With the increase of different sensors, applications and customers, the demand from data providers and users is for a new geospatial data service model, which supports low cost, high dexterity, and which would provide a comprehensive service. Based on such requirements and demands, the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise, Twenty First Century Aerospace Technology Co., Ltd. (21AT). The company is the first commercial Earth observation satellite operator and service provider in China. This new geospatial data service model allows the user to directly access multi-source satellite data, manage the data order, and carry out automatic massive data production and delivery. The solution also implements safe and hierarchical user management, statistical data analysis, and automatic information reports. In addition, a mobile application is also available for users to easily access system functions. This new geospatial solution has already been successfully applied and installed in many customer sites in China, and is now available globally for international clients interested in fast geospatial solutions. It enables the success of customers’ operational services. Besides providing TripleSat Constellation images, the multi-source data access system also allows the users to access other satellite data sources, based on customized agreement. This paper describes and discusses this new geospatial data service model.

Automatically mapping building footprints has a wide range of applications in many fields. In recent years, the automatic building extraction methods based on deep learning show an absolute advantage over the traditional image segmentation methods due to its high speed and high precision. However, the building footprint extracted by deep learning is just an irregular building mask. There is still much work to be done to transform the building mask into a vector building footprint in the usual sense. One of the most important tasks is to determine the orientation of each side of the building. Most of the current methods are based on the building mask to determine the orientation of each side of the building. The biggest disadvantage of this method is that it completely relies on the building mask which is often unsatisfactory. In this case, the article proposes a method to determine the orientation of each side of the building based on the building mask and line segments, thereby effectively avoiding the danger of relying on the building mask. Experiments show that the proposed method can achieve high-speed and high-precision automatic extraction of building footprints from remote sensing images, saving costs.

The development of deep learning techniques such as convolutional neural networks (CNN) has injected new vitality into the study of semantic segmentation. Benefiting from the development of new technology, the automatic and intelligent work of remote sensing image segmentation has also entered a new stage. With the further improvement of China’s urbanization rate, urban construction is also developing rapidly. One of the most important tasks in land surveillance is to monitor buildings above ground using remote sensing images. Prefabricated building is the most common type of illegal building in developing area. At present, supervision mainly relies on the traditional human-centered method, which is time-consuming and low in efficiency. Whether the deep learning methods can benefit the prefabricated building segmentation has not yet been tested. By taking the realistic demand into account, this paper proposes a customized neural network structure, which combines multiple refined modules and improves the feature extraction, especially for the prefabricated building segmentation task. Through the comparison with the baseline algorithm, the specifically refined model for the prefabricated building has shown better results in our experimental area, which proves the feasibility and superiority in practical application.

Water is one of a fundamental natural resource and strategic compound. However, with socio-economic development, the water environment is being confronted with a series of problems such as aggravated pollution. Accurate monitoring of the water quality is the prerequisite and foundation for water environment supervision and governance. With the development of remote sensing and computer technologies, the retrieval of water quality parameters has attracted increasing attention from researchers and practitioners. This paper aims to present progress of remote sensing technique in water quality parameter retrieval methods and applications. The following aspects were investigated in this review: (a) water quality parameters retrieval data source; (b) water quality parameters retrieval models and evaluation metrics; (c) water quality parameters remote sensing retrieval applications; (d) some challenges and potential directions for water quality parameters retrieval. This review provides some support for re-searchers, as well as management departments, in theoretical research and application for remote sensing water quality parameters retrieval.