A Scalable Geospatial Web Service for Near Real-Time, High-Resolution Land Cover Mapping

A land cover classification service is introduced toward addressing current challenges on the handling and online processing of big remote sensing data. The geospatial web service has been designed, developed, and evaluated toward the efficient and automated classification of satellite imagery and the production of high-resolution land cover maps. The core of our platform consists of the Rasdaman array database management system for raster data storage and the open geospatial consortium web coverage processing service for data querying. Currently, the system is fully covering Greece with Landsat 8 multispectral imagery, from the beginning of its operational orbit. Datasets are stored and preprocessed automatically. A two-stage automated classification procedure was developed which is based on a statistical learning model and a multiclass support vector machine classifier, integrating advanced remote sensing and computer vision tools like Orfeo Toolbox and OpenCV. The framework has been trained to classify pansharpened images at 15-m ground resolution toward the initial detection of 31 spectral classes. The final product of our system is delivering, after a postclassification and merging procedure, multitemporal land cover maps with 10 land cover classes. The performed intensive quantitative evaluation has indicated an overall classification accuracy above 80%. The system in its current alpha release, once receiving a request from the client, can process and deliver land cover maps, for a 500-$text{km}^2$ region, in about 20 s, allowing near real-time applications.