Abstract: One such research area is building detection, which has a high influence and potential impact in urban planning, disaster management, and construction development. Classifying buildings using satellite images is a difficult task due to building designs, shapes, and complex backgrounds which lead to occlusion between buildings. The current study introduces a new method for constructing recognition and classification globally based on Google Maps contour trace detection and an evolved image processing technique, seeking synergies with a systematic methodology. We first extract the building outlines by taking the image from the ¨Roadmap¨view in Google Maps, converting it to gray scale, thresholding it to create binary boundaries,and finally applying morphological operations to facilitate noise removal and gap filling. These binary outlines are overlaid on colorful satellite imagery, which aids in identifying buildings. Machine learning techniques can also be used to improve aspect ratio analysis and improve overall detection accuracy and performance.

Arbab Sufyan Wadood, Ahthasham Sajid, Muhammad Mansoor Alam, Mazliham MohD Su’ud, Arshad Mehmood and Inam Ullah Khan, “Building Detection from Satellite Imagery Using Morphological Operations and Contour Analysis over Google Maps Roadmap Outlines” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01601119

@article{Wadood2025,
title = {Building Detection from Satellite Imagery Using Morphological Operations and Contour Analysis over Google Maps Roadmap Outlines},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01601119},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01601119},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Arbab Sufyan Wadood and Ahthasham Sajid and Muhammad Mansoor Alam and Mazliham MohD Su’ud and Arshad Mehmood and Inam Ullah Khan}
}