Integrating Remote Sensing and Artificial Intelligence: A Review of Technological Innovations in Wild Life Crime Detection

Wildlife crime remains one of the biggest global challenges worldwide even up to the current decade. This is even worse when it is compounded by what is referred to as the 'dark figure' where cases go unreported or are not detected by law enforcement agencies due to victim reluctance or sheer inability of the police to arrest all offenders. To address this issue, scientists have used remote sensing techniques to detect illegality using satellite and other aerial images such as drone and airplane images. Since the amount of remote sensing data is growing very rapidly, and increasing even more in the future, efficient computational systems and sophisticated preprocessing methods are essential for handling and analyzing these data. Artificial Intelligence(AI) has been instrumental in this area through functions like object recognition, data integration, filtering, and anomaly detection that aid the efficiency and accuracy of remote sensing exercises. Overcoming scaling challenges, enhancing engagement, and navigating privacy hurdles remain vital for the implementation of live AI-based models. This review article will also try to give a measure of the likelihood of technological solutions to prevent wildlife crimes and the overriding issue of the 'dark figure' and the expanding mass of data through a critical analysis of the literature on the respective remote sensing devices and the AI algorithms that may be used to combine them.