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Related Experiment Videos

A Unified Multidimensional Benchmark and Multi-Dataset Evaluation of YOLO-Based Models for Remote Sensing Building

Zhengsheng Chen1, Junjie Xu1, Dongdong Guan1

  • 1PLA Rocket Force University of Engineering, Xi'an 710025, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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This study benchmarks remote sensing building instance segmentation models, finding YOLOv11x-seg offers top accuracy and YOLOv11m-seg balances accuracy with speed. These results aid selecting models for urban management and disaster assessment.

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Building instance segmentation in remote sensing is crucial for urban management, disaster assessment, and land-cover monitoring.
  • Challenges include variations in building scale, dense distribution, complex backgrounds, shadows, and occlusions, impacting accuracy, boundary recovery, and efficiency.
  • Existing benchmarks lack a unified, multidimensional evaluation covering diverse aspects like accuracy, speed, and robustness.

Purpose of the Study:

  • To establish a unified, multidimensional benchmark for evaluating remote sensing building instance segmentation models.
  • To compare the performance of leading mask-predicting models (YOLOv8-seg, YOLOv11-seg, YOLO26-seg, Mask R-CNN) and auxiliary detection baselines (RT-DETR).
  • To assess model transferability across datasets and robustness against degradation factors like shadows, occlusions, and blur.
Keywords:
YOLObuilding segmentationmodel evaluationremote sensing

Related Experiment Videos

Main Methods:

  • Implemented a consistent training and evaluation framework for mask-predicting instance segmentation models.
  • Included benchmark metrics for bounding-box detection, mask-based segmentation, inference efficiency, model complexity, and training behavior.
  • Conducted zero-shot cross-dataset testing (WHU-to-Inria), in-domain training/testing with varied initializations, and controlled degradation tests.

Main Results:

  • High-capacity YOLO-seg models demonstrated strong competitiveness, with YOLOv11x-seg achieving top mask-based accuracy and YOLOv11m-seg offering a superior balance of accuracy, speed, and complexity.
  • Zero-shot WHU-to-Inria testing highlighted a significant domain shift, but in-domain training enabled YOLO-seg models to regain competitive performance.
  • YOLOv11x-seg exhibited greater robustness to shadow/occlusion compared to Gaussian blur, indicating differential sensitivity to image degradations.

Conclusions:

  • The developed benchmark provides crucial evidence for selecting remote sensing building instance segmentation models based on specific deployment needs (accuracy vs. efficiency).
  • YOLO-seg models, particularly YOLOv11x-seg and YOLOv11m-seg, are highly competitive and adaptable for various remote sensing applications.
  • Understanding domain shift and degradation robustness is essential for reliable real-world deployment of building instance segmentation systems.