Integration of Texture and Spectral Response with AI Techniques for Buildings Footprint Identification Using High-Resolution Satellite Images

Abstract

The rapid growth and advancement in the application of remote sensing (RS) and geographic information systems (GIS) in several application domains helped many researchers to analyze a wide range of information from satellite imagery. Settlement information that includes building foot prints is a very essential parameter for different applications such as urban planning, environmental planning and disaster management. VGG-16 (Visual Geometry Group) conventional neural network (CNN) model is a popular model used to detect and classify the input images. In this study, a new approach was proposed by integration of VGG-16-CNN model with spectral and textural information of satellite images for identification of building foot prints. The model was trained and implemented to identify the building footprints using Worldview-3 high-resolution satellite image over part of Mumbai city of Maharashtra state, India. Classification accuracy in the proposed approach is observed to be nearly 94% as compared to 82% in case of single-shot detector (SSD) algorithm alone. Metric parameters such as F1 score of 0.957, intersection over union (IoU) of 94.86% and total error rate of 8.133% also indicated better performance of the proposed approach. Particularly, the approach is highly beneficial for urban development authorities as they need to monitor the large number of vacant lands spread across urban areas.