Time is regarded as an essential feature of land information enabling to track historical landmarks of land uses, ownerships, and taxations based on cadastral maps. Object-oriented temporal modeling helps to simulate and imitate time-varying cadastral data in a chronological and persistent manner. The aim of study is to analyze the role of temporal cadastre tracing footprints of foregoing events in response to various needs and demands associated with historical information of cadastral transactions. In this paper, temporal cadastral object model (TCOM) is proposed to delineate object version history. As an evidence of a new approach and conceptual idea for the importance of temporal cadastre, a part of spatio-temporal processes is illustrated to explain major changes of cadastral map. The feasibility and application of the approach is confirmed by proof-of-concept of temporal cadastre in land information management.

Accesses to lands, houses, the Internet and other utilities are regarded as basic human needs to escape poverty and are required to recognize the relationships between the digital divide and socio-economical inequality. The digital divide might not be a single technical disparity, but stems from a complicated mixture of economic and socio-technical symptoms of inequality. There is growing awareness of scrutinizing causal mechanisms between the digital divide and poverty since combating poverty could be a primary step to mitigate the digital divide. In this paper, the Hexad model is proposed to explicate poverty interpretation by using 6 parameters as a major tool for partly assisting in poverty monitoring system in connection with land information. A solution model of the Internet is suggested to break the digital divide. It expounds a conceptual framework and new idea for poverty management to notify spatio-temporal locations of poor actors and geography of the digital divide when efforts of poverty eradication hinge on understandings of geographic location of digital disadvantaged groups through parcel-based land information.

The goal of image merging techniques are to enhance the resolution of low-resolution images using the detail information of the high-resolution images. Among the several image merging methods, wavelet-based image merging techniques have the advantages of efficient decorrelation of image bands and time-scale analysis. However, they have no regard for spatial information between the bands. In other words, multiresolution data merging methods merge the same information-the detail information of panchromatic image-with other band images, without considering specific characteristics. Therefore, a merged image contains much unnecessary information. In this paper, we discussed this 'mixing' effect and, proposed a method to classify the detail information of the panchromatic image according to the spatial and spectral characteristics, and to minimize distortion of the merged image.

The most widespread techniques for DEM generation are stereoscopy for optical sensor images and SAR interferometry(InSAR) for SAR images. These techniques suffer from certain sensor and processing limitations, which can be overcome by the synergetic use of both sensors and DEMs respectively. This study is associated with improvements of accuracy with consistency of image's characteristics between two different DEMs coming from stereoscopy for the optical images and interferometry for SAR images. The MWD(Multiresolution Wavelet Decomposition) and HPF(High-Pass Filtering), which take advantage of the complementary properties of SAR and stereo optical DEMs, will be applied for the fusion process. DEM fusion is tested with two sets of SPOT and ERS-l/-2 satellite imagery and for the analysis of results, DEM generated from digital topographic map(1 to 5000) is used. As a result of an integration of DEMs, it can more clearly portray topographic slopes and tilts when applying the strengths of DEM of SAR image to DEM of an optical satellite image and in the case of HPF, the resulting DEM.

The geometriccorrection of a satellite imagery is performed by making a systematic correction with satellite ephemerides and attitude angles followed by employing the Ground Control Points (GCSs) or Digital Elevation Models (DEMs). In a remote area or an inaccessible area, however, GCPs are unavailable to be surveyed and thus they can be obtained only by reading maps, which are not accurate in reality. In this study, we performed the systematic correction process to the inaccessible area and the precise geometric correction process to the adjacent accessible area by using GCPs. Then we analyzed the correlation between the two geo-referenced Korea Multiurpose Satellite (KOMPSAT-1 EOC) images. A new geometrical correction for the inaccessible area imagery is achieved by applying the correlation to the inaccessibleimagery. By employing this new method, the accuracy of the inaccessible area imagery is significantly improved absolutely and relatively.

Depending upon geographical features and surrounding errors in the survey field, inaccurate positioning is inevitable in a kinematic DGPs survey. Therefore, a data inaccuracy detection algorithm and an interpolation algorithm are essential to meet the requirement of a digital map. In this study, GPS characteristics are taken into account to develop the data inaccuracy detection algorithm. Then, the data interpolation algothim is obtained, based on the feature type of the survey. A digital map for 20km of a rural highway is produced by the kinematic DGPS survey and the features of interests are lines associated with the road. Since the vertical variation of GPS data is relatively higher, the trimmed mean of vertical variation is used as criteria of the inaccuracy detection. Four cases of 0.5%, 1%, 2.5% and 5% trimmings have been experimented. Criteria of four cases are 69cm, 65cm, 61cm and 42cm, respectively. For the feature of a curved line, cublic spine interpolation is used to correct the inaccurate data. When the feature is more or less a straight line, the interpolation has been done by a linear polynomial. Difference between the actual distance and the interpolated distance are few centimeters in RMS.