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

PloS One
Susana López-AparicioIslen Vallejo

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 based on machine learning techniques. The images from the real estate advertisements are collected and processed to identify wood burning installations, which are automatically classified according to the three classes used in official statistics, i.e., open fireplaces, stoves produced before 1998 and stoves produced after 1998. The model recognizes and classifies the wood appliances with a precision of 81%, 85% and 91% for open fireplaces, old...Continue Reading

References

May 2, 2013·Briefings in Bioinformatics·Daniel Glez-PeñaFlorentino Fdez-Riverola
Jan 17, 2017·Journal of Environmental Management·Susana López-AparicioAnna Broberg

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Citations

Jun 1, 2019·Journal of Environmental Management·E PisoniC Cuvelier

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Methods Mentioned

BETA
scraping

Software Mentioned

YOLO
FINN
SQLite
GoodOvening webcrawler
MetVed
Scrapy

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