Developments in UAV sensors and platforms in recent decades have stimulated an upsurge in its application for 3D mapping. The relatively low-cost nature of UAVs combined with the use of revolutionary photogrammetric algorithms, such as dense image matching, has made it a strong competitor to aerial lidar mapping. However, in the context of 3D city mapping, further 3D modeling is required to generate 3D city models which is often performed manually using, e.g., photogrammetric stereoplotting. The aim of the paper was to try to implement an algorithmic approach to building point cloud segmentation, from which an automated workflow for the generation of roof planes will also be presented. 3D models of buildings are then created using the roofs’ planes as a base, therefore satisfying the requirements for a Level of Detail (LoD) 2 in the CityGML paradigm. Consequently, the paper attempts to create an automated workflow starting from UAV-derived point clouds to LoD 2-compatible 3D model. Results show that the rule-based segmentation approach presented in this paper works well with the additional advantage of instance segmentation and automatic semantic attribute annotation, while the 3D modeling algorithm performs well for low to medium complexity roofs. The proposed workflow can therefore be implemented for simple roofs with a relatively low number of planar surfaces. Furthermore, the automated approach to the 3D modeling process also helps to maintain the geometric requirements of CityGML such as 3D polygon coplanarity vis-à-vis manual stereoplotting.

The development and management of green open spaces are essential in overcoming environmental problems such as air pollution and urban warming. 3D modeling and biomass calculation are the example efforts in managing green open spaces. In this study, 3D modeling was carried out on point clouds data acquired by the UAV photogrammetry and UAV LiDAR methods. 3D modeling is done explicitly using the point clouds fitting method. This study uses three fitting methods: the spherical fitting method, the ellipsoid fitting method, and the spherical harmonics fitting method. The spherical harmonics fitting method provides the best results and produces an R2 value between 0.324 to 0.945. In this study, Above-Ground Biomass (AGB) calculations were also carried out from the modeling results using three methods with UAV LiDAR and Photogrammetry data. AGB calculation using UAV LiDAR data gives better results than using photogrammetric data. AGB calculation using UAV LiDAR data gives an accuracy of 78% of the field validation results. However, for visualization purposes with a not-too-wide area, a 3D model of photogrammetric data using the spherical harmonics method can be used.

The food crisis is a problem that the world will face. The availability of growing areas that continues to decrease with the increase in food demand will result in a food crisis in the future. Good planning is needed to deal with future food crises. The absence of studies on the development of spatial models in estimating an area’s future food status has made planning for handling the food crisis suboptimal. This study aims to predict food security by integrating the availability of paddy fields with environmental factors to determine the food status in West Java Province. Food status modeling is done by integrating land cover, population, paddy fields productivity, and identifying the influence of environmental factors. The land cover prediction will be developed using the CA-Markov model. Meanwhile, to identify the influence of environmental factors, multivariable linear regression (MLR) was used with environmental factors from remote sensing observations. The data used are in the form of the NDDI (Normalized Difference Drought Index), NDVI (Normalized Difference Vegetation Index), land surface temperature (LST), soil moisture, precipitation, altitude, and slopes. The land cover prediction has an overall accuracy of up to 93%. From the food status in 2005, the flow of food energy in West Java was still able to cover the food needs and obtain an energy surplus of 6.103 Mcal. On the other hand, the prediction of the food energy flow from the food status in 2030 will not cover food needs and obtain an energy deficit of up to 13,996,292.42 Mcal. From the MLR results, seven environmental factors affect the productivity of paddy fields, with the determination coefficient reaching 50.6%. Thus, predicting the availability of paddy production will be more specific if it integrates environmental factors. With this study, it is hoped that it can be used as planning material for mitigating food crises in the future.

Indonesia is the largest archipelagic country consisting of 17,504 islands which have 99,093 km of coastline. From the total, approximately only 10% had mapped. The coastline is essential for several applications such as topographic height reference, a reference in the delimitation of the marine management area, coastal boundaries, etc. Law number 4 of 2011 (UUIG), in article 13 paragraph 2 concerning Geospatial Information, mentioned three types of coastlines, namely: (a) the lowest astronomical tide, (b) the highest astronomical tide, and (c) the mean sea level. The existing method for determining the coastlines is observing a tide gauge over a long period at several places, then densify the point height by levelling method. This method is less effective due to time, cost, and amount of sample points. This paper presents our experience on coastlines determination by extracting it from a digital terrain model (DTM). The Airborne Topo-Bathymetric LiDAR technology is utilized to provide DTM that covers land and seabed. The points cloud, which is the output of this technology, was transformed to the geoid and corrected by tidal datum before those three types of coastlines were determined and delineated. The Western Part of Java Island is a study area. The project covers 1,000 km of coastline. The DTM quality was validated using several independent check-points along the coastline and hundreds of shorelines transect points at two locations. The result shows that vertical accuracy within the decimeter level.

AbstrakMitigasi bencana merupakan salah satu hal penting yang harus dipertimbangkan terutama dalam konstruksi bangunan karena hal tersebut cukup rumit terlebih apabila dikaitkan dengan fakta tidak adanya informasi yang dapat digunakan untuk orang-orang menyelamatkan diri mereka sendiri. Maka dari itu, makalah ilmiah ini memperkenalkan mengenai network analysist untuk rute evakuasi darurat yang bertujuan untuk mencari rute terbaik menuju tempat aman seperti titik berkumpul tergantung pada situasi terkini. Pembuatan keputusan berdasarkan rute yang tepat akan dipilih berdasarkan kategori usia korban dan kondisi saat bencana terjadi, sehingga dapat mengurangi dampak buruk yang akan muncul. Algoritma Dijkstra menunjukan suatu algoritma perncarian rute terpendek antara gedung dan titik berkumpul dengan menghubungkan keduanya melalui data jalan. Model rute evakuasi ini dibentuk dengan menggunakan kombinasi antara model bangunan tiga dimensi yang dibangun dari data LiDAR, orthophoto, dan data lainnya yang berkaitan dengan pemodelan. Bangunan tiga dimensi dapat digunakan dalam manajemen bencana dan respon darurat karena dapat menyediakan informasi penting seperti lokasi bangunan. Evaluasi dari model yang diajukan meningkatkan kemampuan penyelamatan diri sendiri yang mengarah pada berkurangnya dampak buruk yang akan terjadi.Kata kunci: Evakuasi Darurat, Algoritma Dijkstra, LiDAR, pemodelan bangunan 3D AbstractMitigation is an important thing to be considered especially in building construction because it is quite complicated due to the fact that much of the information is unavailable for people to rescue themselves. Hence, this paper introduces about network analysis for evacuation emergency route which aims at finding the best route to the secured place such as the closest assembly point depends on the situation. Thus, decision making regarding the proper route to be chosen depends of the victim age category and current condition to minimize impact that can be generated. Dijkstra’s Algorithm is presented an algorithm for finding the shortest paths between building and assembly point by linking them through road data. This emergency evacuation route model is constructed by combining with three dimensional building model which constructed by using LiDAR data, orthophoto, and the other related data. Three dimensional geo data can be used in disaster management and emergency response because they may provide valuable information such as location of the building. The evaluation of the proposed model for a case study building improve self-sustaining which lead to chances of less adverse effects can appear.Keywords: Emergency Evacuation, Dijkstra’s Algorithm, LiDAR, 3D building model