Structure-from-Motion (SfM) pipelines rely heavily on the detection and matching of repeatable keypoints across images, yet the performance of modern learned feature extractors in challenging environments remains insufficiently understood. This paper evaluates classical and deep keypoint detectors for SfM reconstruction using winter Arctic UAV imagery, a domain characterized by low texture, repetitive patterns, and limited man-made structure. We compare three feature pipelines within a shared PyCOLMAP-based framework: SIFT with nearest-neighbor matching (SIFT+NN), SuperPoint, and DISK, along with a hybrid approach combining SuperPoint and DISK correspondences.</p> <p>Quantitative evaluation is conducted using standard SfM metrics, including number of observations, track length, observations per image, and reprojection error, complemented by qualitative analysis of keypoint distributions and reconstruction interpretability. Results show that SIFT+NN consistently achieves the most complete and stable reconstructions, producing the highest number of matched observations and lowest reprojection error across aggregate experiments. However, on more challenging subsets lacking clear structural features, learned methods demonstrate improved robustness, successfully reconstructing multiple views where SIFT fails. SuperPoint provides broader spatial coverage, while DISK produces denser clusters in high-confidence regions, highlighting complementary behaviors between learned approaches.</p> <p>Overall, the findings indicate that classical methods remain strong baselines for Arctic UAV photogrammetry under standard SfM pipelines, while learned detectors offer advantages in difficult conditions. The observed performance gap is attributed to domain mismatch and backend optimization for handcrafted features. These results suggest that domain-specific training and improved spatial feature distribution are promising directions for advancing learned keypoint methods in Arctic reconstruction tasks.