Enhancing Urban Mapping in Indonesia with YOLOv11

Object recognition in urban and residential settings has become more vital for urban planning, real estate evaluation, and geographic mapping applications. This study presents an innovative methodology for house detection with YOLOv11, an advanced deep-learning object detection model. YOLO is based on a Convolutional Neutral Network (CNN), a type of deep learning model well suited for image analysis. In the case of YOLO, it is designed specifically for real-time object detection in images and videos. The suggested method utilizes sophisticated computer vision algorithms to recognize residential buildings precisely according to their roofing attributes. This study illustrates the potential of color-based roof categorization to improve spatial analysis and automated mapping technologies through meticulous dataset preparation, model training, and rigorous validation. This research enhances the field by introducing a rigorous methodology for accurate house detection relevant to urban development, geographic information systems, and automated remote sensing applications. By leveraging the power of deep learning and computer vision, this approach not only improves the efficiency of urban planning processes but also contributes to the development of more resilient and adaptive urban environments.

@article{Kusputra2025,
  title     = {Enhancing Urban Mapping in Indonesia with YOLOv11},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Emir Kusputra and Alesandra Zhegita Helga Prabowo and Kamel and Hady Pranoto},
  doi       = {10.14569/IJACSA.2025.0160261},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160261}
}