WORKING GROUPS of ISPRS COMMISSION II
INTERCOMMISSION WGs of ISPRS COMMISSION II
WG II/1 - Image Orientation
WG II/1 Terms of Reference
- Orientation of non-conventional image sources, i.e. oblique images, cameras with rolling shutter, crowd-sourced images
- Feature extraction and stereo/multi-view sparse matching
- Image alignment and applications such as mosaicking, denoising, deblurring, super resolution, etc.
- Direct and indirect geo-referencing
- Intrinsic/extrinsic camera calibration including methods based on single images, online approaches, or handling ambiguous and degenerate configurations
- Structure from Motion and SLAM
- Geometric/algebraic computer vision, multi-image geometry, and modern approaches to Bundle Adjustment, e.g. large-scale or structureless BA
- Evaluation of performance, reliability, robustness and generality of methods
WG II/2 - Point Cloud Generation
WG II/2 Terms of Reference
- Stereo and Multi-View-Stereo approaches for terrestrial / UAV / aerial / spaceborne imagery
- Methods for mesh generation
- Filtering, fusion and integration of point clouds from different data sources or sensors for surface reconstruction
- Quality and performance evaluation of point cloud generation with respect to computational complexity, precision, robustness and scalability of methods
WG II/3 - Point Cloud Processing
WG II/3 Terms of Reference
- Development of new methodologies, algorithms and applications for point cloud processing
- Information extraction from point clouds, including low-level feature extraction, segmentation and classification
- Point cloud registration and fusion
- Cloud Computing and high-performance computing for massive point cloud processing
- Geospatial Big Data processing for point clouds
- Point cloud rendering and streaming for massive point clouds
- Point cloud processing for building information modelling (BIM)
- Ubiquitous point cloud sensing
WG II/4 - 3D Scene Reconstruction and Analysis
WG II/4 Terms of Reference
- Models and techniques for extracting features, geometrical primitives and objects from data acquired by airborne and/or terrestrial sensors, including object recognition and 3D object reconstruction, and possibly integrating information about multiple object classes and their relations within complex scenes.
- Classification and semantic segmentation of point clouds and surface meshes with or without radiometric information.
- Generation and update of high-resolution 3D city models and road databases, including mesh based, polyhedral, parametric and multiscale representations possibly with level-of-detail (LOD) and (semantic) attributes, and texturing of the resultant 3D models.
- Object detection, recognition and 3D reconstruction in the context of robotics or autonomous driving.
- Multimodal data fusion: performing any of the tasks mentioned above by exploiting the complementarity of using different viewpoints (space-borne, nadir/oblique aerial, UAV, fixed/mobile terrestrial), different sensor types (monoscopic/stereoscopic images, LiDAR, (In)SAR), and existing data (traditional cartographic products, CAD models, urban GIS).
- Assessment of efficiency and quality and of their dependence on the quality of the input data, including uncertainty analysis and uncertainty propagation, for any of the tasks mentioned above.
WG II/5 - Dynamic Scene Analysis
Michael Ying Yang
WG II/5 Terms of Reference
- Dynamic scene understanding from image sequences.
- Models and methods to determine ego-motion for photogrammetric and computer vision applications including but not limited to navigation, geo-referencing and object reconstruction.
- Detection, reconstruction, classification and tracking of single and multiple objects in image sequences
- Event reconstruction and scene analysis from single and multiple image streams.
- Offline and real-time 3D processing of image sequences (MOCAP system, mobile mapping, etc.)
- Quality assessment techniques for calibration, orientation and object detection from image sequences
- Benchmarking of object detection and semantic segmentation from image sequences
- Change detection in image time-series and/or 3D point clouds
WG II/6 - Large-scale Machine Learning for Geospatial Data Analysis
WG II/6 Terms of Reference
- Large-scale image classification,
- Machine learning, deep learning,
- Pixel-wise semantic segmentation at large-scale,
- Supervised, weakly supervised, transfer, and human-in-the-loop learning
- Multi-view, multi-temporal, multi-modal image interpretation
- Change detection and environmental / urban monitoring
WG II/7 - Vision Metrology
Key Support Personnel
Key Support Personnel
WG II/7 Terms of Reference
- Performance evaluation of active and passive systems
- Definition of accuracy and best practice
- Contribution to international standards
- System developments and industrial applications
- Very close range and large volume measurement applications
- Camera-controlled robot and machine guidance
- Measurement of dynamic processes
- Structural deformation analysis
- Medical systems and applications
- Greater involvement of industrial partners in ISPRS activities
WG II/8 - Data Acquisition and Processing in Cultural Heritage
WG II/8 Terms of Reference
- Development and promotion of data acquisition strategies, data processing techniques and data management solutions (such as the spatial information systems GIS and BIM) applicable to all subjects that can be categorized as cultural heritage.
- Integration of data and measurement techniques supporting metric and remote sensing surveys, and monitoring actions for the valorization, conservation, restoration and archiving of archeological, architectural, urban and natural landscape heritage.
- Development and dissemination of best practice protocols to aid appropriate application across related cultural heritage fields.
- Development and promotion of low-cost, rapid, innovative, automated, commercial and open-source approaches for metric and remote sensing survey of heritage assets.
- Development of both virtual and augmented reality as well as online applications and advanced visualization systems to promote the dissemination and the correct use of 3D metric surveys.
- Close co-operation with related disciplines, national / international groups (e.g. CIPA, EAA, ICOMOS etc.) and other ISPRS working groups
WG II/9 - Underwater Data Acquisition and Processing
Key Support Personnel
Higinio González Jorge
Key Support Personnel
WG II/9 Terms of Reference
- Definition of best practice for geometric calibration, color correction and validation of systems for underwater 3D measurements
- Geometric and stochastic modeling of multimedia geometry for underwater image and range measurements
- Lidar bathymetry for seafloor and water surface measurement
- Algorithms and methods for underwater localization and navigation used in ROVs, AUVs and augmented and virtual reality applications
- Combined above water, through water and underwater techniques for 3D modeling of artefacts and mapping of coastal areas
- Integration and performance evaluation of platforms such as ROVs, AUVs, towed bodies and diver controlled systems
- Underwater applications and techniques in archaeology, 3D/2D mapping, modeling and visualization, biomass estimation, habitat monitoring, metrology, inspections and volumetric reconstruction for flow tracking
WG II/10 - 3D Mapping for Environmental & Infrastructure Monitoring
Key Support Personnel
WG II/10 Terms of Reference
- Improve methodology for 3D mapping and monitoring of geohazards, geomorphology and vegetation
- Study and promote the use of 3D photogrammetric techniques for inspection and life cycle monitoring of infrastructures like bridges, buildings, dikes, and to improve on the integration with structural component analysis
- Analyse, share and promote best 3D approaches and results in biomedical applications in collaboration with the biomedical society
- Study techniques for near-continuous spatio-temporal 3D monitoring of environmental, infrastructural and biomedical processes.
- Evaluation and integration of new 3D and 2D imaging sensors for the purpose of 3D mapping for environmental and infrastructure monitoring
ICWG II/III - Pattern Analysis in Remote Sensing
ICWG II/III Terms of Reference
- Automatic identification and learning of 2D and 3D patterns in uni-modal and multi-modal remote sensing data, e.g. multi-scale aerial and satellite data; multi- and hyperspectral data; SAR-, radargrammetric and SAR-tomography data
- Automatic identification and learning of temporal patterns in remote sensing data, e.g. image-based flow estimation and learning from InSAR data (traffic, glaciers, currents, etc.); analysis-by-synthesis approaches for motion and deformation modeling with passive and active sensors
- Integration of radiometry and radiometric models into pattern recognition; radiometrically enhanced object models for range-intensity images and sequences; integration of SAR-simulation into SAR-image and analysis
- Recognition of 2D and 3D patterns in remote sensing data exploiting 3d and 4d models (GIS / CAD / BIM )