Gluski Pauline, Ramos-Bonilla J., Petriglieri J. R., Turci F., Giraldo M., Tommasini M., Poli G., Lysaniuk Benjamin. (2024). Remote detection of asbestos-cement roofs : evaluating a QGIS plugin in a low- and middle-income country. Remote Sensing Applications : Society and Environment, 36, 101351 [14 p.]. ISSN 2352-9385.
Titre du document
Remote detection of asbestos-cement roofs : evaluating a QGIS plugin in a low- and middle-income country
Année de publication
2024
Auteurs
Gluski Pauline, Ramos-Bonilla J., Petriglieri J. R., Turci F., Giraldo M., Tommasini M., Poli G., Lysaniuk Benjamin
Source
Remote Sensing Applications : Society and Environment, 2024,
36, 101351 [14 p.] ISSN 2352-9385
Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for generating new knowledge from observations. In the realm of geographic information systems (GIS), machine learning techniques have become essential for spatial analysis tasks. Satellite image classification methods offer valuable decision-making support, particularly in land-use planning and identifying asbestos cement roofs, which pose significant health risks. In Colombia, where asbestos has been used for decades, the detection and management of installed asbestos is critical. This study evaluates the effectiveness of the RoofClassify plugin, a machine learning-based GIS tool, in detecting asbestos cement roofs in Sibat & eacute;, Colombia. By employing high-resolution satellite imagery, the study assesses the plugin's accuracy and performance. Results indicate that RoofClassify demonstrates promising capabilities in detecting asbestos cement roofs, achieving an overall accuracy score of 69.73%. This shows potential for identifying areas with the presence of asbestos and informing decision-makers. However, false positives remain a challenge, necessitating further on-site verification. The study underscores the importance of cautious interpretation of classification results and the need for tailored approaches to address specific contextual factors. Overall, RoofClassify presents a valuable tool for identifying asbestos cement roofs, aiding in asbestos management strategies.
Plan de classement
Sciences du milieu [021]
;
Urbanisation et sociétés urbaines [102]
;
Télédétection [126]
Description Géographique
COLOMBIE
Localisation
Fonds IRD [F B010091365]
Identifiant IRD
fdi:010091365
