Abstract. Quality inspection software is an important means of quality inspection and an important factor affecting quality inspection work. Facing the current situation and problems of quality inspection software for surveying and mapping geographic information achievements, and combining existing means and new technology, this paper puts forward a software system for quality inspection of surveying and mapping geographic information achievements under "Multi-Dimensional Synergy", which is composed of achievement dimension, project dimension, big data dimension, intelligence dimension and history dimension, expounds the work contents of surveying and mapping geographic information results quality inspection in various dimensions, introduces the relationship of business process and data flow between the multi dimensions. It provides the idea for the future research content and development direction of Surveying and mapping geographic information results quality inspection software.

Abstract. Convolutional Neural Networks have been widely introduced to building rooftop segmentation using satellite and aerial imagery. Preparing efficient training data is still among the critical issues on this topic. Therefore, adopting available annotated cross-domain multisource dataset is needed. This paper evaluates the performance of fusing the state-of-art deep learning neural network architectures for cross-domain building rooftop segmentation. We have selected three semantic image segmentation neural networks, including Swin transformer, OCRNet and HRNet. The predictions from these three neural networks are combined with majority voting, max value and union fusion techniques, a refined building rooftop segmentation mask is therefore delivered. The experiments on two benchmark datasets show that the proposed fusion techniques outperform single models and other state-of-art cross-domain segmentation approaches.

Abstract. Since the release of Google Earth image data, it has been the most widely used remote sensing data worldwide, and its accuracy evaluation has also been the focus of historical research. However, the researchers found that Google Earth's image accuracy assessment results have obvious regional characteristics. This article uses the Australian continent as the research area and WorldView-2 remote sensing images as reference data to study the accuracy evaluation results of Google Earth data. The research shows that the overall accuracy of the assessment area in Australia is better. The areas with the best overall accuracy appear in the western coastal areas, with an accuracy range of 0.7–1.4; the accuracy assessment results in the central desert area are also better, with the accuracy range 1.4–2.2, and the areas with the worst accuracy appear in the western mountains and hills of 14.5 and 17.1.

Abstract. Combined with the acceptance of the surface coverage data results of the national geographic situation monitoring project, as well as the consistency requirements between the surface coverage data results and the background data, the "three tone" data results, the digital orthophoto data and the field investigation and verification data, this paper puts forward the quality control method of the surface coverage classification data results by analyzing the quality problems found in the inspection. This method provides a technical reference for the quality control of the national geographic situation monitoring project and a method basis for quality inspection.