromorphic Network (PANN) to detect changes caused by natural disasters from a sequence of multi-spectral satellite images and produce a change map, enabling relevant data to be prioritised for downlinking. The PANN used in this study is motivated by physical neural networks comprised of nano-electronic circuit elements known as "memristors" (nonlinear resistors with memory). The weights in the network are dynamic and update in response to varying input signals according to memristor equations of state and electrical circuit conservation laws. The PANN thus generates physics-constrained dynamical output features which are used to detect changes in a natural disaster detection task by applying a distance-based metric. Importantly, this makes the whole model training-free, allowing it to be implemented with minimal computing resources. The PANN was benchmarked against a state-of-the-art AI model and achieved comparable or better results in each natural disaster category. It thus presents a promising solution to the challenge of resource-constrained on-board processing.