Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 147-154, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-147-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 147-154, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-147-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

MODEL FOR LAND COVER ESTIMATION USING UNSUPERVISED MACHINE LEARNING ON GOOGLE MAPS COLOR IMAGES

R. S. Bhowmick1, A. Kumar1, G. D. Singh1, and S. Kumar2 R. S. Bhowmick et al.
  • 1University of Petroleum and Energy Studies, Dehradun, India
  • 2Indian Institute of Remote Sensing, Dehradun, India

Keywords: Unsupervised learning algorithms, CORINE nomenclature, Google Static Maps API, k-means clustering

Abstract. Remote sensing data and satellite images are broadly used for land cover information. There are so many challenges to classify pixels on the basis of features and characteristics. Generally it is pixel classification that required the count of pixels for certain area of interest. In the proposed model, we are applying unsupervised machine learning to classify the content of the input images on the basis of pixels intensity. The study aims to compare classification accuracy of different landscape characteristics like water, forest, urban, agricultural areas, transport network and other classes adapted from CORINE (Coordination of information on the environment) nomenclature. To fulfil the aim of the model, accessing data from Google map using Google static API service which creates a map based on URL parameters sent through a standard HTTP (Hyper Text Transfer Protocol) request and returns the map as an image which can be display on any graphical user interface platform. The Google Static Maps API returns an image either in GIF, PNG or JPEG format in response to an HTTP request. To identify different land cover/use classes using k-means clustering. The model is dynamic in nature that describes the clustering as well formulate the area of the concerned class or clustered fields.