# PSIque: Next Sequence Prediction of Satellite Images using a Convolutional Sequence-to-Sequence Network

arXiv: https://arxiv.org/abs/1711.10644 · HTML: https://mlsanity.com/paper/1711.10644/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task](https://mlsanity.com/paper/2104.10066/) (2021)
- [Explainable Earth Surface Forecasting under Extreme Events](https://mlsanity.com/paper/2410.01770/) (2024)
- [Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks](https://mlsanity.com/paper/2105.02752/) (2021)

## Abstract (arXiv)

Predicting unseen weather phenomena is an important issue for disaster management. In this paper, we suggest a model for a convolutional sequence-to-sequence autoencoder for predicting undiscovered weather situations from previous satellite images. We also propose a symmetric skip connection between encoder and decoder modules to produce more comprehensive image predictions. To examine our model performance, we conducted experiments for each suggested model to predict future satellite images from historical satellite images. A specific combination of skip connection and sequence-to-sequence autoencoder was able to generate closest prediction from the ground truth image.