d_kishkinev (d_kishkinev) wrote,
d_kishkinev
d_kishkinev

Machine learning добрался до geolocation data

Официально статья выйдет завтра

Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data.

Abstract

Geolocators are a well-established technology to reconstruct migration routes of animals that are too small to carry satellite tags (e.g. passerine birds). These devices record environmental light-level data that enable the reconstruction of daily positions from the time of twilight. However, all current methods for analysing geolocator data require manual pre-processing of raw records to eliminate twilight events showing unnatural variation in light levels, a step that is time-consuming and must be accomplished by a trained expert. Here, we propose and implement advanced machine learning techniques to automate this procedure and we apply them to 108 migration tracks of barn swallows ( Hirundo rustica). We show that routes reconstructed from the automated pre-processing are comparable to those obtained from manualselection accomplished by a human expert. This raises the possibility of fully automating light-level geolocator data analysis and possibly analysing the large amount of data already collected on several species.

KEYWORDS:

deep neural network; light-level tag; migratory species; movement ecology; path estimation; random forest

Tags: bird migration, electronics, geolocation, machine learning, remote sensing, science
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