# Identification of Object-Based Residential Density Using the Mask R-CNN Method
> Ekawaty Y.

URL kanonis: https://discover.unhas.ac.id/publications/identification-of-object-based-residential-density-using-the-mask-r-cnn-method
Jurnal / Konferensi: Proceedings 3rd International Conference on Artificial Intelligence and Machine Learning Applications Healthcare and Internet of Things Aimla 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/AIMLA63829.2025.11041121
Citations: 0

## Authors
- Ekawaty Y.

## Abstract
This research employs the Mask R-CNN algorithm to identify and categorize residential density in Makassar City utilizing satellite imagery. This algorithm allows for pixel-based object segmentation with high precision so that residential areas can be grouped into three density categories: high, medium, and low. The satellite imagery dataset was obtained from Google Earth, manually annotated using polygons, and trained using Mask R-CNN with the ResNet-50 backbone. The evaluation means Average Precision (mAP), loss graphs, and confusion matrices. The results showed an overall accuracy of93%, with an accuracy of90% for the high-density category and 80% for the medium and low categories, respectively. This algorithm provides an in-depth understanding of urban density patterns and spatial relationships between structures, thus supporting more effective urban planning. These findings are expected to help the government formulate data-based development policies to overcome population density problems, especially in urban areas while improving the community's quality of life through sustainable regional management.

## Keywords
- Computer science
- Identification (biology)
- Artificial intelligence
- Computer vision
- Object (grammar)
- Pattern recognition (psychology)
- Cognitive neuroscience of visual object recognition
- Botany
- Biology

---
Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
Saat mengutip, gunakan DOI bila tersedia atau URL kanonis di atas.