International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 155–159, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-155-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 155–159, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-155-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Feb 2020

14 Feb 2020

AUTOMATED LAND COVER CHANGE DETECTION THROUGH RAPID UAS UPDATES OF DIGITAL SURFACE MODELS

C. T. White1, A. Petrasova1, W. Reckling2, and H. Mitasova1,2 C. T. White et al.
  • 1Center for Geospatial Analytics, North Carolina State University, USA
  • 2Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, USA

Keywords: Google Earth Engine, GRASS GIS, PlanetScope, random forest, data fusion

Abstract. Up to date geospatial data provide the foundation for the development of smart and connected communities. While high-resolution 2D imagery is becoming widely available at less than monthly intervals and several infrastructure layers (e.g., roads, building footprints) are updated on a continuous basis, digital surface models (DSM) are generated less frequently and become quickly obsolete in rapidly developing regions. We present a methodology for continuous and efficient updates of DSM based on automated change detection from high-resolution satellite imagery that is used to develop UAS deployment plan, data acquisition, and DSM generation for targeted areas. The resulting UAS-derived DSM is then seamlessly fused with existing (usually lidar-based) DSM. We demonstrate our methodology in a rapidly developing watershed in the Triangle Region, North Carolina. The change detection maps were created using pixel-based classification methods on monthly composite data generated from PlanetScope satellites (3m resolution) as input for UAS flight planning, data acquisition, and processing. In future work a GRASS GIS script using a moving window resampling process will create flight areas to resample the change detection output into 10 acres flight areas for the UAS flight planning software, and a plugin for WebODM will be developed using GRASS GIS to enable seamless updates to centralized repositories of DSM.