Deep Learning for Agricultural Land Detection in Insular Areas
Résumé
Nowadays, governmental programs like ESA’s Copernicus provide freely available data that can be easily utilized for earth observation. In the present work, the problem of detecting agricultural and non-agricultural land cover is addressed. The methodology is based on classification with convolutional neural networks (CNNs) and transfer learning using AlexNet. The study area is located at the Ionian Islands, which include several land cover classes according to Copernicus CORINE Land Cover 2018 (CLC 2018). Furthermore, the dataset consists of natural color images acquired by Sentinel-2A multi-spectral instrument. Experimentation proves that extra addition of training data from foreign grounds, unfamiliar to the Greek data, serves much as a confusing agent regarding network performance.