# Harnessing Artificial Intelligence for Wildlife Conservation
## 2 Methodologies
### Detection and Classification  
Once trained, the AI models are deployed to process the data collected by the camera traps and drones. The detection and classification process involves several key steps:1. Preprocessing: Raw images and videos undergo preprocessing to enhance quality and reduce noise. This step includes resizing, rescaling, normalisation, and augmentation of the data;
2. Inference: The pre-processed data are then fed into the AI models for inference. The models analyse the images and videos to detect the presence of specific animal species, humans, and objects;
3. Classification: Detected entities are classified into predefined categories, such as species type, human presence, and potential indicators of poaching (for example, cars and people). The classification results are subsequently stored in a database for further analysis. Figures 1-4 demonstrate the robust detection and classification capabilities of Conservation AI's models across various challenging scenarios. Figure 1 illustrates one of our elephant detections using the Sub-Saharan Africa model. This detection was made by a real-time camera installed in the Welgevonden Game Reserve in Limpopo Province, South Africa. Figure 2 highlights a particularly challenging detection, which is central to the mission of Conservation AI. Captured by a real-time camera, it shows a zebra at night, detected from a considerable distance. Figure 3 demonstrates the models' proficiency in handling occlusion and heavily camouflaged animals. In this example, a deer is partially concealed behind tree branches in a forest, yet the model successfully identifies the animal. Such capabilities are crucial for comprehensive biodiversity assessments and conservation efforts. This image was processed from data uploaded by a non-real-time camera. Figure 4 presents an intricate case where our U.K. Mammals model not only detects a squirrel but accurately distinguishes it as a grey squirrel. This detection is particularly noteworthy given the challenging conditions: the animal is small, distant, and partially obscured by a tree, with poor lighting in the early morning. Figure 4: A grey squirrel detection in the early morning. Figure 3: A deer situated in a wooded forest captured using a traditional non-real-time camera trap. Images like these form the foundation of the datasets used by Conservation AI to train our models. When models are designed for camera traps, we exclusively use data from camera traps. The datasets reflect region-specific variables such as seasonality, day and night cycles, and varying weather conditions like rain and sunshine. Through the process of situated learning and precision modelling, our models undergo continuous fine-tuning, often taking up to a year to achieve the desired accuracy using data collected from the cameras we deploy. This ongoing learning and adaptation process is a unique feature of Conservation AI. Our commitment to developing long-term relationships with users and partners ensures that the models achieve the level of accuracy necessary for rigorous conservation studies. Real-Time and Non-Real-Time Capabilities  
Conservation AI offers both real-time and non-real-time detection capabilities, each serving distinct but complementary roles in conservation efforts. Real-time detection is critical for enabling immediate responses to poaching activities [17]. The AI models process data on the fly, triggering alerts to conservationists and authorities when suspicious activities are identified. Figure 5 illustrates a real-time Conservation AI camera deployment in the U.K.  
Non-real-time detection, on the other hand, involves batch processing of data collected over a specific period [18]. This approach typically utilises traditional camera traps that store data on SD cards, which must be retrieved at designated intervals for offline processing. This method is particularly valuable for long-term monitoring and the comprehensive analysis of wildlife populations and habitat health.