Growth in the world's population and the acceleration of industrialization and urbanization are straining already scarce natural resources and food supplies, which must scale up to keep pace with growing demand. The consequences of the resulting large-scale changes include tremendous stresses on the environment, as well as challenges to our ability to feed the world's population, which could be calamitous at the current rate of change if not managed sustainably. Meeting this challenge will require timely information on global changes; for example, the changing productivity performance of land in agriculture; the conversion of forest to farmland or plantations, and the loss of productive farmland due to urbanization; and soil and water degradation. To address these challenges in monitoring global change, this project will develop advanced machine learning techniques, especially deep learning. The project's primary focus will be on the analysis of remote sensing data, available from a variety of instruments and sensors aboard satellites through United States and international agencies. These rich datasets capture multiple facets of the natural processes and human activities that shape the physical landscape and environmental quality of our planet, and thus offer an opportunity to study and better shape the nature and impact of global changes.
This project seeks to greatly advance the state-of-the-art in machine learning techniques for analyzing the multi-scale, multi-source, spatio-temporal data about earth system processes. Specifically, this project will advance deep learning techniques to meet the challenges involved in using remote sensing data for global change monitoring. Deep learning has been successful in addressing problems in a number of domains involving complex data sets with spatial and temporal (sequential) information such as vision, video, and natural language processing. The promise of deep learning mainly stems from its capacity to exploit complex mapping relationships and extract discriminative features over space and time using large volumes of training data. Consequently, there has been a flurry of activity in applying deep learning techniques to remote sensing data. However, due to challenges that are unique to environmental applications, off-the-shelf deep learning techniques developed for related applications such as computer vision have limited utility. This research will address these challenges and advance the state-of-the-art in deep learning techniques by developing techniques that can make use of the underlying complex, multi-scale, spatio-temporal earth system processes for global change monitoring. Methods developed in this project are expected to also have an impact across many disciplines that deal with large-scale time-varying data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
