Abstract

Abstract. Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score.

Highlights

  • Land use describes the socio-economic function of a piece of land

  • Many object catalogues, e.g. the catalogue used in the German Authoritative Real Estate Cadastre Information System (ALKIS; AdV, 2008), provide land use information in multiple semantic levels with a hierarchical structure

  • In Hameln, LuNet-lite-joint optimization (JO)-T outperforms LuNet-lite-JO-MS by up to 2.4% in terms of OA and +2.8% in terms of mean F1 score; the corresponding numbers are +3.2% (OA) and +2.5% in Schleswig

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Summary

Introduction

Land use describes the socio-economic function of a piece of land. This information is frequently maintained by governmental mapping agencies. In order to verify this information automatically as a first step of a database update, current remote sensing data can be employed to predict a land use label. Work on image-based classification is dominated by convolutional neural networks (CNN) (Krizhevsky et al, 2012). CNN require images of a fixed size as input. Object catalogues of geospatial databases typically contain a very large number of land use classes ( called categories, these terms are used interchangeably in this paper), many of which cannot be expected to be distinguishable in remote sensing imagery. From the point of view of the application, it is useful to obtain predictions at multiple semantic levels simultaneously

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