arXiv:2607. 14160v1 Announce Type: new Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference.
By Jaiman Munshi (IonQ Team, App Dev Club, University of Maryland, College Park), Tanvi Tewary (IonQ Team, App Dev Club, University of Maryland, College Park), Sawyer Bloom (IonQ Team, App Dev Club, University of Maryland, College Park), Aidan Chu (IonQ Team, App Dev Club, University of Maryland, College Park), Chetan Maviti (IonQ Team, App Dev Club, University of Maryland, College Park), Kyon Winston-Bey (IonQ Team, App Dev Club, University of Maryland, College Park), Harshit Badjatia (IonQ Team, App Dev Club, University of Maryland, College Park), Farhan Kittur (IonQ Team, App Dev Club, University of Maryland, College Park), Vardhan Madhavarapu (IonQ Team, App Dev Club, University of Maryland, College Park), Varun Kota (IonQ Team, App Dev Club, University of Maryland, College Park), Joshua Kwon (IonQ Team, App Dev Club, University of Maryland, College Park), Nazia Rangwala-Vohra (IonQ Team, App Dev Club, University of Maryland, College Park), Franz Klein (IonQ Team, App Dev Club, University of Maryland, College Park)
arXiv:2604. 26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.
By Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos, Dimitris Syvridis
The paper presents an evolutionary framework, EXAQC, that automatically discovers parameterized quantum circuits (PQCs) for use as intermediate modules in hybrid quantum‑classical neural networks for image classification. By evolving PQCs while keeping classical feature‑extraction and prediction layers fixed, the authors achieve high accuracies on MNIST, Fashion‑MNIST, and CIFAR‑10 with gate counts comparable to other quantum architecture‑search methods. The evolved hybrid models match or exceed the performance of classical networks while using far fewer trainable parameters, and the choice of encoding (rotation‑based vs amplitude) significantly impacts accuracy.
By Devroop Kar, Daniel Krutz, Travis Desell
arXiv:2603.02465v2 Announce Type: replace-cross
Abstract: Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video dat...
By Emadeldeen Hamdan, Ahmad Faiz Tharima, Mohd Zahirasri Mohd Tohir, Dayang Nur Sakinah Musa, Erdem Koyuncu, Adam J. Watts, Ahmet Enis Cetin