Automated Detection of Asbestos-cement Roofs Using Multi-source Remote Sensing Data and Machine Learning
issued: 2026
subject: asbestos
type: artykuł
rights: Biblioteka Uniwersytetu Zielonogórskiego
subject: LIDAR
language: eng
source: Civil and Environmental Engineering Reports (CEER), no 36, vol. 1
description: Asbestos, due to its resistance to temperature, chemicals, and mechanical damage, was widely used in construction, especially in the second half of the 20th century. Today, most of the remaining asbestos products are roofing sheets, which pose a serious health risk and the WHO calls for its elimination. One of the key actions is to identify buildings with asbestos-cement roofing.
format: application/pdf
date: 2026
rights: CC 4.0
description: tytuł dodatkowy: Prace z Inżynierii Lądowej i Środowiska
description: The resulting F1-scores (0.70-0.88) confirm a strong balance between correct and false detections. Detection accuracy was influenced by environmental factors such as tree coverage and surface contamination, which introduced visual noise in RGB imagery and occasionally led to misclassification. Despite these limitations, the results confirm the high potential of this approach for asbestos detection. The study demonstrates that integrating LiDAR intensity, roof geometry, and visual data significantly improves the reliability of asbestos-cement roof identification compared to single-source methods.
publisher: Zielona Góra: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
subject: machine learning
description: As part of the research, a method for detecting such buildings was developed using machine learning, LiDAR technology, orthophotos, and satellite images. The developed model is based on data such as reflection intensity, roof slope, and year of construction. The proposed methodology allows for the effective localization of buildings covered with this hazardous material.
creator: Durczak, Kacper
creator: Janicka, Joanna
description: The model successfully identified between 79% and 91% of asbestos-covered buildings across three diverse test areas, and up to 90% of buildings were correctly classified in terms of roofing material. Quantitative evaluation demonstrates that the proposed method effectively identifies asbestos-covered buildings achieving precision values ranging from 0.59 to 0.89 and recall between 0.77 and 0.86.
contributor: Kuczyński, Tadeusz - red.
creator: Błaszczak-Bąk, Wioleta