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.

Institutions

Publication Details

Journal
Istituto Nazionale di Fisica Nucleare. Centro Nazionale Analisi Fotogrammi
Published
2025-03-17
DOI
https://doi.org/10.15161/oar.it/hv72b-98j89
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

Istituto Nazionale di Fisica Nucleare. Centro Nazionale Analisi Fotogrammi
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.

Istituto Nazionale di Fisica Nucleare. Centro Nazionale Analisi Fotogrammi
National Institute for Astrophysics (IT)
Sustainable cities and communities
Openalex Percentile: Top 61%
Flood Risk Assessment and Management
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