Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024

[Submitted on 8 Jul 2024]

View a PDF of the paper titled Tile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024, by Anthony Miyaguchi and 1 other authors

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Abstract:We explore methods to solve the multi-label classification task posed by the GeoLifeCLEF 2024 competition with the DS@GT team, which aims to predict the presence and absence of plant species at specific locations using spatial and temporal remote sensing data. Our approach uses frequency-domain coefficients via the Discrete Cosine Transform (DCT) to compress and pre-compute the raw input data for convolutional neural networks. We also investigate nearest neighborhood models via locality-sensitive hashing (LSH) for prediction and to aid in the self-supervised contrastive learning of embeddings through tile2vec. Our best competition model utilized geolocation features with a leaderboard score of 0.152 and a best post-competition score of 0.161. Source code and models are available at this https URL.