@article{fdi:010091365,
  title = {{R}emote detection of asbestos-cement roofs : evaluating a {QGIS} plugin in a low- and middle-income country},
  author = {{G}luski, {P}auline and {R}amos-{B}onilla, {J}. and {P}etriglieri, {J}. {R}. and {T}urci, {F}. and {G}iraldo, {M}. and {T}ommasini, {M}. and {P}oli, {G}. and {L}ysaniuk, {B}enjamin},
  editor = {},
  language = {{ENG}},
  abstract = {{M}achine learning, a subset of artificial intelligence, has emerged as a powerful tool for generating new knowledge from observations. {I}n the realm of geographic information systems ({GIS}), machine learning techniques have become essential for spatial analysis tasks. {S}atellite image classification methods offer valuable decision-making support, particularly in land-use planning and identifying asbestos cement roofs, which pose significant health risks. {I}n {C}olombia, where asbestos has been used for decades, the detection and management of installed asbestos is critical. {T}his study evaluates the effectiveness of the {R}oof{C}lassify plugin, a machine learning-based {GIS} tool, in detecting asbestos cement roofs in {S}ibat & eacute;, {C}olombia. {B}y employing high-resolution satellite imagery, the study assesses the plugin's accuracy and performance. {R}esults indicate that {R}oof{C}lassify demonstrates promising capabilities in detecting asbestos cement roofs, achieving an overall accuracy score of 69.73%. {T}his shows potential for identifying areas with the presence of asbestos and informing decision-makers. {H}owever, false positives remain a challenge, necessitating further on-site verification. {T}he study underscores the importance of cautious interpretation of classification results and the need for tailored approaches to address specific contextual factors. {O}verall, {R}oof{C}lassify presents a valuable tool for identifying asbestos cement roofs, aiding in asbestos management strategies.},
  keywords = {{ACM} roof mapping ; {R}emote sensing ; {I}mage classification ; {S}ibat{\'e} ; {C}olombia ; {COLOMBIE} ; {PAYS} {EN} {DEVELOPPEMENT}},
  booktitle = {},
  journal = {{R}emote {S}ensing {A}pplications : {S}ociety and {E}nvironment},
  volume = {36},
  numero = {},
  pages = {101351 [14 ]},
  ISSN = {2352-9385},
  year = {2024},
  DOI = {10.1016/j.rsase.2024.101351},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091365},
}