DEEP LEARNING APPROACH APPLIED TO DRONE IMAGERY FOR REAL ESTATE TAX ASSESSMENT: CASE OF THE TAX ON UNBUILT LAND KENITRA-MOROCCO
Keywords: Property taxation, UAV Images, Unbuilt land, Automatic detection, Semantic segmentation, Deep Learning
Abstract. According to the Court of Audit, urban taxation is the main source of revenue for local authorities in almost all regions of the world. In Morocco, in particular, the tax on unbuilt urban land accounts for 35% of the revenue from taxes managed directly by the municipality. The property tax assessment system currently adopted is not regularly updated and is not properly monitored. These difficulties do not allow for a significant expansion of the land base. The current efforts aim at accelerating the census of the urban heritage using innovative and automated approaches which are intended to lead to the next generation of urban information services and the development of smart cities. In this context we propose a methodology that consists of acquisition of high-resolution UAV images. Then the training of a deep learning algorithm of semantic segmentation of the images in order to extract the characteristics defining the unbuilt land. U-Net, the deep architecture of the convolutional neural network that we have parameterized in order to adapt it to the nature of the phenomenon treated and the volume of data we have as well as the performance of the machine, offers a segmentation accuracy that reaches 98.4%.
Deep learning algorithms are seen as more promising for overcoming the difficulties of extracting semantic features from complex scenes and large differences in the appearance of unbuilt urban land. The results of prediction will be used for defining urban areas where updates are made from the perspective of tracking urban taxes.