Abstract
The classification of land use and land cover (LULC) is a well-studied task within the domain of remote sensing and geographic information science. It traditionally relies on remotely sensed imagery and therefore models land cover classes with respect to their electromagnetic reflectances, aggregated in pixels. This paper introduces a methodology which enables the inclusion of geographical object semantics (from vector data) into the LULC classification procedure. As such, information on the types of geographic objects (e.g., Shop, Church, Peak, etc.) can improve LULC classification accuracy. In this paper, we demonstrate how semantics can be fused with imagery to classify LULC. Three experiments were performed to explore and highlight the impact and potential of semantics for this task. In each experiment CORINE LULC data was used as a ground truth and predicted using imagery from Sentinel-2 and semantics from LinkedGeoData using deep learning. Our results reveal that LULC can be classified from semantics only and that fusing semantics with imagery—Semantic Boosting—improved the classification with significantly higher LULC accuracies. The results show that some LULC classes are better predicted using only semantics, others with just imagery, and importantly much of the improvement was due to the ability to separate similar land use classes. A number of key considerations are discussed.
Highlights
Land cover classes or types can be defined and determined in multiple ways
The focus of this research was to investigate the inclusion of geospatial semantics within a land use and land cover (LULC) classification of remotely sensed imagery
The results show that when geospatial semantics are fused with remotely sensed imagery, LULC classification accuracies are increased
Summary
Land cover classes or types can be defined and determined in multiple ways This can lead to ambiguous understandings of their characteristics and their spatial distribution. Land cover can be modelled and determined from different data sources, the most prominent of which is remotely sensed imagery. This is commonly used to determine the presence of different land covers with respect to their electromagnetic signatures. Sensed imagery allows LULC to be classified with high accuracy but only with respect to the aggregated electromagnetic reflectance as recorded in a pixel
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