PREDICTING IMPERVIOUS LAND EXPANSION USING DEEP DECONVOLUTIONAL NEURAL NETWORKS

In this research, we propose a method for modeling land change using the idea of pixel-wise semantic segmentation through deep deconvolutional neural networks. This analysis is done on a watershed scale with a focus on where developed lands are predicted to expand. The novelty of our approach is in the application of deep learning in the prediction of land transformation, where we integrate high dimensional feature classes encompassing multiple variables. We introduce a method to construct cubes of land patches which include information related to the characteristics of terrain, proximity to other features, population, geo-political-boundaries, and public policy. After modeling the development expansion in a watershed using an encoder-decoder network, the accuracy of the model is computed through the Area Under the Curve of Receiver Operating Characteristics (AUC-ROC). Model performance indicates an accuracy of 80%. Future modeling should consider the use of this technique to better understand and map the spatially explicit landscape changes and aid in land use decision making.