Spatial-Net: A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets


Knowledge discovery from spatial data is essential for many important societal applications including crop monitoring, solar energy estimation, traffic prediction and public health. This paper aims to tackle a key challenge posed by spatial data – the intrinsic spatial heterogeneity commonly embedded in their generation processes – in the context of deep learning. In related work, the early rise of convolutional neural networks showed the promising value of explicit spatial-awareness in deep architectures (i.e., preservation of spatial structure among input cells and the use of local connection). However, the issue of spatial heterogeneity has not been sufficiently explored. While recent developments have tried to incorporate awareness of spatial variability (e.g., SVANN), these methods either rely on manually-defined space partitioning or only support very limited partitions (e.g., two) due to reduction of training data. To address these limitations, we propose a Spatial-Net to simultaneously learn a space-partitioning scheme and a deep network architecture with a Significance-based Grow-and-Collapse (SIG-GAC) framework. SIG-GAC allows collaborative training between partitions and uses an exponential reduction tree to control the network size. Experiments using real-world datasets show that Spatial-Net can automatically learn the pattern underlying heterogeneous spatial process and greatly improve model performance.

In ACM SIGSPATIAL, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Jia Yu
Jia Yu

Jia Yu is a co-founder of Wherobots Inc. and leads its engineering team. Jia is the creator of Apache Sedona and was a Tenure-Track Assistant Professor of Computer Science at Washington State University from 2020 - 2023. Jia’s research interests include database systems, distributed data systems and geospatial data management.