arXiv Computer Vision By Wojciech Gruszczy\'nski, Edyta Puniach, Pawe{\l} \'Cwi\k{a}ka{\l}a, Wojciech Matwij

Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model

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arXiv Machine Learning
Aug 11

Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data

arXiv:2608. 07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield.

By Vaishali Swaminathan, Nithya Rajan, J Alex Thomasson, Amrit Shrestha, Karem Meza Capcha, Robert Hardin, Pramod Pokhrel
arXiv Computer Vision
22h ago

The Impact of Processing Parameters on High-Accuracy Measurements in UAV Photogrammetry

The paper investigates how processing parameters affect the accuracy of UAV photogrammetry. By testing 768 processing variants on ten datasets over 1.5 years, the study finds that the best configuration yields an RMSE of 16 mm, while the worst reaches 303 mm. Key factors include the number of ground control points, camera calibration corrections, and the use of Post‑Processing Kinematic GNSS for camera center determination, which together reduce systematic errors by more than half.

By Pawe{\l} \'Cwi\k{a}ka{\l}a, Edyta Puniach, El\.zbieta Pastucha, Wojciech Gruszczy\'nski
arXiv AI
Jul 14

Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation

arXiv:2607. 11412v1 Announce Type: cross Abstract: Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical.

By Ritu Yadav, Andrea Nascetti, Yifang Ban