AERIAL TRIANGULATION WITH LEARNING-BASED TIE POINTS

10 citations as recorded by crossref.

1. Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching A. Kostrzewa et al. https://doi.org/10.3390/app14093635
2. Enhancing the Accuracy of Digital Terrain Model in Complex Coastal Landscape: Case Study at Jupiter Inlet Lighthouse ONA, Florida N. Manikkavasagam et al. https://doi.org/10.1061/JSUED2.SUENG-1599
3. AI Implementation Roadmap for Automated HBIM: Toward Standardised Digital Workflows for UK Cultural Heritage A. Gil & Y. Arayici https://doi.org/10.3390/buildings16050921
4. 2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery Y. Ren et al. https://doi.org/10.3390/drones7090558
5. Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring F. Ioli et al. https://doi.org/10.1007/s41064-023-00272-w
6. A Visual Odometry Pipeline for Real-Time UAS Geopositioning J. Wei & A. Yilmaz https://doi.org/10.3390/drones7090569
7. Unlocking the Past: A review of digital processing of historical aerial and satellite stereo analog imagery for geoscience applications L. Piermattei et al. https://doi.org/10.1109/MGRS.2025.3645144
8. ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry D. Marelli et al. https://doi.org/10.1016/j.isprsjprs.2023.03.002
9. Integrating GNSS/IMU and DEM data for precise aerial triangulation: Insights from airborne hybrid systems in upper Egypt A. Wahba et al. https://doi.org/10.1016/j.ejrs.2025.04.004
10. Uncertainty quantification framework for aerial and UAV photogrammetry through error propagation D. Huang & R. Qin https://doi.org/10.1016/j.isprsjprs.2026.04.047