Aerial House Detection for Location Intelligence

Contributing to the development of a computer vision pipeline that detects and counts residential buildings from aerial imagery, enabling location intelligence for business feasibility analysis.

Estimating residential density traditionally requires manual surveys, making location analysis expensive, slow, and difficult to scale.

Prepared and curated large-scale aerial image datasets while contributing to the object detection pipeline using YOLOv8. The system estimates residential density within configurable geographic areas to support business decision-making.

• Built a custom dataset by combining publicly available aerial imagery with manually collected data.
• Established annotation guidelines to maintain dataset consistency across varying image resolutions and perspectives.
• Focused on improving data quality to increase model robustness against different roof colors, shapes, and scales.

• High visual variation between residential buildings.
• Differences in image resolution and acquisition altitude.
• Occlusion and dense urban environments.

• High-quality datasets are often more valuable than increasingly complex model architectures.
• Consistent annotation standards significantly improve model reliability.
• Geospatial computer vision introduces challenges beyond conventional object detection.

• Instance segmentation.
• Building footprint estimation.
• Population estimation.
• GIS integration.
• Multi-scale object detection.