As one road network is often the backbone in a geo-spatial dataset, capturing and/or updating the road networks using remote sensing imagery play an important role in traffic management, urban planning, vehicle navigation, and emergency management. Along with the progress of remote sensing launching technologies and the successful applications of deep learning in the field of computer vision, it has become more and more efficient and economical to employ deep learning methods for road-features extractions from very high-resolution (VHR) remote sensing imagery. Meanwhile, as one of the most significant and popular volunteer geographic information data sources, the OpenStreetMap (OSM) including the complete road networks in the wide world has been accumulated in the past decades. In this article, a generic and automatic approach for extracting road networks from VHR remote sensing images has been proposed based on fully convolutional neural network, in which the road centerlines from OSM have been employed to construct the labels for the model training and validation. In the conducted experiments on various VHR image datasets with two different spatial resolutions of 0.3 and 1 m, the proposed model has demonstrated quite satisfactory results – the overall completeness and correctness of the roads extraction from VHR remote sensing images exceed 94.0% and 98.0%, respectively.

The estimation of PM_x (incl. PM₁₀ and PM_2.5) concentrations using satellite observations is of great significance for detecting environmental issues in many urban areas of north China. Recently, aerosol optical depth (AOD) data have been being used to estimate the PM_x concentrations by implementing linear and/or nonlinear regression analysis methods. However, a lot of relevant research based on AOD published so far have demonstrated some limitations in estimating the spatial distribution of PM_x concentrations with respect to estimation accuracy and spatial resolution. In this research, the Google Earth Engine (GEE) platform is employed to obtain the band reflectance (BR) data of a large number of Landsat 8 Operational Land Imager (OLI) remote sensing images. Combined with the meteorological, time parameter and the latitude and longitude zone (LLZ) method proposed in this article, a new BR (band reflectance)-PM_x (incl. PM₁₀ and PM_2.5) model based on a multilayer perceptron neural network is constructed for the estimation of PM_x concentrations directly from Landsat 8 OLI remote sensing images. This research used Beijing, China as the test area and the conducted experiments demonstrated that the BR-PM_x model achieved satisfactory performances for the PM_x-concentration estimations. The coefficient of determination (R²) of the BR-PM_2.5 and BR-PM₁₀ models reached 0.795 and 0.773, respectively, and the root mean square error (RMSE) reached 20.09 μg/m³ and 31.27 μg/m³. Meanwhile, the estimation results have been compared with the results calculated by Kriging interpolation at the same time point, and the spatial distribution is consistent. Therefore, it can be concluded that the proposed BR-PM_x model provides a new promising method for acquiring accurate PM_x concentrations for various cities of China.

With the rapid improvement of geospatial data acquisition and processing techniques, a variety of geospatial databases from public or private organizations have become available. Quite often, one dataset may be superior to other datasets in one, but not all aspects. In Germany, for instance, there were three major road network vector data, viz. Tele Atlas (which is now “TOMTOM”), NAVTEQ (which is now “here”), and ATKIS. However, none of them was qualified for the purpose of multi-modal navigation (e.g., driving + walking): Tele Atlas and NAVTEQ consist of comprehensive routing-relevant information, but many pedestrian ways are missing; ATKIS covers more pedestrian areas but the road objects are not fully attributed. To satisfy the requirements of multi-modal navigation, an automatic approach has been proposed to conflate different road networks together, which involves five routines: (a) road-network matching between datasets; (b) identification of the pedestrian ways; (c) geometric transformation to eliminate geometric inconsistency; (d) topologic remodeling of the conflated road network; and (e) error checking and correction. The proposed approach demonstrates high performance in a number of large test areas and therefore has been successfully utilized for the real-world data production in the whole region of Germany. As a result, the conflated road network allows the multi-modal navigation of “driving + walking”.

Land surface temperature (LST) is an important parameter related to the environmental assessment concerning the surface energy, water balance, greenhouse effect, etc. at local and global scales. With the rapid development of the remote sensing technology, various methodologies have been developed to retrieve LST from space-based remote sensing images. Due to the ill-posed problem, the LST retrieval is still a challenge. In this research, a so-called multiple band reflectance (MBR)-LST model has been proposed based on the back-propagation neural (BPN) network, which can be employed to retrieve the LSTs from Landsat 8 Operational Land Imager (OLI)/TIRS images as well as produce continuous spatial LST distributions with a spatial resolution of 30 m. Experiments conducted in two randomly selected areas in mainland China proved that the proposed MBR-LST model has yielded a much better performance than the traditional radiative transfer equation (RTE) method with respect to both the accuracy and stability for the LST retrievals. Moreover, another significant advantage of the proposed MBR-LST is the generic nature – once trained by the sample data in the whole region of mainland China, the proposed MBR-LST model can be utilized for the accurate LST-retrievals in any area of mainland China.

With the increasing availability of diverse geospatial databases, data integration becomes an indispensable process to assure the quality and add values to each single data source as well as promote the interoperability among different data sources. The paper presents an operational automatic matching approach for road networks based on the Delimited Stroke Oriented (DSO) algorithm. It consists of four processes: (1) establishment of an index to record the relationship between conjoint objects; (2) construction of the delimited strokes; (3) matching between the corresponding delimited strokes; and (4) matching growing from seeds. With the help of index, the conjoint edges to a delimited stroke can be easily brought together. The corresponding network is then treated as an integral unit in the matching process. As compared with point- or line-based matching, such as Buffer Growing (BG) and Iterative Closest Point (ICP), the network matching allows the consideration of more topological information in a larger context environment. Consequently, the DSO algorithm is able to yield a considerably improved matching performance in terms of computing speed, matching rate, matching certainty and robustness. The approach has been successfully tested on large road networks from a number of federal states in Germany.

As one of the key operators of automated map generalization, algorithms for the line simplification have been widely researched in the past decades. Although many of the currently available algorithms have revealed satisfactory simplification performances with certain data types and selected test areas, it still remains a challenging task to solve the problems of (a) how to properly divide a cartographic line when it is too long to be dealt with directly; and (b) how to make adaptable parameterizations for various geo-data in different areas. In order to solve these two problems, a new line-simplification approach based on the Oblique-Dividing-Curve (ODC) method has been proposed in this paper. In this proposed model, one cartographic line is divided into a series of monotonic curves by the ODC method. Then, the curves are categorized into different groups according to their shapes, sizes and other geometric characteristics. The curves in different groups will trigger different strategies as well as the associated criteria for line simplification. Whenever a curve is simplified, the whole simplified cartographic line will be re-divided and the simplification process restarts again, i.e., the proposed simplification approach is iteratively operated until the final simplification result is achieved. Experiment evidence demonstrates that the proposed approach is able to handle the holistic bend-trend of the whole cartographic line during the simplification process and thereby provides considerably improved simplification performance with respect to maintaining the essential shape/salient characteristics and keeping the topological consistency. Moreover, the produced simplification results are not sensitive to the parameterizations of the proposed approach.