Alessandro Chiabodo
Deep Learning Techniques for Water Level Forecasting in Satellite Images.
Rel. Paolo Garza, Daniele Rege Cambrin. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
As the climate continues to change, the ability to automatically map bodies of water and predict their future variations is certainly very important, both to ensure that people are able to prevent and detect certain extreme weather events, such as floods and droughts, and to aid urban, agricultural and industrial planning around large lakes or waterways. Since the launch of the first satellites, images from orbit have been used to detect and monitor our planet's water masses, such as oceans and lakes. Over the years, increasingly accurate methods have replaced human work, and in recent years, deep learning algorithms such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) have achieved near-perfect performance in this task.
However, although the detection of even the smallest lake from Earth orbit is now near perfect, we are still unable to predict changes in the size and shape of bodies of water with high accuracy
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