Applications of the Web of Things reach from smart shoes posting your running performance online, over the localization of goods in the production chain, to computing the insurance cost of cars based on the actually driven kilometers. Thereby, Web of Things applications follow the REST paradigm, i.e. access to things and their properties is offered via REST APIs. This allows an easy meshing of web-enabled things into existing Web applications.

We present the SenseBox, a small computing device equipped with (1) different sensors to perceive its environment and (2) with a Web server and an according REST API which makes it available as a first class citizen on the Web. In an example use case, the SenseBox is deployed next to a road and its in-built ultra sonic sensor is used to detect the number of bypassing cars and eventually determine the traffic density.

The portal to access those SenseBoxes and display them in their spatial context is based on the open geo-stack consisting of OpenLayers, GeoServer, and PostGIS. We demonstrate how the integration of such Web-enabled things with these software tools works.

Point cloud data, in the form of pseudo-randomly distributed multidimensional XYZ triplets, is rapidly becoming a fundamental data type along with raster and vector data in the GIS domain. Whether collected by expensive laser systems or consumer-grade products such as Kinect, point cloud data are finding uses throughout GIS for tasks such as 3D building modeling and terrain generation. Few open source libraries exist to work with point cloud data, and this talk will focus on libPC (http://libpc.org) and libLAS (http://liblas.org), two BSD-licensed libraries for manipulating and processing point cloud data. A short survey of the open source LiDAR landscape will be provided along with some demonstration of the capabilities of both libLAS and libPC.

This presentation is about the U.S. Geological Survey Geo Data Portal (GDP) project which formalizes common data acquisition and assimilation tasks to assist in model parameterization, model coupling, and data integration in a standards based data retrieval and analysis application. The GDP spatial data infrastructure has been designed to leverage and integrate open-standards and open source software implementations from the geographic, oceanographic, hydrologic, and atmospheric science communities. Interoperability is achieved by adopting existing standards and open-source software components where possible, by working closely with enterprise-scale data providers to implement standards compliant services, and by developing transformations and interoperable connections where necessary.  The GDP spatial data infrastructure allows easy discovery of and access to data from distributed open standards compliant web servers regardless of data scale or volume. GDP Web Processing Services, written relying on numerous open-source software libraries, can dynamically sub-set, reformat, or summarize accessed data. The set of geographic, atmospheric, and general opensource software community standards support simple, rapid development of lightweight user interfaces to commonly needed environmental data access and manipulation tools. Standalone, open source service components of the GDP infrastructure provide the metadata cataloging, data subset access, and spatial-statistics calculation needed to support interdisciplinary environmental modeling.  Ultimately, the services developed for and adopted by the GDP project simplify and streamline the time consuming and resource intensive tasks that are often barriers to inter-disciplinary scientific collaboration.