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