Identification of asbestos roofing from hyperspectral images

Regulations relating to the disposal of the use of asbestos was introduced in Italy with Law no. 257 of 1992 and its implementation took place over time. The Regional Asbestos Plan was put in place in 1996 and is updated periodically.

Modern remote sensing techniques constitute an essential tool for studies over an environmental and territorial scale. These systems can detect for each pixel of the acquired image from tens to hundreds of bands of the electromagnetic spectrum. This is useful as any material has its own characteristic spectral signature that can be exploited for different types of investigation.

The work involved the experimentation of a neural network for the classification of airborne remotely sensed hyperspectral images to identify and map the asbestos-cement roofing existing in some Municipalities of the Autonomous Region of Friuli Venezia Giulia.

The Region covers an area of approximately 8,000 square kilometres. To detect the entire area, it was necessary to carry out flights on different directions, different days and with different solar exposure conditions and so, the radiometric quality of the images is not uniform. Moreover, the images have high geometric resolution (1 meter pixel) and radiometric resolution (over 180 bands), that required a particular attention in their management: more than 4,000 images, for a total size of 25-30 TB.

Starting from these hyperspectral images and using the information already available relating to the mapping of the asbestos roofs of 25 Municipalities of the Region, we generated an adequate ground truth to train, test and validate a neural network implemented using the Keras library.

Given the differences in the territories of the various Municipalities, in the first step of the processing we calculated 3 different models generated on different training datasets for each considered Municipality: a total and a partial one that are independent on the considered Municipality, and the last one adapted to the specific Municipality. The combination of these predictions allowed us to obtain a raster result which is supposed to better adapt to the characteristics of the considered Municipality.

Obtained the data, it was then necessary to move on from the raster results to vector data using a zonal analysis on the buildings available in the Regional Numerical Map. An initial automatic classification, determined through the definition of adequate thresholds, was then manually refined exploring it with additional tools, such as Google StreetView and the 10 cm regional orthophoto, to obtain a final refined classification.

The results obtained for the 5 pilot Municipalities represent a clear indication of the presence of asbestos material on some building roofs. This work emphasized an operational workflow using data at a regional scale and could also be easily extended to other territorial entities. It has the great advantage to allow the government authority to save at least an order of magnitude in term of costs with respect to traditional investigations. Finally, the automation of the neural network represents a useful tool for programming, planning and management of the territory also in terms of human health.