Javier Noa Turnes | Searcharxiv

arXiv2026

Arctic-SAR: A Cross-Sensor RADARSAT Constellation Mission and Sentinel-1 Dataset and Fusion Benchmark for Multi-Task Sea Ice Mapping

Current sea ice mapping benchmarks are mostly built around single-sensor SAR products, limiting the evaluation of cross-sensor variability and the effectiveness of combining observations from different SAR platforms. Models trained on such datasets may also be sensitive to sensor-specific artifacts, radiometric characteristics, and acquisition conditions, which can reduce robustness and limit generalization. To address these limitations, this study introduces Arctic-SAR, a cross-sensor SAR dataset for Arctic sea ice mapping, constructed by matching RADARSAT Constellation Mission (RCM) dual-polarization imagery with Sentinel-1 scenes from the AI4Arctic Sea Ice Dataset. The dataset consists of 221 co-registered scenes and supports multi-task prediction of sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). In addition to the dataset, we establish a benchmark for cross-sensor SAR sea ice mapping by quantifying the contribution of RCM imagery and evaluating single-sensor baselines alongside representative input-, feature-, and decision-level fusion strategies within a consistent multi-task U-Net framework. Across three independent runs, the test results show that the effectiveness of cross-sensor fusion is strongly dependent on the fusion strategy and prediction task. Decision-level fusion achieves the highest mean combined score of $77.28\pm0.29\%$, essentially matching the Sentinel-1-only baseline at $77.26\pm0.13\%$, while achieving the highest SIC $R^2$ score. These findings show that Sentinel-1 and RCM provide task- and class-dependent information, but that simple joint cross-sensor fusion does not consistently improve generalization over a strong single-sensor baseline. Arctic-SAR therefore provides a benchmark for cross-sensor SAR fusion research in automated Arctic sea ice mapping.

eess.IV↗

arXiv2025

OpenWildlife: Open-Vocabulary Multi-Species Wildlife Detector for Geographically-Diverse Aerial Imagery

We introduce OpenWildlife (OW), an open-vocabulary wildlife detector designed for multi-species identification in diverse aerial imagery. While existing automated methods perform well in specific settings, they often struggle to generalize across different species and environments due to limited taxonomic coverage and rigid model architectures. In contrast, OW leverages language-aware embeddings and a novel adaptation of the Grounding-DINO framework, enabling it to identify species specified through natural language inputs across both terrestrial and marine environments. Trained on 15 datasets, OW outperforms most existing methods, achieving up to \textbf{0.981} mAP50 with fine-tuning and \textbf{0.597} mAP50 on seven datasets featuring novel species. Additionally, we introduce an efficient search algorithm that combines k-nearest neighbors and breadth-first search to prioritize areas where social species are likely to be found. This approach captures over \textbf{95\%} of species while exploring only \textbf{33\%} of the available images. To support reproducibility, we publicly release our source code and dataset splits, establishing OW as a flexible, cost-effective solution for global biodiversity assessments.

cs.CV↗