# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 2 Related Work  
### Diverse Datasets for Wildlife Detection  
Currently, there are numerous publicly available animal datasets based on ground images as shown in Table 1. As seen from Table 1, [PERSON] et al. [40] introduced the Animal10N dataset, containing 55,000 images from five pairs of animals: (cat, lynx), (tiger, cheetah), (wolf, coyote), (gorilla, chimpanzee), and (hamster, guinea pig). The training set comprises 50,000 images, whereas the test set has 5000 images. [PERSON] et al. [41] presented the Animal Kingdom dataset, a vast and diverse collection offering annotated tasks to comprehensively understand natural animal behavior. It includes 50 h of annotated videos for locating relevant animal behavior segments in lengthy videos, 30 K video sequences for fine-grained multi-label action recognition tasks, and 33 K frames for pose estimation tasks across 850 species of six major animal phyla. [PERSON] et al. [42] introduced the Animal-Pose dataset for animal pose estimation, covering five categories: dogs, cats, cows, horses, and sheep, with over 6000 instances from 4000+ images. Additionally, it provides bounding box annotations for seven animal categories: otter, lynx, rhinoceros, hippopotamus, gorilla, bear, and antelope. AnimalWeb [43] is a large-scale, hierarchically annotated animal face dataset, encompassing 22.4 K faces from 350 species and 21 animal families. These facial images were captured in the wild under field conditions and annotated with nine key facial landmarks consistently across the dataset. However, these existing datasets may not sufficiently support the demands of comprehensive aerial wildlife monitoring. For instance, the Animal10 dataset focuses on animal detection rather than wildlife detection in natural environments. The Animal Kingdom and Animal-Pose datasets were not specifically designed for wildlife detection in drone aerial images. In addition, most publicly available aerial image datasets are concentrated in urban areas and primarily used for tasks like building detection, vehicle detection or segmentation, and road planning [44; 45; 46]. However, to the best of the author's knowledge, there are only a few public datasets available for animal detection in aerial images, such as the NOAA Arctic Seal dataset [47] and the Aerial Elephant Dataset (AED) [48]. The NOAA Arctic Seal dataset comprises approximately 80,000 color and infrared (thermal) images, collected during flights conducted by the NOAA Alaska Fisheries Science Center in Alaska in 2019. The images are annotated with around 28,000 ice seal bounding boxes (14,000 on color images and 14,000 on thermal images). The AED is a challenging dataset containing 2074 images, including a total of 15,581 African forest elephants in their natural habitat. The imaging was conducted with consistent methods across a range of background types, resolutions, and times of day. However, these datasets are annotated specifically for certain species. In addition to this, there are a number of small-scale aerial wildlife datasets (e.g., elephants or camels) available on data collection platforms (e.g., rebowflow). In summary, there are three limitations to the current aerial wildlife detection datasets. First, the current aerial wildlife datasets are small in size and not species-rich. Second, these datasets often suffer from limited image quantity and inconsistent annotation quality. Third, there are no open-source, large-scale, high-quality wildlife aerial photography datasets yet. In summary,