FrogDeepSDM: Improving Frog Counting and Occurrence Prediction Using Multimodal Data and Pseudo-Absence Imputation

TL;DR

FrogDeepSDM leverages multimodal data, deep learning, and data imputation to significantly enhance frog distribution modeling, counting accuracy, and habitat prediction, addressing data sparsity and improving conservation tools.

Contribution

This work introduces a multimodal deep learning approach with data balancing and pseudo-absence imputation to improve frog SDM accuracy and robustness, a novel combination in ecological modeling.

Findings

Data balancing reduced MAE from 189 to 29 in frog counting.

Multimodal ensemble model achieved 84.9% accuracy and 0.90 AUC.

Feature selection identified key environmental predictors.

Abstract

Monitoring species distribution is vital for conservation efforts, enabling the assessment of environmental impacts and the development of effective preservation strategies. Traditional data collection methods, including citizen science, offer valuable insights but remain limited in coverage and completeness. Species Distribution Modelling (SDM) helps address these gaps by using occurrence data and environmental variables to predict species presence across large regions. In this study, we enhance SDM accuracy for frogs (Anura) by applying deep learning and data imputation techniques using data from the "EY - 2022 Biodiversity Challenge." Our experiments show that data balancing significantly improved model performance, reducing the Mean Absolute Error (MAE) from 189 to 29 in frog counting tasks. Feature selection identified key environmental factors influencing occurrence, optimizing inputs while maintaining predictive accuracy. The multimodal ensemble model, integrating land cover, NDVI, and other environmental inputs, outperformed individual models and showed robust generalization across unseen regions. The fusion of image and tabular data improved both frog counting and habitat classification, achieving 84.9% accuracy with an AUC of 0.90. This study highlights the potential of multimodal learning and data preprocessing techniques such as balancing and imputation to improve predictive ecological modeling when data are sparse or incomplete, contributing to more precise and scalable biodiversity monitoring.