Advances in Machine Learning Approaches for UAV-Based Remote Sensing in Data-Deficient Antarctic Environments

Abstract

Highlights: What are the main findings? Task-specific methods continue to perform well in small-scale data regimes, but data scarcity means that Antarctic science continues to lag behind broader ML advances; A paradigm shift in Earth Observation (EO) towards foundation models is underway, but Antarctic scenes remain largely absent from global datasets and benchmarking efforts. What are the implications of the main findings? Generalist EO models effectively support multimodal and multi-scale inputs, but remain limited by focusing on urban and agricultural satellite data; Integrating UAV-based polar data is essential for effective cross-domain adaptation to data-scarce Antarctic environments. Remote sensing plays a vital role in monitoring environmental change in Antarctica, offering non-invasive insights into ice dynamics, biodiversity, and fragile ecosystems. Harsh conditions, limited field access, and logistical challenges result in sparse, noisy, and often unlabelled datasets, posing major obstacles for machine learning (ML) approaches. Data scarcity remains a fundamental challenge for uncrewed aerial vehicle (UAV)-based ecological monitoring. While ML models in other Earth observation domains demonstrate state-of-the-art performance, their applicability in Antarctic and polar regions’ settings is limited. This paper reviews the intersection of ML and UAV-based remote sensing in Antarctica under extreme data constraints. We surveyed recent strategies designed to overcome these limitations, including self-supervised learning, physics-informed modelling, and foundation models. Results highlight a notable gap, as polar environments remain excluded from global datasets and benchmarks due to the extensive data requirements of large-scale models. Opportunities exist where multimodal and multi-scale generalisation can enhance cross-domain adaption to data-scarce use cases. Unlike prior reviews on general remote sensing or task-specific polar studies, this work uniquely underscores the need for Antarctic representation in global ML advances, positioning Antarctica as a frontier testbed for machine learning in extreme, inaccessible, and under-resourced fields.