Statistics for Spatial Data.

In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Liao%20-%20Data%20mining%20techniques%20and%20applications%20A%20decade%20review%20from%202000%20to%202011%20-%202012.pdf

Review: Data mining techniques and applications - A decade review from 2000 to 2011

CityGML is the international standard of the Open Geospatial Consortium (OGC) for the representation and exchange of 3D city models It defines the three-dimensional geometry, topology, semantics and appearance of the most relevant topographic objects in urban or regional contexts These definitions are provided in different, well-defined Levels-of-Detail (multiresolution model) The focus of CityGML is on the semantical aspects of 3D city models, its structures, taxonomies and aggregations, allowing users to employ virtual 3D city models for advanced analysis and visualization tasks in a variety of application domains such as urban planning, indoor/outdoor pedestrian navigation, environmental simulations, cultural heritage, or facility management This is in contrast to purely geometrical/graphical models such as KML, VRML, or X3D, which do not provide sufficient semantics CityGML is based on the Geography Markup Language (GML), which provides a standardized geometry model Due to this model and its well-defined semantics and structures, CityGML facilitates interoperable data exchange in the context of geo web services and spatial data infrastructures Since its standardization in 2008, CityGML has become used on a worldwide scale: tools from notable companies in the geospatial field provide CityGML interfaces Many applications and projects use this standard CityGML is also having a strong impact on science: numerous approaches use CityGML, particularly its semantics, for disaster management, emergency responses, or energy-related applications as well as for visualizations, or they contribute to CityGML, improving its consistency and validity, or use CityGML, particularly its different Levels-of-Detail, as a source or target for generalizations This paper gives an overview of CityGML, its underlying concepts, its Levels-of-Detail, how to extend it, its applications, its likely future development, and the role it plays in scientific research Furthermore, its relationship to other standards from the fields of computer graphics and computer-aided architectural design and to the prospective INSPIRE model are discussed, as well as the impact CityGML has and is having on the software industry, on applications of 3D city models, and on science generally

CityGML – Interoperable semantic 3D city models

Semiology of Graphics: Diagrams Networks Maps.

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey: first, autoencoder-based remote sensing image scene classification methods; second, convolutional neural network-based remote sensing image scene classification methods; and third, generative adversarial network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly used benchmark datasets. Finally, we discuss the promising opportunities for further research.

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23,463 images and 192,472 instances, covering 20 object classes. The proposed DIOR dataset (1) is large-scale on the object categories, on the object instance number, and on the total image number; (2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter- and intra-class size variability across objects; (3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and (4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-the-art approaches on our DIOR dataset to establish a baseline for future research.

Object detection in optical remote sensing images: A survey and a new benchmark

The development of wireless telecommunication and ubiquitous computing te- nologies has led to a growing mobile population and dramatically changed p- terns of working and everyday life A smooth and safe mobility is only possible when the mobile person is well-informed of the happenings in his ambient en- ronments Location-sensitive maps have proved a strong enhancement to what a mobile user can directly perceive from his ambient environments Since ancient times the map has been the favorite communication language of spatial infor- tion It is even more the case for mobile applications where brand-new maps can be wirelessly retrieved or generated in real-time The upsurge of map-based s- vices on mobile devices has raised a number of new questions challenging the conventional computer-assisted cartography Map-based mobile services provides a contemporary overview of research and development issues related to the design and the use of mobility-supporting maps The book has been written for professional cartographers who are striving for - tending their theoretical, methodological and practical knowledge to mobile m- making, for surveyors and geo-service providers involved in the development of intelligent location-based services, for software developers and cognitive scientists engaged in human-computer interaction, and for students and academics in cart- raphy and geoinformation sciences The book was initiated by the multidisciplinary workshop Design of m- based mobile services within the frame of the conference Human and Computer 2003 Interaction on the movement held in Stuttgart, Germany, September 2003

Map-based Mobile Services: Theories, Methods and Implementations

Kartographie - Visualisierung raum-zeitlicher Informationen

This chapter gives a general introduction into map-based mobile services which are considered as value-added location-based services. Starting from an overview of digital map types, their rapidly growing affordances and required learning efforts, the natures and design constraints of offline screen maps, web maps and mobile maps are comparatively studied. The aspects of immediate usability are highlighted as a central thread drawing together the essential research challenges involved in the design process of user-centred mobile maps.

Liqiu Meng

Papers

Object detection in optical remote sensing images: A survey and a new benchmark

Map-based Mobile Services: Theories, Methods and Implementations

Kartographie - Visualisierung raum-zeitlicher Informationen

Map-Based Mobile Services

An iterative road-matching approach for the integration of postal data