Safeguarding Biodiversity: Remote Forest Poaching Camp Detection with CNN and Spatial Transformer Networks

The increasing threat of poaching continues to endanger global biodiversity, calling for innovative and timely measures in detection as well as interventions. In response, this study presents a novel approach to methodology that utilizes high-resolution optical satellite imagery along with an advanced hybrid deep learning model comprising Convolutional Neural Networks (CNN) and Spatial Transformer networks (STN). The broad dataset of 25,000 images reflects the complexities of poaching activities which include temporary camps, human movement, vehicles, and abandoned camps. The hybrid model rises as an outstanding tool, which demonstrates great performance with an overall accuracy of 95.90%. Accordingly, precision, recall, and Fl-score measurements are diligently used for every class that show how well the model is performing in distinguishing complex patterns connected to various stages of poaching. Most significantly, the impressive recall of 96.52 % for detection temporary camps, is a testament to the model's proactive nature in early-onset threat mitigation - this being one of the key aspects of poaching threats control. Notably, a comprehensive analysis using confusion matrices not only verifies the accuracy of classification capabilities but also offers several subtle insights into model performance towards different classes. This fine-grained knowledge helps in the implementation of pertinent adjustments that improve model responsiveness to different environmental levels and types of comparative studies in the light of other models or baseline methods emphasizing how better is the proposed hybrid model about its several multi-classification tasks that are associated with poaching detection.