Deep learning approach for land use images classification

Abstract

CNN (convolutional neural networks) are a category of neural networks that are majorly used for image classification and recognition. This Deep Learning (DL) technique is used to solve complex problems, particularly for environmental protection, its approaches have affected several domains without exception, geospatial world is one vised domain. In this paper we aim to classify aerial images of Tangier region, city located in north of Morocco, by using pixel based image classification with convolutional Neural Networks. Flickr API is used to get our test images dataset. These images are used as input to a pretrained network Resnet18, a small convolution neural network architecture, which is able to recognize 21 land use classes of images. Our methodology is based on the following steps, first we set up the data, and then we re-train the cited Deep Learning model (Transfer Learning) and perform a quick and visual verification, by generating a labeled map from the geotagged images, labels correspond to class provided by the CNN neural network.