ISPRS

🏆 Final post in our ISPRS Best Paper Awards 2022–2025 series.

We are pleased to announce that the paper:

“Word2Scene: Efficient remote sensing image scene generation with only one word via hybrid intelligence and low-rank representation”

by Jiaxin Ren, Wanzeng Liu, Jun Chen, Shunxi Yin, and Yuan Tao

has been selected as the recipient of the U. V. Helava Award – Best Paper 2022–2025, recognizing the best paper published in the ISPRS Journal of Photogrammetry and Remote Sensing during the four-year period 2022–2025.

The award-winning research introduces Word2Scene, an innovative framework for generating realistic remote sensing imagery from a single word using hybrid intelligence and low-rank representation. The method provides strong semantic control over image generation and offers new opportunities for understanding both the capabilities and limitations of AI models in remote sensing.

The award will be presented during the XXV Congress ISPRS 2026 Toronto in July 2026.

👏 Congratulations to all authors on this outstanding achievement!

🔗 DOI: https://doi.org/10.1016/j.isprsjprs.2024.11.002
🔗 Read the full announcement:
https://www.isprs.org/news/announcements/details.aspx?ID=449

🏆 Continuing our ISPRS Best Paper Awards 2022–2025 series.

We are pleased to announce that the paper:

“Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment”

by Kyriaki Mouzakidou, Aurélien Brun, Davide Antonio Cucci, and Jan Skaloud

has been selected as the recipient of the Fritz Ackermann Award 2022–2025, recognizing the best paper published in the ISPRS Open Journal of Photogrammetry and Remote Sensing during the evaluation period 2021–2025.

The award-winning research addresses a key challenge in photogrammetry: improving the accuracy of drone-based mapping through tightly coupled sensor orientation and concurrent adjustment. The study demonstrates significant improvements in trajectory attitude estimation and georeferencing accuracy, with broad relevance for photogrammetry and geospatial applications.

The award will be presented during the XXV ISPRS Congress in Toronto, Canada, in July 2026.

👏 Congratulations to all authors on this outstanding achievement!

🔗 DOI: https://doi.org/10.1016/j.ophoto.2023.100057

🔗 Read the full announcement:
https://www.isprs.org/news/announcements/details.aspx?ID=451

🌍 ISPRS Elections 2026–2030

The bids and nominations for the next ISPRS term have been officially announced!

Six destinations are competing to host the XXVI ISPRS Congress 2030:
🇦🇺 Sydney
🇨🇳 Wuhan
🇰🇪 Nairobi
🇰🇷 Incheon
🇸🇦 Saudi Arabia
🇦🇪 Abu Dhabi

Council nominations and Technical Commission bids for 2026–2030 have also been announced, bringing together experts from across the globe to help lead the future of photogrammetry, remote sensing, and spatial information science.

We thank all candidates and supporting organizations for their commitment to the international geospatial community.

Full list of bids, nominations and Technical Commission proposals:
https://www.isprs.org/news/announcements/details.aspx?ID=453

🏆 The Fritz Ackermann Award – Best Paper 2025 announced

We are pleased to announce that the paper:

“Intensity-based stochastic model of terrestrial laser scanners: Methodological workflow, empirical derivation and practical benefit”

by Florian Schill, Christoph Holst, Daniel Wujanz, Jens Hartmann, and Jens-André Paffenholz

has been selected as the 2025 Best Paper from papers published exclusively in the ISPRS Open Journal of Photogrammetry and Remote Sensing.

The paper will be included in the final selection for the newly established Fritz Ackermann Award, which will be presented at the 2026 ISPRS Congress in Toronto.

Congratulations to all authors on this well-deserved recognition! 👏

DOI: https://doi.org/10.1016/j.ophoto.2024.100079

Read the full announcement:
https://www.isprs.org/society/awards/ackermann/2025.aspx

🔍 ISPRS Research Spotlight

How do we make point cloud registration research more robust and closer to real-world conditions?

PC2Model introduces a benchmark dataset for realistic point cloud-to-model registration, designed to support research in 3D vision, photogrammetry, robotics, and LiDAR applications.

The benchmark includes:
• simulated and real-world point cloud datasets
• realistic artefacts such as noise, occlusions, mixed pixels, and density variation
• ground truth transformations and standardized evaluation metrics

Designed for both classical and learning-based approaches, PC2Model provides an open resource for evaluating robust registration methods under realistic conditions.

The work is connected to the ISPRS Scientific Initiative 2025 led by Mehdi Maboudi (https://www.isprs.org/society/si/default.aspx ) and will be presented at the XXV ISPRS 2026 Toronto.

📄 Paper: https://arxiv.org/abs/2604.19596
📂 Dataset: https://zenodo.org/records/17581812
🔧 Repository & tools: https://github.com/MehdiMaboudi/PC2Model-Dataset &
https://github.com/saidharb/PC2Model