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Due to the non-standardization and the personalized need of thematic mapping, the processing efficiency of basic geographic information data has not been effectively improved. The thematic mapping technology based on Photoshop has been adopted in railway industry widely. This paper applied the JavaScript technology to the secondary development of mapping, called the API of Windows and Photoshop, and used common mapping modules, mapping functions, main parameters, and hotkeys to encapsulate packages. It realized batch processing of remote sensing images, automatically processing of features on thematic map, and modification of map features in human-machine interaction manner. The research includes three parts: (1) The introduction of the configure environment, compile platform, and result of secondary development based on Javascript. (2) The application of four application scenarios for JavaScript technology: 1 Image batch processing to achieve automatic spectral enhancement of remote sensing image. 3 Automatic mapping for thematic map elements. 2 Batch and automatic modification of features on maps. 4 processing of elements on the map in human-machine interaction manner. (3) The example of the thematic mapping of a railway project in Europe using remote sensing image for the verification of the feasibility. The results show that the technology can significantly improve the mapping efficiency, make it easier to standardize drawing map symbol, and solve the problems on high redundancy of data and low efficiency of execution during batch and automatic processing scripts. [ABSTRACT FROM AUTHOR]

Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence ( J S D), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms. [ABSTRACT FROM AUTHOR]

Europe is witnessing a new wave of collective self-organized forms of housing provision. We refer to the wide variety of these forms under the umbrella term "Collaborative Housing". Alongside growing grassroots activity, the number and breadth of academic publications is rapidly growing. Nevertheless, the field remains thematically fragmented, with literature spread across disciplines. This article aims to establish a basis for greater conceptual clarity by exploring the domain of collaborative housing research over the period of 1990–2017. We carry out a thematic mapping and assessment covering 195 relevant peer-reviewed journal articles in English, German and French. These are inductively synthesized and categorized into five main thematic areas: socio-demographic, collaboration, motivation, effects and context. We conclude that the systematic and inductive approach of our review unravels new perspectives and makes the case for the conceptualization of collaborative housing as an independent research field, providing a basis for further theory development. [ABSTRACT FROM AUTHOR]

The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article's contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone. [ABSTRACT FROM AUTHOR]

• A tree map was created using unsupervised classification for six Sentinel-2 tiles. • The combination with highest agreement with the NFI was the bands 2, 3, 6 and 12. • Good qualitative agreement between the present map and the NFI. • The map shows between 8% and 79% greater tree cover compared to previous estimates. • The overall accuracy of the present map was assessed to be up to 90%. The spatial and temporal distribution of trees has a large impact on human health and the environment through contributions to important climate mechanisms as well as commercial, recreational and social activities in society. A range of tree mapping methodologies has been presented in the literature, but tree cover estimates still differ widely between the individual datasets, and comparisons of the thematic accuracy of the resulting tree maps are rather scarce. The Copernicus Sentinel-2 satellites, which were launched in 2015 and 2017, have a combination of high spatial and temporal resolution. Given that this is a new satellite, a substantial amount of research on development of tree mapping algorithms as well as accuracy assessment of said algorithms have to be done in the years to come. To contribute to this process, a tree map produced through unsupervised classification was created for six Sentinel-2 tiles. The agreement between the tree map and the corresponding national forest inventory, as a function of the band combination chosen, was analysed and the thematic accuracy was assessed for two out of the six tiles. The results show that the highest agreement between the present tree map and the national forest inventory was found for bands 2, 3, 6 and 12. The present tree map has a relative difference in tree cover between 8% and 79% compared to previous estimates, but results are characterised by large scatter. Lastly, it is shown that the overall thematic accuracy of the present map is up to 90%, with the user's accuracy ranging from 34.85% to 92.10%, and the producer's accuracy ranging from 23.80% to 97.60% for the various thematic classes. This demonstrates that tree maps with high thematic accuracy can be produced from Sentinel-2. In the future the thematic accuracy can be increased even more through the use of temporal averaging in the mapping procedure, which will enable an accurate estimate of the European tree cover. [ABSTRACT FROM AUTHOR]

In this paper we explore the potential of repeat-pass interferometric SAR (InSAR) for land cover classification purposes. We introduce a novel approach for the generation of large-scale thematic maps, based on the use of multi-temporal data, acquired over short observation intervals (short-time-series). A larger interferometric coherence loss is expected with the increasing time difference between two interferometric acquisitions. This phenomenon is normally indicated as temporal decorrelation whose amount differs depending on the type of imaged target on ground. The basic idea is therefore to accurately model the evolution in time of the temporal decorrelation and use the estimated parameters, together with backscatter, as input features for the Random Forest machine learning classification algorithm. The work has been carried out on the case study of land cover mapping over central Europe, considering Sentinel-1 C-band interferometric stacks, acquired over a time span of about one month. Three different land cover classes have been considered: artificial surfaces as e.g. urban areas, forests , and non-forested areas as the ensemble of low vegetation, bare soil, and agricultural areas. The results show a level of agreement above 91%, when compared to the CORINE land cover map product of 2012, which has been used as external reference for both training and testing of the classification algorithm. • We present a novel approach for land classification from multi-temporal InSAR data. • Temporal decorrelation and backscatter are used for a machine learning classifier. • The framework is tested with Sentinel-1 data over a region in Europe. • The results demonstrate the potential of InSAR stacks for land cover mapping. • The approach is suitable for the generation of large-scale land cover maps. [ABSTRACT FROM AUTHOR]

Presents a map indicating various smuggling of weapons-grade uranium and plutonium in Europe.