A Graph-Based Representation of Wastewater Maps

This paper presents a method for the automatic extraction of the structure of wastewater networks from geographical maps, as well as their representation in the form of graphs. The approach consists first in detecting the different important elementary elements composing a wastewater network, such as the manholes, their identifiers (using optical character recognition, OCR), and the wastewater pipes that connect them. Detecting these elementary elements is a first difficult problem, despite many existing tools, mainly due to the quality of the wastewater network maps used. However, the challenge addressed in this paper is how to select the relevant elementary elements detected, and bring them together to finally extract the wastewater network. One of the main contributions of this paper is to propose an efficient algorithm to solve these selection and assignment problems (e.g., manhole identifiers to manholes represented by circles). We also deal with the situation of isolated nodes to have the most connected clusters possible. The experimental results conducted on real map data show very promising results despite the low quality of the maps.

Authors

Institutions

Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10826095
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Graph-Based Representation of Wastewater Maps

Nanée Chahinian, Carole Delenne, Salem Benferhat, Ikram El Miqdadi et al.
Semantic Web and Ontologies
article

A Graph-Based Representation of Wastewater Maps

Nanée Chahinian, Carole Delenne, Salem Benferhat, Ikram El Miqdadi, Fatima Abouzid
article en

Abstract

This paper presents a method for the automatic extraction of the structure of wastewater networks from geographical maps, as well as their representation in the form of graphs. The approach consists first in detecting the different important elementary elements composing a wastewater network, such as the manholes, their identifiers (using optical character recognition, OCR), and the wastewater pipes that connect them. Detecting these elementary elements is a first difficult problem, despite many existing tools, mainly due to the quality of the wastewater network maps used. However, the challenge addressed in this paper is how to select the relevant elementary elements detected, and bring them together to finally extract the wastewater network. One of the main contributions of this paper is to propose an efficient algorithm to solve these selection and assignment problems (e.g., manhole identifiers to manholes represented by circles). We also deal with the situation of isolated nodes to have the most connected clusters possible. The experimental results conducted on real map data show very promising results despite the low quality of the maps.

Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), Université de Montpellier (FR), Aix-Marseille Université (FR), Laboratoire HydroSciences Montpellier (FR), Centre de Recherche en Informatique (FR), Château Gombert (FR), Institut Universitaire des Systèmes Thermiques Industriels (FR), Institut de Recherche pour le Développement (FR), Université d'Artois (FR), Sidi Mohamed Ben Abdellah University (MA)
European Commission, HORIZON EUROPE Marie Sklodowska-Curie Actions
Clean water and sanitation
Openalex Percentile: Top 78%
Semantic Web and Ontologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.