# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 1 Introduction  
With the impacts of global climate change and habitat loss, wild animals are facing unprecedented threats to their survival [1, 2]. For instance, rising temperatures, increased extreme weather events, and rising sea levels all pose threats to the survival of wildlife [1]. The global urbanization and agricultural expansion have led to large-scale habitat destruction and loss. This has deprived many wild animals of places to live and breed [2]. Moreover, the decline of biodiversity has also exacerbated human concerns about environmental sustainability [3]. As a result, the issue of wildlife conservation has received extensive international attention [4]. In this context, governments and international organizations have taken various initiatives in an attempt to curb the decline of wildlife populations and to improve the efficiency of conservation through the use of scientific and technological means [1]. In recent years, aerial imagery has been extensively utilized in wildlife conservation due to the fast-paced evolution of drones and other aerial platforms [5]. Moreover, Unmanned Aerial Vehicles (UAVs) offer several advantages in aerial imagery due to their high altitude and ability to carry high-resolution camera payloads. Aerial imagery can, therefore, provide humans with valuable information in complex and harsh natural environments in a relatively short period of time [6]. For instance, high-resolution aerial photographscan accurately identify wildlife categories and population numbers, facilitating wildlife population counts and assessments of their endangerment levels [7]. Similarly, aerial photographs with broader coverage can detect wildlife in remote or otherwise inaccessible areas [8]. The wide field of view is advantageous as it enables the aerial images to observe a large area, covering several square kilometers, while simultaneously detecting individuals and groups of animals that are otherwise difficult to access [9]. Various methods have been explored for detecting wildlife on aerial images, including target detection algorithms, semantic segmentation algorithms, and deep learning methods [10; 11; 12; 13; 14]. For instance, [PERSON] et al. [11] used convolutional neural networks to monitor herds of cattle with drones. [PERSON] et al. [12] employed high-resolution UAV images to establish an accurate ground truth. They utilized a target detection-based method using YOLOv5 to achieve animal localization and counting on Australian farms. Similarly, [PERSON] et al. [14] counted sea lions and African elephants in aerial images by utilizing a UNet model based on semantic segmentation. Furthermore, wildlife detection using deep learning techniques and aerial imagery can rapidly locate wildlife, aiding in the planning of effective protection and search activities [15]. Additionally, it can determine the living conditions of wildlife in dangerous and complex natural habitats such as swamps and forests [16]. In a word, deep learning models, such as YOLO, have emerged as a significant approach in UAV aerial imagery-based applications for wildlife detection. The success of deep learning in wildlife detection with drones relies on having a large amount of real-world data available for training algorithms [17]. For example, training on large datasets like ImageNet [18] and MSCOCO [19] was then followed by transfer learning on actual data. Unfortunately, the extremely high cost of acquiring high-quality aerial images makes obtaining real transfer learning data challenging, not to mention constructing large-scale aerial wildlife datasets. That is to say, due to the lack of large-scale aerial wildlife image datasets, most current methods involve adapting object detection algorithms developed for natural scene images to aerial images, which is not suitable for wildlife detection [20]. These challenges contribute to the current shortcomings of drone-based wildlife detection algorithms, including low accuracy, weak robustness, and unsatisfactory practical application outcomes [21; 22].