Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions.

ABSTRACT: In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input data for a high resolution emission inventory for residential wood combustion (RWC). The first method is a webcrawler that extracts openly online available real estate data in a systematic way, and thereafter structures them for analysis. The webcrawler reads online Norwegian real estate advertisements and it collects the geo-position of the dwellings. Dwellings are classified according to the type (e.g., apartment, detached house) they belong to and the heating systems they are equipped with. The second method is a model trained for image recognition and classification b

SUBMITTER: Lopez-Aparicio S 

PROVIDER: S-EPMC6047804 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature