ISPRS Benchmarks

ISPRS Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Meshes

Automated extraction of geographic objects from airborne data is an important research topic in photogrammetry and remote sensing since decades. In addition to images, 3D point clouds from airborne LiDAR and Multi-View-Stereo-Image-Matching became more and more important as basic data source. The aim of H3D is to provide state-of-the-art data sets to the community, which can be used by interested researchers to test own methods and algorithms on semantic segmentation for geospatial applications. We propose a benchmark consisting of highly dense LiDAR point clouds captured at three different epochs. The respective point clouds are manually labeled into at least 11 classes and are used to derive labeled textured 3D meshes as an alternative representation. Core features of H3D are:

  • UAV-based simultaneous data collection of both LiDAR data and imagery from the same platform
  • High density LiDAR data of 800 points/m² enriched by RGB colors of on board cameras incorporating a GSD of 2-3 cm → H3D(PC)
  • High resolution 3D textured mesh data generated from both LiDAR data and imagery in an hybrid manner → H3D(Mesh)
  • Manually set labels for the LiDAR point cloud, which are automatically transferred to the 3D mesh
  • Multi-temporal data set available for 3 different epochs (March 2018, November 2018, and March 2019) captured over the same area with the same sensor configuration (for now only the March 2018 dataset is online, the others will follow in 2021)

More information you will find here »


ISPRS Benchmark on UAVid: A semantic segmentation dataset for UAV imagery

Semantic segmentation has been one of the leading research interests in photogrammetry and computer vision in recent years. The fast development of semantic segmentation attributes enormously to the large scale datasets, especially for the deep learning related methods. We introduce our UAVid dataset, a new high-resolution UAV semantic segmentation dataset, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation. UAVid dataset is a high-resolution UAV semantic segmentation dataset focusing on street scenes.

The dataset consists of 42 video sequences (seq1 to seq42), which are captured with 4K high-resolution in oblique views.

In total, 420 images have been densely labeled with 8 classes for the semantic labeling task.

More information you will find here »


ISPRS / EuroSDR Benchmark for Multi-Platform Photogrammetry

The aim of the project is to assess the accuracy and reliability of current methods for calibration and orientation of images acquired by different platforms as well as their integration for image matching and dense point cloud generation. In current research especially the question on how large changes of perspective and scale differences need to be tackled in image orientation and (dense) image matching is not approached systematically.

By providing a new benchmark dataset consisting of state-of-the-art sensor data and covering different relevant tasks and scenarios the current status of research is identified and further works will be stimulated.

The DENSE IMAGE MATCHING (DIM) benchmark is performed in cooperation with the EuroSDR’s Scientific Initiative “Benchmark on High Density Image Matching for DSM Computation”, joining both the two available datasets on Dortmund (Germany) and Zurich (Switzerland). The efforts of both the initiatives are contributing to set up a more complete and challenging dataset, considering flights with different features in terms of GSD size and overlap.

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ISPRS Test Project on Urban Classification, 3D Building Reconstruction and Semantic Labeling

ISPRS WG II/4 is running following bechmarks:

  • Urban classification and 3D reconstruction (Vaihingen/Germany and Toronto/Canada)
  • 2D Semantic Labeling (Vaihingen/Germany and Potsdam/Germany)
  • 3D Semantic Labeling (Vaihingen/Germany)

More information you will find here »


Benchmark on High Density Aerial Image Matching

Background and Scope of the project

Innovations in matching algorithms as well as the increasing quality of digital airborne cameras considerably improved the quality of elevation data generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project “Benchmark on High Density Aerial Image Matching” aiming at evaluating the potential of photogrammetric 3D data capture in view of the ongoing developments of software for automatic image matching. Basic scope is the evaluation of 3D point clouds and DSM produced from aerial images with different software systems. Such a comparative evaluation provides a platform for software developers to demonstrate the state-of-the-art of their ongoing developments. Furthermore, it can help potential users like National Mapping and Cadastral agencies (NMCAs), which consider a state-wide-generation of high quality DSMs to understand the applicability of such tools while triggering further developments based on their needs.

As a joint test data set subsets of three aerial image blocks are provided.Two data sets cover nadir imagery, which are captured at different landuse and block geometry, while the third data set includes oblique aerial images.

More information you will find here »



Nice 2020
XXIVth ISPRS Congress:

July 4-10, 2021
June 6-10, 2022

The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.

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