An automatic approach for detecting building fences from high-resolution images: the case study of Makkah, Saudi Arabia

In the light of the constantly expanding technological advancements concerning high-resolution satellites and imaging techniques for investigative and information uses it is important to look at how these algorithms progress to acquire more reliable data in a much efficient manner. This study aims to (1) investigate the ability to use remote sensing and geographic information system techniques to extract the detecting building edges in the City of Makkah and (2) investigate new machine learning techniques to derive the illegal building fences in the study area. Two WorldView-3 images will be the first obtained for the City of Makkah in 2016 and 2018. Convolutional neural networks algorithm will be investigated to detect all the fences within the two images. These traits have been utilized to create automated object detection techniques, which are a core requirement of information extraction and large frame analysis of images covering large expanses of land. In high-resolution images, object detection identifies objects belonging to a class, locating them using a bounding box. Based on satellite images time series, the outputs will detect the changes that occurred during 2016 and 2018. A web map application will be designed as the primary tool to make it easier, illustrating the differences between the main changes. Evaluation of binary classifiers approach will be used to evaluate the outcomes of building fences based on several performances that measure data interpretation. Preliminary findings will illustrate the precision and accuracy of the used machine learning algorithm. The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting building fences for urban areas and/or suburban areas.