Semantic segmentation is a fundamental task in remote sensing image understanding. Recently, Deep Convolutional Neural Networks (DCNNs) have considerably improved the performance of the semantic segmentation of natural scenes. However, it is still challenging for Very High Resolution (VHR) remote sensing images. Due to the large and complex scenes as well as the influence of illumination and imaging angle, it is particularly difficult for the existing methods to accurately obtain the category of pixels at object boundaries---the so-called boundary blur. We propose a framework called Boundary-Aware Semi-Supervised Semantic Segmentation Network ( $\mathrm{BAS}^4$ Net), which obtains more accurate segmentation results without additional annotation workload, especially at the object boundaries. The Channel-weighted Multi-scale Feature (CMF) module balances semantic and spatial information and the Boundary Attention Module (BAM) weights the features with rich semantic boundary information to alleviate the boundary blur. Additionally, to decrease the amount of difficult and tedious manual labeling of remote sensing images, a discriminator network infers pseudo-labels from unlabeled images to assist semi-supervised learning and further improves the performance of the segmentation network. To validate the effectiveness of the proposed framework, extensive experiments have been performed on both the ISPRS Vaihingen dataset and the novel remote sensing dataset AIR-SEG with more categories and complex boundaries. The results demonstrate a significant improvement of accuracy especially on boundaries and for small objects.

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future.

This paper presents a means for automatic generation of a level of detail (LOD) representation of three‐dimensional (3D) building data, with the formally well‐defined scale‐spaces as the underlying theory. More specifically, we propose an approach that employs vector‐based mathematical morphology and discrete curvature‐space to generalize two‐dimensional (2D) building outlines as well as 3D building surfaces. Scale‐space events that are related to the semantics of objects are the major triggers of the generalization. The implemented approach preserves right angles. Additionally, more heuristic means to square buildings are presented. The test results have validated the approach.