4 accepted papers
Deep learning models commonly benefit from data augmentation techniques to diversify the set of training images. When working with satellite imagery, it is common for practitioners to apply a limited set of transformations developed for natural images (e.g., flip and rotate) to expand the training s…
Citizen science biodiversity data present great opportunities for ecology and conservation across vast spatial and temporal scales. However, the opportunistic nature of these data lacks the sampling structure required by modeling methodologies that address a pervasive challenge in ecological data co…
Under-counted matrix completion (UC-MC) has many important applications, especially in epidemiology and ecology where the observed data are often smaller than the actual numbers. Existing works model the under-counting effects using entry-wise miss detection probabilities, which are usually formulat…
This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to b…