Satellite Image Based Animal Identification System Using Deep Learning Assisted Remote Sensing Strategy

Over the past ten years, tiny Unmanned Aerial Vehicles (UAVs) have exploded in popularity for a variety of aerial monitoring applications, including livestock counting, wildlife tracking in their natural environments, and agricultural region monitoring. When used in conjunction with deep learning, they make it possible to analyze and recognize images automatically. Recently, species population detection and monitoring in remotely sensed data has been made possible with the use of deep learning, an efficient machine learning technology. Animal recognition in satellite and aerial photos is one area where deep learning approaches are finding practical use right now, and this paper intends to give an experimental review of those areas. To simplify the process of animal identification from satellite images, this study presented a new deep learning method called the Learning based Animal Sensing and Classification Model (LASCM). To test how well the method worked, it was cross-validated with the traditional Random Forest (RF) model. The primary obstacles to implementing these deep learning techniques are unbalanced datasets, tiny samples, tiny objects, picture annotation techniques, picture backgrounds, animal counting, evaluation of model performance, and uncertainty calculation. Barely and self-supervised techniques for learning, optimizing either favorable or adverse instances, improving network architecture, and sample annotation methods were all considered as potential answers. The following areas are projected to get increased attention in the next years: video-based detection; detection based on extremely high-resolution satellite images; identification of several species; novel methods for annotation; and the creation of specialized network frameworks and big foundational modelling. The proposed methodology is designed to sort out all these problems and provide an efficient animal identification scheme based on deep learning model from the satellite images.