ABSTRACT:Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

<p><strong>Abstract.</strong> The third national land survey is based on orthoimages with the results of the second national land survey to investigate the land classification, area and ownership of the national land in order to comprehensively understand the status of land use and land resources changes, and to improve the level of delicacy management for national nature resources. Therefore, an important task to ensure the completion of the third national land survey is to the develop a set of technical methods applicable to the quality inspection on orthoimage of the third national land survey.</p><p>In view of the inapplicability of the existing inspection standards and technical methods of traditional basic surveying and mapping results on the orthoimage quality inspection of the third national land survey, The paper analyzes the orthoimage production characteristics and quality need of the third national land survey, improves the inspection quality elements, sampling strategy, inspection methods, evaluation methods and quality problem handling based on the existing inspection standards and technical methods by pilot, and puts forward the corresponding inspection standards and technical methods to promote the quality inspection of orthoimage on the third national land survey.</p><p>Compared with the traditional method, the application of this method in 2865 inspection of county-level’s orthoimage of the third national land survey which covers the whole china, shows that, its inspection index is more reasonable, the inspection process is more scientific, the inspection efficiency is more efficient, and the direct cost of inspection is saved by 31%.</p>

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.

Abstract. Technologies such as satellite remote sensing, global positioning, laser scanning, airborne radar, tilt photography, and drones are rapidly developing. Spatial data is exploding at an unprecedented rate every day, in a short period of time. The elements of natural resources products vary, and sampling inspection is generally used to evaluate the quality level of a lot of products. The sampling inspection is based on the rigorous probability theory. Samples are randomly taken from a lot of products according to the sampling scheme for inspection, and the quality of the lot is represented by the quality status of a small number of samples. In the small data era, it can achieve quality inspection of natural resources products with the least labor cost and the smallest number of samples. However, how to select a good sample is a difficult problem. In theory, using any set of sample data, we can not get the exact total truth value, and the sampling error is inevitable.This paper gives an overview of the basic rules of sampling inspection, including the basics of mathematics, basic principles and selection of sampling schemes. It introduces in detail the parameters, characteristics and methods of a sampling inspection, and uses the surveying and sampling scheme adopted for Natural Resources Products inspection. For example, it analyzes the incompatibility of the risk of the producer, the user, the inspector and the sample size selection, and puts forward suggestions for improving the sampling scheme of natural resources products.

National resources investigation and basic surveying and mapping are two important tasks of the surveying and mapping department, and they are similar in production organization and technology realization. In the process of operation, both of them need to carry out internal collection, base map production, field verification and so on. It is operationally feasible to carry out cooperative production of national resources investigation and basic surveying and mapping. From the perspective of technical process and method, both of them are carried out by combination internal and field work. Firstly, based on remote sensing images and thematic geographic data, the internal work will perform image interpretation. Then, the field verification will be carried out to make judgments and adjustments. Finally, the results of the field verification will be transferred back to the internal work, and the data will be further edited and organized in the internal work to obtain the final data. This paper analyzes the technical methods and workflow of the cooperative update mechanism of national resources investigation and basic surveying and mapping. It will enable one-time acquisition of data needed for the national resources investigation and basic surveying and mapping. It is conducive to unifying the classification system, technical standards, survey methods, establishing a natural resource data service system, and improving the natural resource data sharing working mechanism. Realize the overall control of the land space, solve the contradictory problems such as multiple doors, uneven thickness, repeated crossover, etc., advance data integration and deep development and utilization by using big data technology and data analysis model.