TO LOCALISE OR TO BE LOCALISED WITH WIFI IN THE HUBEI MUSEUM?

ISPRS The first method detects the beacon frames send by smartphones, laptops and other WiFi enabled devices in range using Libelium Meshlium Xtreme monitors. Their MAC addresses and the signal strength is measured by the Meshlium Xtreme and stored on an external database. We call this method WiFi monitoring. The second method a Wifi enabled device, like a smartphone, measures the signal strength of multiple Wifi Access Points in range to localise itself based on a previously created radio map. This method is known as WiFi fingerprinting. Both methods have some advantages and disadvantages. Advantages of the common way of WiFi fingerprinting are that the implementation costs are relatively low, because it is usually possible to use (a part of) the existing WiFi AP infrastructure. WiFi fingerprinting can reach a relatively high accuracy in the order of magnitude of meters. Finally, the location granularity can be adjusted to what is necessary for the purpose of the indoor localisation. This makes it employable for a wide range of purposes. The question remains how suitable these methods are for a 3D indoor navigation system for the Hubei provincial museum. One important aspect is the localisation-granularity necessary for the application. In a museum it is not necessary to know the exact X,Y position of a user (such high accuracy is unnecessary), more important is to know in which room the user is located so the information on exhibitions can be presented and the starting point of the navigation can be determined. Both methods can track the user and tell the room he or she is located at. Although WiFi smartphone monitoring may have a low update frequency it is still suitable for a navigation system for a museum since visitors usually spend more than a couple of minutes within a room

DISTANCE-VALUE-ADDED PANORAMIC IMAGES AS THE BASE DATA MODEL FOR 3D-GIS

Panoramic images portray a surround view of the real world in one image. This kind of presentation however does, alike regular pictures, not give any real depth information. Depth related relationships are only to be detected (within one image) by psychological cues like relative size, linear perspective and shadow. The relative distance between two features can only be retrieved by 1) stereoscopic measurement within two pictures or 2) by the integration of terrestrial laser scanning systems. In the second approach the photo is enhanced with information about the distance between each pixel and the location of image recording. If we visualize this cloud of ‘distance pixels ’ from a point of view chosen at the recording place through a panoramic perspective it will give us the impression of the original panoramic picture, but now with the added value of depth-related queries. This kind of ‘distance-valueadded’ panoramic pictures can be used as a base data model for 3D-GIS visualizations. This paper concentrates on the database organization of RGB laser scan point clouds, the creation of virtual ‘distance-value-added ’ panoramic images from these clouds, and the visualization and spatial analysis based on this kind of perception rich images. 1

WEB-BASED SHARING OF A GEO-PROCESSING CHAIN: COMBINATION AND DISSEMINATION OF DATA AND SERVICES

The recent integration of the internet with geo-information systems (GIS) has evolved the concept of Internet Distributed Geo-Information Services. Numerous research and implementation studies of standards of metadata, geo-libraries, data storage and retrieval etc. are being done giving way to exchange and share of geo-information. However, very little effort has gone into making it possible to share process objects, or representations of processes of GIS use. Currently in most cases, the innovative algorithms for a certain geo-processing technique are developed for a dedicated purpose with a limited testing on the required prototype. One of the main reason of these problems is that we do not have a standard way to package geo-processes and making them available for testing [Regnauld, 2006]. If the geo-processing model itself is shared, and made understandable to the research communities, the risk of duplications and ambiguities in geo-processing can be taken care of. The model sharing would be highly useful to track the sequence of operations, exact criteria of conversion and transformation of spatial data, input/output specifications of processes, model constraints etc. Here we present a framework of a combination of geo-processing services for a dedicated application of land subsidence data visualization and interpretation. We combine different geo-data sources to correlate the local data (for example, cadaster, land use, water levels etc.) with PS-InSAR land subsidence data. An ESRI ModelBuilder tool is used to clearly describe the subsidence data classification with respect to the cadaster and height data. The geo-processing model to carry out this visualization and interpretation of PS-InSAR data is described step by step in form of a sequential model