Instance segmentation scheme for roofs in rural areas based on Mask R-CNN
Mark Amo-Boateng, Nana Ekow Nkwa Sey, Amprofi Ampah Amproche, Martin Kyereh Domfeh
Abstract
Rooftop detection has numerous applications such as change detection in human settlements, land encroachments, planning routes to rural areas and estimation of solar generation potential of cities. Detecting the number, type and shape of building roofs form part of preliminary procedures to perform a variety of tasks for making decisions. Assessment of rooftops in rural areas is an important task in the estimation of potential solar generation and sizing of solar PV systems which has proven to be very challenging due to the different quality, lighting conditions and resolution of aerial and satellite images. In this research, we implement a mask RCNN algorithm using TensorFlow Object Detection API to detect the rooftop of buildings in a typical rural settlement. The average precision and recall values (@ IoU = 0.5:0.05:0.95) of the trained model were 85% and 88.2% respectively. The results of the experiment show that the approach can effectively and accurately detect and segment rural rooftops from high-resolution aerial images.