This research developed a versatile land-use information system (VLUIS) based on moderate- and high-spatial resolution imagery for supporting local planning in Indonesia. It was motivated by the fact that the existing land-use information contained by the Key Dataset for Local Development (KDLD) was not adequate to support environmental planning at local levels in Indonesia. This was due to its inconsistent mapping methods, contents/classification scheme, and inflexibility to be used as an input to local physical planning processes. Although the KDLD was developed by most local coordinating agencies for development planning (Bappedas), the land-use map was not used as a common reference by various local and provincial institutions in assessing the state of environment. Therefore, each institution had a tendency to develop its own land-cover/land-use information, resulting redundant works of land-cover/land-use mapping, which were incompatible to each others. With regard to that problem, the objectives of this study were: (a) to specify land-use related planning tasks at local level in Semarang-Salatiga area, Java, Indonesia; (b) to design a versatile landuse classification scheme for urban and rural environment at local level in Java in order to support various applications in the local planning context; and (c) to develop and verify the versatile land-use mapping methods based on moderate- and high-spatial satellite imagery. Semarang-Salatiga area was chosen due to its relatively complex land-use phenomena and data availability. In this study, two types of satellite image dataset were used, Landsat-7 ETM+ and Quickbird, representing moderate- and high-spatial resolution imagery respectively. To achieve the research objectives, a methodology comprising three stages of activity was developed. The first stage specified local physical planning tasks and their required land-cover/land-use information, based on literature study and interview with 36 stakeholders in the study area. In the second stage, versatile land-use information contents were specified in a classification scheme containing five land-use dimensions, i.e. spectral, spatial, temporal, ecological, and socio-economic. In the third stage, a set of image classification methods was developed for generating all land-use dimension maps with the specified classes. For each type of imagery, the study area was divided into northern and southern parts. The northern part represents more developed/urbanised area, while the southern part represents less developed or rural areas. Multi-spectral classification in terms of both standard and non-standard approaches were explored to derive the spectral-related land-cover classes, while visual interpretation and object-oriented image segmentation were compared to find most accurate method in generating the spatial dimension classes. The standard multi-spectral classification approach made use of original bands as input to the classification process, while the non-standard approach involved texturally filtered and texturally aggregated bands in addition to the original ones. The spectral-related land-cover and spatial dimension maps, supported by a terrain unit map, were integrated in a raster GIS environment to derive the temporal, ecological, and socio-economic maps in separate processing methods. After that, all derived maps were integrated into a single dataset of VLUIS, ready for query-based activation at will and translation to other classification systems. Based on the interview with the respondents, a list of variables related to land-cover/land-use information required by various local planning tasks was regrouped with respect to the developed five land-use dimensions. After that, a classification scheme containing five columns representing spectral-related land-cover, spatial, temporal, ecological, and socioeconomic dimensions were created. The specified classes under each dimension referred to the variables used in various local planning and to the existing, widely used, classification systems. The spectral-related land-cover mapping results showed that standard multi-spectral classification methods using the original spectral bands gave higher accuracy results (84.63% or Kappa=0.8276 for Landsat-7 ETM+ and 68.75% or Kappa=0.6813 for Quickbird) than non-standard classification methods involving textural filtering (80.55% or Kappa=0.7988 for Landsat-7 ETM+ and 66.45 or Kappa=0.6503 for Quickbird) and textural aggregation (66.68% or Kappa=0.6512 for Landsat-7 ETM+ and 63.91% or Kappa=0.6222 for Quickbird) approaches. This was due to the fact that the texture is closer to spatial rather than spectral concept, while the specified categories in the spectral-related land-cover dimension is purposively developed for spectral classification. For the same image coverage and number of classes, Landsat-7 ETM+ gave higher accuracies (84.63% or Kappa=0.8276 for 40 classes, and 87.05% or Kappa=0.8535 for 25 classes) than Quickbird’s (82.81% or Kappa=0.8118 for 40 classes, and 83.23% or Kappa=0.8184% for 25 classes). In terms of spatial dimension mapping, the object-oriented image segmentation could not generate an accurate spatial dimension map in comparison with the visual interpretation, since the categories were specified using location/site and regularity criteria in addition to shape and density, which were not possible to recognise using the available software. However, by integrating the spectral-related land-cover dimension and the visual interpretation-based spatial dimension maps in a raster GIS environment, the temporal, ecological and socio-economic dimension maps could be derived in relatively accurate levels, i.e. with overall accuracies higher than 80%. For all land-use dimensions, the results obtained using Landsat-7 ETM+ and Quickbird imagery consistently showed that rural areas were more accurately classified than urban areas. This study demonstrated that a VLUIS could be developed based on moderate- and highspatial resolution imagery. In this VLUIS, a multi-dimensional classification scheme was developed first, with separate column representing spectral-related land-cover, spatial, temporal, ecological, and socio-economic dimensions. After that, the classification scheme was used as reference in extracting information and mapping each dimension into separate map layers. The five layers were then stacked into a single dataset. An example of querybased translation from the VLUIS to the Indonesian National Land Agency (BPN)’s classification system was given to show its versatility. However, it was also realised that land-use is too complex to be mapped merely using remotely sensed imagery and be modelled simply based on the five dimensions. With its limitations, remote sensing should be put in the context of complementary and alternative approach, where field surveys often fail to generate comprehensive, efficient and rapidly provided information that is required in a planning process. This study also recommends future work for more effective impact of the results, i.e. (a) development of information extraction methods of versatile land-use information system (VLUIS)’s dimensions using state of the art image and spatial data analyses, (b) development of translation system from the VLUIS to widely used landcover/ land-use classification schemes, and (c) demonstration of versatility in supporting several applications related to local planning tasks.