Towards large scale land-cover recognition of satellite images

The entire Earth surface has been documented with satellite imagery. The amount of data continues to grow as higher resolutions and temporal information become available. With this increasing amount of surface and temporal data, recognition, segmentation, and event detection in satellite images with a highly scalable system becomes more and more desirable. In this paper, a semantic taxonomy is constructed for the land-cover classification of satellite images. Both the training and running of the classifiers are implemented in a distributed Hadoop computing platform. Publicly available high resolution datasets were collected and divided into tiles of fixed dimensions as training data. The training data was manually indexed into the semantic taxonomy categories, such as ”Vegetation”, ”Building”, and ”Pavement”. A scalable modeling system implemented in the Hadoop MapReduce framework is used for training the classifiers and performing subsequent image classification. A separate larger test dataset of the San Diego region, acquired from Microsoft BING Maps, was used to demonstrate the efficacy of our system at large scale. The presented methodology of land-cover recognition provides a scalable solution for automatic satellite imagery analysis, especially when GIS data is not readily available, or surface change may occur due to catastrophic events such as flooding, hurricane, and snow storm, etc.