Mapping of urban features from LIDAR data has gained enormous research interest. LIDAR (LIght Detection And Ranging) is a powerful remote sensing technology for the acquisition of terrain Surface. A LiDAR sensor delivers 3D point clouds with the intensities of the returned signals. A point cloud is a collection of discrete three-dimensional locations (points) that can have additional metadata associated with each record.

LiDAR has become a valuable data source for urban feature acquisition (buildings and roads). Buildings and roads data are important for urban planning.

We encourage submissions that substantially improve algorithms. Applications of these data and assessments of accuracy are also encouraged.

The assessment of landslides has been recognized from the scientific community as one of the most significant tools in the management of landslide phenomena, especially concerning the development of specific land regulations by local and national authorities that aim at minimizing the loss of lives and the damages to local properties.

A great number of different approaches have been utilized during the past three decades in the assessment of landslides that can be classified into qualitative and quantitative techniques. In general, qualitative methods are mainly based on the experience and knowledge provided by experts, while quantitative methods rely on statistical or probabilistic theories or even the use of deterministic models. Recently, Soft Computing techniques and methods are utilized as promising tools to evaluate the susceptibility and risk against landslides. These new applications are characterized by the ability of learning and discovering hidden and unknown patterns from large multi-thematic databases.

Along with the advantage of processing and analyzing data, a similar evolution has been achieved with the usage of Geographic Information Systems (GIS). GIS has proven to be a significant scientific tool that provides new possibilities for better data manipulation and more advanced modeling opportunities.

In this context, a special issue on "Landslide assessments through Soft Computing techniques within a GIS-based framework" is proposed in order to provide a forum for advancing the appropriate usage of Soft Computing techniques and GIS, for the assessments of landslide events. Specifically, the main objective of the special issue is to provide a review of the state-of-the-art in using integrated systems for effective landslide assessment and zonation incorporating expert knowledge, artificial intelligence and data mining technology in a GIS-based framework.

The intention of the present special issue is to provide a stand for researchers to share ideas and information regarding landslide phenomena and advanced analytic tools. We seek original and innovative papers, that address this theme of "Landslide assessments through Soft Computing techniques within a GIS-based framework" and present research of successful applications.

Public health professionals around the world are using GIS as a research and analytical tool in many innovative but isolated ways. GIS is still not integrated well as a business tool for most local public health agencies. There are many public health threats where GIS can help to monitor more effectively.

A special issue on "Recent Advances in the applications of GIS in Public Health" will provide a forum for advancing the effective use of spatial information and geographic information system technologies across the domains of public health, healthcare and community health preparedness. This forum will provide an outstanding environment for public health and GIS professionals to share ideas and information and provide an opportunity to contribute to the strengthening of health workers’ capacity for epidemiological analysis by providing efficient tools with GIS to facilitate such tasks.

We seek original and innovative papers which address this theme of "Recent Advances in the applications of GIS in Public Health " and present research which demonstrates application and evaluates the approach taken.

Spatial data commonly refers to data located in a -typically 2D or 3D- space. Spatial data structures and algorithms are used to efficiently store, retrieve, manipulate, and visualize such data. This topic is of interest for researchers of variety of fields including Geographic Information Systems (GIS), robotics, computer graphics, virtual reality, as well as other disciplines like finite element analysis, solid modeling, computer-aided design and statistics.

Especially, the recent improvements in collection of spatial data from the geographical space - in terms of type, volume, speed, accuracy, dimension, etc. - has provoked the researchers of geoinformatics to deploy more efficient data structures and algorithms to interact with such data. This special issue aims to concentrate on the main issue raised by the recent research in this area and presents the achievement.