The use of Region-based Convolutional Neural Network Model for Analysing Unmanned Aerial Vehicle Remote Sensing

Object detection is a fundamental task in the geospatial research. Researchers have used approaches such as computer vision and image processing, which are time consuming and cumbersome. This approach has inadvertently hindered the processing of geospatial imagery products both in real time and offline. However, Artificial Intelligence offers great processing power to overcome these limitation using improved classification efficiency. This research work employs the use of Faster Region-based Convolutional Neural Network(Faster R-CNN) to detect Unmanned Aerial Vehicle(UAV) images. The methodology entails capturing UAV data, training and validation dataset were annotated and prepared on various object classes using PASCAL VOC standard, training was performed using the Faster R-CNN model, the training model was validated with qualitative and quantitative approaches. Results from the experiment is a mean average precision of 0.87 over all sampled test images when classifying and localizing objects. To this end, it was concluded that deep learning can be used by geospatial analyst to solve visual recognition problems.