arXiv Computer Vision
Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery
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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.
arXiv Computer Vision
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Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model
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UAV Thermal Imagery for Inert Ordnance Screening: Multi Campaign Dataset Development,Object Detection, and Practical Recommendations
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arXiv AI
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Remote Sensing and Machine Learning-Based Analysis of Land Use and Vegetation Change in Dhaka District, Bangladesh
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arXiv Machine Learning
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A multi-temporal dataset for mapping burned areas in the Brazilian Cerrado using time series of remote sensing imagery
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arXiv Computer Vision
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Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery
The paper introduces a geometry‑aware post‑detection framework that resolves overlapping plant instances in RGB UAV imagery by combining object detection with geometric clustering of plant components. It uses component centroids and radial intersection points (RIPs) to determine whether a detected region contains one or two plants, applying K‑means or Gaussian mixture models and density filtering to improve clustering accuracy. Evaluated on eggplant and tomato crops, the method achieved high F1‑scores (0.89 for eggplant, 0.75 for tomato) and demonstrated that geometric reasoning can enhance plant‑level interpretation without requiring additional sensors or retraining.