arXiv:2510.27224v1 Announce Type: new 
Abstract: Accurate building instance segmentation and height classification are critical for urban planning, 3D city modeling, and infrastructure monitoring. This paper presents a detailed analysis of YOLOv11, the recent advancement in the YOLO series of deep learning models, focusing on its application to joint building extraction and discrete height classification from satellite imagery. YOLOv11 builds on the strengths of earlier YOLO models by introducing a more efficient architecture that better combines features at different scales, improves object localization accuracy, and enhances performance in complex urban scenes. Using the DFC2023 Track 2 dataset -- which includes over 125,000 annotated buildings across 12 cities -- we evaluate YOLOv11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLOv11 achieves strong instance segmentation performance with 60.4\% mAP@50 and 38.3\% mAP@50--95 while maintaining robust classification accuracy across five predefined height tiers. The model excels in handling occlusions, complex building shapes, and class imbalance, particularly for rare high-rise structures. Comparative analysis confirms that YOLOv11 outperforms earlier multitask frameworks in both detection accuracy and inference speed, making it well-suited for real-time, large-scale urban mapping. This research highlights YOLOv11's potential to advance semantic urban reconstruction through streamlined categorical height modeling, offering actionable insights for future developments in remote sensing and geospatial intelligence.

تسلط ورقة جديدة الضوء على نموذج YOLOv11 المبتكر، الذي يعزز من تقسيم المباني وتصنيف الارتفاعات باستخدام صور الأقمار الصناعية. هذه التطورات مهمة للتخطيط الحضري ومراقبة البنية التحتية، حيث تسمح بجمع بيانات أكثر دقة وكفاءة. من خلال تحسين كيفية استخراج وتصنيف معلومات المباني، يمكن أن يحول YOLOv11 عمليات نمذجة المدن والتخطيط، مما يجعلها أكثر فعالية واستجابة للاحتياجات الحضرية.

Un nuevo artículo destaca el innovador modelo YOLOv11, que mejora la segmentación de instancias de edificios y la clasificación de alturas utilizando imágenes de satélite. Este avance es significativo para la planificación urbana y el monitoreo de infraestructuras, ya que permite una recolección de datos más precisa y eficiente. Al mejorar la forma en que extraemos y clasificamos la información sobre edificios, YOLOv11 podría transformar los procesos de modelado y planificación de ciudades, haciéndolos más efectivos y receptivos a las necesidades urbanas.

Un nouvel article met en avant le modèle innovant YOLOv11, qui améliore la segmentation des instances de bâtiments et la classification de la hauteur à l'aide d'images satellites. Cette avancée est significative pour l'urbanisme et le suivi des infrastructures, car elle permet une collecte de données plus précise et efficace. En améliorant la manière dont nous extrayons et classifions les informations sur les bâtiments, YOLOv11 pourrait transformer les processus de modélisation et de planification urbaine, les rendant plus efficaces et réactifs aux besoins urbains.

A new paper highlights the innovative YOLOv11 model, which enhances building instance segmentation and height classification using satellite imagery. This advancement is significant for urban planning and infrastructure monitoring, as it allows for more accurate and efficient data collection. By improving how we extract and classify building information, YOLOv11 could transform city modeling and planning processes, making them more effective and responsive to urban needs.

Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery

One More Thing in AI – Your Shortcut to AI Mastery

Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery

Was this article worth reading? Share it

One More Thing in AI

VSDECO

Heights Platform

Lenso.ai

OneSky Localization Agent

AIPortalX

Ready to build your own newsroom?