PROTOTYPE OF NATIONAL DIGITAL ELEVATION MODEL IN INDONESIA

Although medium scale mapping has been done for the entire territory of Indonesia, there has never been a DEM unification to produce seamless national DEM. The main problem to generate national DEM is multi-data-sources. Each of them has their own specification, so unification of data becomes not easy to do. This research aims to generate global DEM database in Indonesia. Because its coverage covers only one country, it is called National DEM. This study will be focused on the northern part of Sumatra island, precisely in the boundaries between Aceh and North Sumatra province. The principle method in this study was to rebuild DEM data by considering the height difference between ground elevation from masspoints and surface elevation from DSM. By setting up a certain threshold value, the filtering process was then performed. The output was generated by gridding process. Validation in this research was done by two methods: visual inspection dan statistical analysis. From visual inspection, the National DEM data becomes smoother than the input data, the reality of the data is maintained, and still shows the landscape of the DSM input. From statistical analysis, compared with 142 GCPs, it is obtained that Root Mean Square Error is 2.237 m, and vertical accuracy based on Indonesian Mapping Accuracy Standard is 3.679 m. The result is good for medium scale base map and, based on the standards in Indonesia, the data can be used for 1 : 25,000 scale mapping

INVESTIGATIONS ON THE BUNDLE ADJUSTMENT RESULTS FROM SFM-BASED SOFTWARE FOR MAPPING PURPOSES

Since its first inception, aerial photography has been used for topographic mapping. Large-scale aerial photography contributed to the creation of many of the topographic maps around the world. In Indonesia, a 2013 government directive on spatial management has re-stressed the need for topographic maps, with aerial photogrammetry providing the main method of acquisition. However, the large need to generate such maps is often limited by budgetary reasons. Today, SfM (Structure-from-Motion) offers quicker and less expensive solutions to this problem. However, considering the required precision for topographic missions, these solutions need to be assessed to see if they provide enough level of accuracy. In this paper, a popular SfM-based software Agisoft PhotoScan is used to perform bundle adjustment on a set of large-scale aerial images. The aim of the paper is to compare its bundle adjustment results with those generated by more classical photogrammetric software, namely Trimble Inpho and ERDAS IMAGINE. Furthermore, in order to provide more bundle adjustment statistics to be compared, the Damped Bundle Adjustment Toolbox (DBAT) was also used to reprocess the PhotoScan project. Results show that PhotoScan results are less stable than those generated by the two photogrammetric software programmes. This translates to lower accuracy, which may impact the final photogrammetric product

Satellite-derived bathymetry using convolutional neural networks and multispectral sentinel-2 images

Satellite-Derived Bathymetry (SDB) has been used in many applications related to coastal management. SDB can efficiently fill data gaps obtained from traditional measurements with echo sounding. However, it still requires numerous training data, which is not available in many areas. Furthermore, the accuracy problem still arises considering the linear model could not address the non-relationship between reflectance and depth due to bottom variations and noise. Convolutional Neural Networks (CNN) offers the ability to capture the connection between neighbouring pixels and the non-linear relationship. These CNN characteristics make it compelling to be used for shallow water depth extraction. We investigate the accuracy of different architectures using different window sizes and band combinations. We use Sentinel-2 Level 2A images to provide reflectance values, and Lidar and Multi Beam Echo Sounder (MBES) datasets are used as depth references to train and test the model. A set of Sentinel-2 and in-situ depth subimage pairs are extracted to perform CNN training. The model is compared to the linear transform and applied to two other study areas. Resulting accuracy ranges from 1.3m to 1.94m, and the coefficient of determination reaches 0.94. The SDB model generated using a window size of 9x9 indicates compatibility with the reference depths, especially at areas deeper than 15m. The addition of both short wave infrared bands to the four visible bands in training improves the overall accuracy of SDB. The implementation of the pre-trained model to other study areas provides similar results depending on the water conditions.Urban Data Scienc

A comparative study of point clouds semantic segmentation using three different neural networks on the railway station dataset

Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.GIS TechnologieArchitecture and the Built Environmen