Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing


  • Johan Bjorck Cornell University
  • Brendan H. Rappazzo Cornell University
  • Qinru Shi Cornell University
  • Carrie Brown-Lima Cornell University
  • Jennifer Dean College of Environmental Science and Forestry, SUNY
  • Angela Fuller Cornell University U.S. Geological Survey New York Cooperative Fish and Wildlife Research Unit
  • Carla Gomes Cornell University


Environmental Sustainability


The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species -- invading flora and fauna that are estimated to cause damages in the billions of dollars annually. As an ongoing collaboration with the New York Natural Heritage Program, we consider the use of unsupervised deep learning techniques for dimensionality reduction of remote sensing images, which can reduce sample complexity for downstream tasks and decreases the need for large labeled data sets. We consider spatially augmenting contrastive learning by training neural networks to correctly classify two nearby patches of a landscape as such. We demonstrate that this approach improves upon previous methods and naive classification for a large-scale data set of remote sensing images derived from invasive species observations obtained over 30 years. Additionally, we simulate deployment in the field via active learning and evaluate this method on another important challenge in computational sustainability -- landcover classification -- and again find that it outperforms previous baselines.




How to Cite

Bjorck, J., Rappazzo, B. H., Shi, Q., Brown-Lima, C., Dean, J., Fuller, A., & Gomes, C. (2021). Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14711-14720. Retrieved from



AAAI Special Track on AI for Social Impact