Back to datasets
Dataset assetOpen Source CommunityPoint Cloud DataFacade Analysis

ZAHA

ZAHA is currently the largest benchmark dataset for point‑cloud façade semantic segmentation, containing 601 million labeled points, introducing the Facade Generalisation Level (LoFG) to support hierarchical understanding of building façades, covering diverse architectural styles, and providing both local and global (UTM) coordinate reference systems.

Source
github
Created
Nov 6, 2024
Updated
Nov 7, 2024
Signals
535 views
Availability
Linked source ready
Overview

Dataset description and usage context

ZAHA Dataset Overview

Dataset Introduction

ZAHA is presently the largest benchmark for point‑cloud façade semantic segmentation.

Highlights

  • 601 million labeled points
  • Introduces LoFG (Facade Generalisation Level) for hierarchical façade understanding
  • Covers multiple architectural styles
  • Provides local and global (UTM) coordinate reference systems
  • Filenames reference official Bavarian CityGML LoD2 building models
  • Includes configuration files for adding custom data

Segmentation Results

LoFG2 Results

ModelOAPRF1IoU
PointNet71.969.668.168.155.8
PointNet++75.573.073.072.659.8
Point Transformer78.275.876.676.163.9
DGCNN82.680.081.880.468.5

LoFG3 Results

ModelOAPRF1IoU
PointNet59.946.142.238.726.4
PointNet++66.437.835.934.825.6
Point Transformer75.052.754.752.141.6
DGCNN71.153.645.844.533.4

Download

Citation

@article{wysockietalZAHA,
  author = {Wysocki, O. and Tan, Y. and Froech, T. and Xia, Y. and Wysocki, M. and Hoegner, L. and Cremers, D. and Holst, C.},
  title = {ZAHA: Introducing the Level of Facade Generalization and the Large‑Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year = {2025},
}
Need downstream help?

Pair the dataset with AI analysis and content workflows.

Once the source passes your review, move straight into summarization, transformation, report drafting, or presentation generation with the JuheAI toolchain.