Land use land cover mapping using uas imagery: scene classification and semantic segmentation

Abstract. Land use and Land cover classification plays a vital role in understanding the changes happening on the surface of the earth. Vegetation classification can be performed by incorporating various deep learning models using Convolution Neural Network approach. The primary purpose of this research is to test the performances of pre-existing Deep Convolution Neural Network (CNN) models for vegetation classification in the Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA). Specifically, this study focuses on examining of the capacity of Scene and Pixel classification technique using certain 3 band and 5 band combinations. Eight well known scene-based deep convnets, namely VGG19, ResNet152V2, InceptionV3, EfficientNetB5, Xception, InceptionResNetV2, MobileNetV2, DenseNet201, and one important pixel classification model, UNet have been used for vegetation mapping in the North and South part of the JILONA. Among all the scene classification models, EfficientNetB5 and DenseNet201 outperformed all other models with an accuracy of over 97%. Xception and ResNet152V2 model exhibited 95% accuracy, whereas the accuracy of remaining models ranged from 85% to 92%. The classification map achieved through UNet pixel-based method, resulted with an accuracy of about 87% and an even better 91% when all 5 bands (Blue, Green, Red, RedEdge, Near-infrared) were used for training.