most citedTowards interpretable-by-design deep learning algorithms

2 citations · 2 across the 2 of their papers we have counts for

collaborators
Showing cs.CVShow all

1 paper · 1 filter

cs.CV2024

IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data

Ziyang Zhang, Plamen Angelov, Dmitry Kangin +1

In this paper, we address two critical challenges in the domain of flood detection: the computational expense of large-scale time series change detection and the lack of interpreta…