Preliminary version of algorithms for automatic or semi-automatic extraction of information from digital twin using machine learning technique

The report aims at describing the preliminary version of an image semantic segmentation algorithm capable of speeding up the detection of flooded areas and related damages. It describes the initial needs, the study of the state of the art algorithms for such a class of problems and the challenges faced during the training and, being still a work in progress, future outlooks for its development.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-17
DOI
https://doi.org/10.15161/oar.it/212645
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Preliminary version of algorithms for automatic or semi-automatic extraction of information from digital twin using machine learning technique

Zenodo (CERN European Organization for Nuclear Research)
Flood Risk Assessment and Management
article

Preliminary version of algorithms for automatic or semi-automatic extraction of information from digital twin using machine learning technique

article en

Abstract

The report aims at describing the preliminary version of an image semantic segmentation algorithm capable of speeding up the detection of flooded areas and related damages. It describes the initial needs, the study of the state of the art algorithms for such a class of problems and the challenges faced during the training and, being still a work in progress, future outlooks for its development.

Zenodo (CERN European Organization for Nuclear Research)
National Institute for Astrophysics (IT)
Sustainable cities and communities
Openalex Percentile: Top 57%
Flood Risk Assessment and Management
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Preliminary version of algorithms for automatic or semi-automatic extraction of information from digital twin using machine learning technique · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS