Keras Spatial Extending deep learning frameworks for preprocessing and on-the-fly augmentation of geospatial data

Aiman Soliman, Jeffrey Terstriep

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The application of deep learning techniques to remote sensing and geospatial data is a burgeoning area of research. However, most state-of-the-art systems have their roots in computer vision with procedures that do not necessarily lend themselves to remote sensing data. On the contrary, managing geospatial data require handling different projections, spatial resolutions and data formats. We present Keras Spatial, a python package for preprocessing and augmenting geospatial data. Keras Spatial provides three main components (1) a spatial data generator class, which is similar to the Keras image data generator, (2) a GeoDataFrame for storing training samples boundaries and properties, (3) a callback mechanism for on-the-fly reprojection and data augmentation.The novelty of the developed package arises due to the flexibility to combine the components in different ways to solve a variety of data preparation problems. We demonstrate the usage of Keras Spatial package by preparing digital elevation data for a segmentation model, and replacing conventional manual preprocessing steps, such as tiling rasters to samples, masking samples outside of the study area, and adding digital elevation model derivatives. We also discuss advanced data preprocessing features of this package, such as accessing remote data source directly, combining different input rasters data regardless of their native projection and resolution, and decoupling the model configuration, input layer dimensions, from the geographic scale, where the latter feature allows training the same model to recognize geographic objects that exist at hierarchical scales.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019
EditorsSong Gao, Shawn Newsam, Liang Zhao, Dalton Lunga, Yingjie Hu, Bruno Martins, Xun Zhou, Feng Chen
PublisherAssociation for Computing Machinery, Inc
Pages69-76
Number of pages8
ISBN (Electronic)9781450369572
DOIs
StatePublished - Nov 5 2019
Event3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019 - Chicago, United States
Duration: Nov 5 2019 → …

Publication series

NameProceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019

Conference

Conference3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019
CountryUnited States
CityChicago
Period11/5/19 → …

Keywords

  • Deep Learning
  • Geospatial Big Data
  • Image Preprocessing
  • Remote Sensing
  • Scientific Reproducibility

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Keras Spatial Extending deep learning frameworks for preprocessing and on-the-fly augmentation of geospatial data'. Together they form a unique fingerprint.

  • Cite this

    Soliman, A., & Terstriep, J. (2019). Keras Spatial Extending deep learning frameworks for preprocessing and on-the-fly augmentation of geospatial data. In S. Gao, S. Newsam, L. Zhao, D. Lunga, Y. Hu, B. Martins, X. Zhou, & F. Chen (Eds.), Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019 (pp. 69-76). (Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3356471.3365240