Call for Papers

Over the past few decades, point clouds from LiDAR (light detection and ranging) and passive imaging technologies have been major data sources for mapping applications in the photogrammetry and remote sensing communities. In recent years, processing large-scale geospatial data, especially point clouds, has also drawn considerable attention from the computer vision, computer graphics and robotics communities. Workshops in recent premier computer vision and graphics conferences, such as the workshop on point cloud processing in computer vision at CVPR (computer vision and pattern recognition) 2012 and IQmulus workshop on processing large geospatial data at SGP (symposium on geometry processing) 2014, were focused on point cloud processing. Several ISPRS conference events, such as the 2014 Photogrammetric Computer Vision Symposium, have also placed an emphasis on point cloud processing. The purpose of this theme issue is to increase interdisciplinary interaction and collaboration in point cloud processing among photogrammetry, computer vision, computer graphics (geometry processing and geometric modeling), and robotics.

With the proliferation of remote sensing sensor and platforms, vast amounts of data are collected daily. A significant computation challenge currently exists, that is how to convert these datasets into accurate, meaningful information. This challenge is further intensified by the wide variety of sensors (e.g. multi/hyperspectral, radar, lidar, SAR), spatial-temporal resolutions and earth materials.

The United Nations website states as follows:


Latin America is one of the richest continents in terms of natural resources. The preservation of those resources matters to the entire planet. Nevertheless, the region faces complex environmental issues that continuously call on the use of remote sensing methodology. In order to give greater visibility to the research being carried out in the region and to foster future developments, the ISPRS announces a theme issue dedicated to relevant scientific contributions to mitigate Latin American environmental problems, in remote sensing related topics.

The ever-increasing amount of geo-referenced data that is captured with a multitude of different sensors at varying scale, temporal, and spatial resolutions contains rich information about our environment. Automatic extraction of meaningful information and knowledge at large scale from heterogeneous, georeferenced data is a major research topic in remote sensing & photogrammetry and in computer vision & machine learning. With a special issue on "Geospatial Computer Vision" we aim at fostering collaboration between the Photogrammetry & Remote Sensing and the Computer Vision & Machine Learning communities.

Photogrammetry, remote sensing and spatial information sciences have witnessed great changes over the last few years. Reasons for this development are, on the one hand new societal and political challenges such as a stronger quest for sustainable development, a globally increasing human population, a more apparent sense of safety and security, and an increased globalisation, visible for example in the mobility of people, goods, capital and education. On the other hand, innovation in our field has been strongly influenced by progress in information and communication technology, which can be summarized under the terms of ubiquitous computing, geosensor networks, geospatial data infrastructures, digital earth, big data, cloud computing, web 2.0, the internet of things, and crowd sourcing.

Spatial data are now collected and processed in larger amounts and used by larger populations than ever before. The availability of such large amounts of data induces an acute need for developing new approaches to assist with their real time or near real time dissemination, visualisation, and analysis by end users. Several issues need to be considered in order to provide a meaningful representation and assist in data visualisation and mining, modeling and analysis; such as data structures allowing representation at different scales or in different contexts of thematic information. Streaming algorithms need to be pipelined with algorithms for efficiently rendering and communicating multi-dimensional data including but not limited to both the spatial (3D/nD) and spatio-temporal (4D/n+1D) data sets. Furthermore, to be able to assess what is meaningful and useful, we need to pay attention to different domain ontologies as well as human factors in developing these ontologies, algorithms and resulting visualizations. The objective of this special issue is to present the latest developments and research on the representation, modeling and analysis of spatial data covering multiple aspects: streaming and parallel algorithms; data structures; context-aware representations; techniques for visualising these data and communicating these data to end users for ultimately making informed decisions about our environment. Therefore, we invite papers with original technical contributions proposing novel and effective solutions to handle the spatial “data deluge” and empirical user studies that evaluate spatial data modeling, analysis, visualization and associated technologies.

Web and cloud based mapping and geoprocessing services are an essential infrastructure to integrate information to users and applications that require them. Although they are increasingly becoming commonplace, the challenge is to apply them intelligently in a useful and effective manner. Developments in web and cloud based service architectures, context-aware environments, multidimensional interactive geovisualisation, virtual globe geospatial analysis, and collaborative user-driven environments, are leading the way towards intelligent environments that link the appropriate information with the intended purposes.