Abstract. Google Earth provide the most accurate and available global high resolution imagery, covering nearly the entire land surface of the earth. However, the precision of Google Earth’s data has not been fully validated.The traditional ground measurement method is difficult to verify the horizontal precision of remote sensing over a large area. This paper focuses on typical regions of Asia, aiming to verify the precision of GE’s data based on purchased WorldView (WV) data by utilization of statistical analysis method.The results show that the highest precision has been estimated as 4.96–6.83 meters over the part of Japan, India and Kazakhstan, respectively. The lowest precision 16.53 and 16.59 meters primarily appear mountainous terrain, including the part of Israel and Syria.The result also presents the horizontal precision estimated in Japan, India and Kazakhstan, which is slightly higher than the precision estimated in Israel and Syria. The regions with larger deviation of relative errors have apparent influence on horizontal accuracy assessment of GE’s imagery. Accuracy assessment may be affected by terrain features and the insignificant feature points over the study area. The results suggest that the most of horizontal accuracy of GE’s high resolution imagery over the most of study regions fulfills precision requirement of 1:50000 maps.

Abstract. Based on the requirements of quality supervision inspection for national surveying and mapping, this paper has carried out research on sampling and auxiliary sampling technology and sampling system for quality inspection targets for different objects and different types of surveying results. The study designed the quantitative random sampling scheme of the random inspection objects considering the principle of supervision and spot check of surveying and mapping quality. It also designed the stratification scheme of surveying and mapping results under different quality inspection targets and object conditions, and the stratified adjustment scheme with prior quality information. Based on the above scheme, the national surveying and mapping supervision inspection sampling system based on the project rolling pool and the stratified random auxiliary sampling prototype system for the quality inspection target of surveying and mapping results are implemented. In recent years, they have been widely used in the national surveying and mapping geographic information supervision inspection and surveying and mapping results quality inspection, which guaranteed the scientific and reasonable determination of surveying and mapping quality supervision inspection objects, and solved problems in the quality inspection of surveying results, such as unreasonable stratification in manual sampling, and inevitable error in random sampling. This is of great significance for further improving sampling efficiency, reducing sampling error, reducing sampling risk, and promoting informatization of quality inspection.

Commission VI, WG VI/4KEY WORDS: 3D-Edge, Straight Line Segment, Filtering, Airborne Laser Scanning Point Cloud, Random Sample Consensus, Ground Breaklines ABSTRACT:Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point's neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.

This article proposes a construction method for a comprehensive geographic conditions evaluation index based on geographic conditions survey data combined with thematic data on society, economy, ecology and population. This article constructs a threelevel evaluation framework composed of index level, object level and factor level from the perspectives of ecological coordination, urban development, regional economic potential and basic public services, studies a method of acquiring all-level factor data on geographic conditions and discusses the comprehensive evaluation factor system of geographic conditions. The components of the all-level index are selected through principal component analysis, and iterative analysis is performed by innovatively setting conditions to ensure the independence of the factors and establish an evaluation factor set for geographic conditions. The weighting for the all-level index is obtained through the analytic hierarchy process resulting in the index of geographic conditions. From the perspective of geographic conditions, this article makes a dynamic and quantitative evaluation of national conditions and strengths to provide a reference basis for regional sustainable development and governmental management decisions. By using the method, this article first obtains the index of geographic conditions of Q city with comprehensive evaluation and analysis to verify the objectivity and scientific nature of the method and expand and deepen the application of survey data on geographic conditions.

Abstract. Under the background of the increasingly unified management of natural resources, remote sensing big-data will become the main data source to support a number of major projects. How to sample the natural resources results efficiently and reliably in the process of quality evaluation is always a research hotspot when it comes to the natural resources results involving remote sensing big-data. A sequential quality evaluation model based on root mean square error (RMSprop) optimization algorithm is constructed by theoretical analysis with an numerical experiments to validate the effectiveness of this method.