  Palms play an outsized role in tropical forests and are important resources
for humans and wildlife. A central question in tropical ecosystems is
understanding palm distribution and abundance. However, accurately identifying
and localizing palms in geospatial imagery presents significant challenges due
to dense vegetation, overlapping canopies, and variable lighting conditions in
mixed-forest landscapes. Addressing this, we introduce PalmProbNet, a
probabilistic approach utilizing transfer learning to analyze high-resolution
UAV-derived orthomosaic imagery, enabling the detection of palm trees within
the dense canopy of the Ecuadorian Rainforest. This approach represents a
substantial advancement in automated palm detection, effectively pinpointing
palm presence and locality in mixed tropical rainforests. Our process begins by
generating an orthomosaic image from UAV images, from which we extract and
label palm and non-palm image patches in two distinct sizes. These patches are
then used to train models with an identical architecture, consisting of an
unaltered pre-trained ResNet-18 and a Multilayer Perceptron (MLP) with
specifically trained parameters. Subsequently, PalmProbNet employs a sliding
window technique on the landscape orthomosaic, using both small and large
window sizes to generate a probability heatmap. This heatmap effectively
visualizes the distribution of palms, showcasing the scalability and
adaptability of our approach in various forest densities. Despite the
challenging terrain, our method demonstrated remarkable performance, achieving
an accuracy of 97.32% and a Cohen's kappa of 94.59% in testing.
