There has been growing demand for 3D modeling from earth observations, especially for purposes of urban and regional planning and management. The results of 3D observations has slowly become the primary source of data in terms of policy determination and infrastructure planning. In this research, we presented an automatic building segmentation method that directly uses LIDAR data. Previous works have utilized the CNN method to automatically segment buildings. However, the existing body of works have relied heavily on the conversion of LIDAR data into Digital Terrain Model (DTM), Digital Surface Model (DSM), or Digital Elevation Model (DEM) formats. Those formats required conversion of LIDAR data into raster images, which poses challenges to the evaluation of building volumes. In this paper, we collected LIDAR data with unmanned aerial vehicle and directly segmented buildings utilizing the said LIDAR data. We utilized a Dynamic Graph Convolutional Neural Network (DGCNN) algorithm to separate buildings and vegetation. We then utilized Euclidean Clustering to segment each building. We found that the combination of these methods are superior to prior works in the field, with accuracy up to 95.57% and an Intersection Over Union (IOU) score of 0.85.

Input-output linkages and multipliers are the two measures that are frequently used to find the drivers of an economy. Deriving from these two measures based on the traditional approach fails to consider the relative sectoral sizes. This paper introduces new linkage and multiplier measures that do not solely adjust for the relative sizes, but also extend the measures for policy-relevant indicators in Malaysia. Comparing the results between the traditional approach and the new approach, there is a clear indication that the former incorrectly identified the drivers of the Malaysian economy. The traditional approach not only introduced bias in linkages, but also overestimated the actual size of the multipliers. The new linkage and multiplier measures that were developed in this paper can be applied for other economies in finding key drivers for specific policy goals.

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Contribution of final demand components to gross domestic product (GDP) is often measured by a simple aggregated national accounting identity. Under this conventional approach, the contribution of exports is subtracted from imports to compute the contribution of net exports but it fails to split the imported intermediate and final use that is embodied in each domestic final demand. The so-called importadjusted approach is considered to be an ideal approach to measure the contribution of each final demand component to GDP. This approach splits imported intermediate and final use for each final demand component instead of accumulating all of them in the export component. This paper provides a heuristic approach for the application of import-adjusted approach to time-series data. We show that given a benchmark inputoutput table and provided with the annual trade statistics, the bias in measuring the contribution of domestic demand and foreign trade can be reduced. More importantly, we have provided a practical approach that does not only reduce man-hours required for database development but also obtain satisfactory findings. Results verify that the conventional approach tends to overestimate the contribution of domestic demands and underestimate the contribution of net trade to GDP.

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