Exploration in Mapping Kernel-Based Home Range Models from Remote Sensing Imagery with Conditional Adversarial Networks

Abstract

Kernel-based home range models are widely-used to estimate animal habitats and develop conservation strategies. They provide a probabilistic measure of animal space use instead of assuming the uniform utilization within an outside boundary. However, this type of models estimates the home ranges from animal relocations, and the inadequate locational data often prevents scientists from applying them in long-term and large-scale research. In this paper, we propose an end-to-end deep learning framework to simulate kernel home range models. We use the conditional adversarial network as a supervised model to learn the home range mapping from time-series remote sensing imagery. Our approach enables scientists to eliminate the persistent dependence on locational data in home range analysis. In experiments, we illustrate our approach by mapping the home ranges of Bar-headed Geese in Qinghai Lake area. The proposed framework outperforms all baselines in both qualitative and quantitative evaluations, achieving visually recognizable results and high mapping accuracy. The experiment also shows that learning the mapping between images is a more effective way to map such complex targets than traditional pixel-based schemes.