# SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis

arXiv: https://arxiv.org/abs/2301.07452 · HTML: https://mlsanity.com/paper/2301.07452/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2023.

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

- [Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data](https://mlsanity.com/paper/2401.03792/) (2024)
- [SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking](https://mlsanity.com/paper/2409.17087/) (2024)
- [Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping](https://mlsanity.com/paper/2402.00023/) (2024)
- [On the use of Graphs for Satellite Image Time Series](https://mlsanity.com/paper/2505.16685/) (2025)

## Abstract (arXiv)

Climate change has caused disruption in certain weather patterns, leading to extreme weather events like flooding and drought in different parts of the world. In this paper, we propose machine learning methods for analyzing changes in water resources over a time period of six years, by focusing on lakes and rivers in Italy and Spain. Additionally, we release open-access code to enable the expansion of the study to any region of the world. We create a novel multispectral and multitemporal dataset, SEN2DWATER, which is freely accessible on GitHub. We introduce suitable indices to monitor changes in water resources, and benchmark the new dataset on three different deep learning frameworks: Convolutional Long Short Term Memory (ConvLSTM), Bidirectional ConvLSTM, and Time Distributed Convolutional Neural Networks (TD-CNNs). Future work exploring the many potential applications of this research is also discussed.