# Super-resolved multi-temporal segmentation with deep permutation-invariant networks

Source: [Super-resolved multi-temporal segmentation with deep permutation-invariant networks](<https://www.sota2.com/research/paper/68fa6c2d1e209131cea3291e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:05:48.516Z

Authors: Diego Valsesia, Enrico Magli

Published: 2022-04-06T07:19:51.000Z

## Abstract

Multi-image super-resolution from multi-temporal satellite acquisitions of a scene has recently enjoyed great success thanks to new deep learning models. In this paper, we go beyond classic image reconstruction at a higher resolution by studying a super-resolved inference problem, namely semantic segmentation at a spatial resolution higher than the one of sensing platform. We expand upon recently proposed models exploiting temporal permutation invariance with a multi-resolution fusion module able to infer the rich semantic information needed by the segmentation task. The model presented in this paper has recently won the AI4EO challenge on Enhanced Sentinel 2 Agriculture.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.02631>)

[PDF](<https://arxiv.org/pdf/2204.02631.pdf>)