Abstract:

The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, segmentation of closely built structures with variable sizes and no definite pattern, a common trait in developing countries, is still a challenging task. Accurate segmentation information of residential and commercial buildings in a given region is of high value for many applications e.g., estimation of disaster damage and restoration, calculation of property tax, analysis of urban growth and population density in less-developed countries. In this research, we present a novel framework to extract accurate building instances in residential societies, where building density is high. We employ the state-of-the-art instance segmentation algorithm, Mask R-CNN as a backbone framework and further improve the segmentation results by adding parallel layers of deep learning models that utilize different aspects of contextual information of the region and overcome the issues of high variability in structure and building patterns. We also provide a large, annotated dataset containing over 400 images of densely built societies of Lahore city.

Evaluation Committee  

 

• Dr. Murtaza Taj (Supervisor)
• Dr. Muhammad Fareed Zaffar (Evaluator)