An adaptive window and resolution-aware detection framework for dense small-object mapping from very-high-resolution satellite imagery

Detecting wildebeest from very-high-resolution (VHR) satellite imagery enables large-area population monitoring in a single acquisition, avoiding aircraft-induced disturbance and reducing sampling bias caused by transect-based surveys. However, wildebeest appear as extremely small objects in satellite images, and direct application of classical object detectors (e.g., YOLO-style detectors) often yields poor performance. In particular, high-density aggregation areas suffer from severe missed detections due to scale mismatch and limitations in post-processing for densely packed small objects.

To address these challenges, we develop a targeted detection solution that integrates (1) an adaptive sliding-window strategy to better capture local context under varying density conditions, (2) resolution–detector adaptation to mitigate scale mismatch between object size and detector design, and (3) improved post-processing modules, including an enhanced non-maximum suppression (NMS) tailored for dense small-object scenarios. We evaluate the proposed framework using WorldView-2 and WorldView-3 imagery over the Serengeti acquired in 2022 and 2023. The overall F1-score improves from 0.727 to 0.770 in 2022 and from 0.682 to 0.756 in 2023. Notably, in high-density areas in 2022, the F1-score increases from 0.330 to 0.821, demonstrating that our approach effectively reduces missed detections in dense small-object scenarios that commonly lead to substantial omissions in traditional pipelines.

Beyond wildebeest monitoring, our results highlight a generalizable pathway for adapting classical detectors to dense small-object detection in VHR satellite imagery, where objects are tiny and crowded.