Geomatics and Environmental Engineering

PyLiGram – Research Application for LiDAR Data Processing Based on Photogrammetric Methods

2025-03-19T14:41:56+01:00

This paper presents the functionality and research possibilities of an application that is based on two concepts: the use of photogrammetric analysis for LiDAR data processing (lidargrammetry), and the assignments of identifiers to cloud points in order to be able to return to the original points after processing without data loss and redundant processing.
The research tool has, thus far, been developed for the implementation of two distinct LiDAR data-enhancement processes. The initial approach involves the altimetric transformation of the LiDAR data (a process that is founded on the principles of stereo model deformation theory), and the second process employs lidargrammetry for the purpose of 3D local point-cloud corrections, global changes, or non-rigid transformation. This is achieved by applying blocks of lidargrams and their subsequent matching and adjustments.

River Area Segmentation Using Sentinel-1 SAR Imagery with Deep-Learning Approach

2025-06-13T17:30:26+02:00

River segmentation is important in delivering essential information for environmental analytics such as water management, flood/disaster management, observations of climate change, or human activities. Advances in remote-sensing technology have provided more complex features that limit the traditional approaches’ effectiveness. This work uses deep-learning-based models to enhance river extractions from satellite imagery. With Resnet-50 as the backbone network, CNN U-Net and DeepLabv3+ were utilized to perform the river segmentation of the Sentinel-1 C-Band synthetic aperture radar (SAR) imagery. The SAR data was selected due to its capability to capture surface details regardless of weather conditions, with VV+VH band polarizations being employed to improve water surface reflectivity. A total of 1080 images were utilized to train and test the models. The models’ performance was measured using the Dice coefficient. The CNN U-Net architecture achieved an accuracy of 0.94, while DeepLabv3+ attained an accuracy of 0.92. Although DeepLabv3+ showed more stability during the training and performed better on wider rivers, CNN U-Net excelled at identifying narrow rivers. In conclusion, a river-segmentation model was conducted using Sentinel-1 C-Band SAR data, with CNN U-Net outperforming DeepLabv3+; this enabled detailed river mapping for irrigationand flood-monitoring applications – particularly in cloud-prone tropical regions.

Synthetic Aperture Radar Technology for Policies Contributing to Natural Disaster Mitigation and Food Security Issues in Indonesia

2025-02-05T12:24:54+01:00

Natural disasters and food security challenges frequently impact many countries, including Indonesia. Over the past decade, the development of remote-sensing technology (particularly, synthetic aperture radar [SAR]) has garnered the attention of policymakers. Its ability to penetrate clouds and rain and data-acquisition techniques unaffected by time (day or night) provide advantages for describing the equatorial region. The application of SAR technology in Indonesia has progressed significantly. However, an important question has arisen: to what extent is the impact of using SAR data? Most SAR data in Indonesia is still limited to academic circles. To address this question comprehensively, this research examines the extent to which studies utilize SAR data – particularly, in global publications. The scope of this research was limited to articles published between 2013 and early 2025 that utilized SAR as the primary or secondary methods. The gap between the numerous studies on SAR technology and its significant impact on various government policies was quantitatively analyzed. In conclusion, this research underscored the need for a more nuanced approach toward integrating SAR technology into policymaking in Indonesia. This study serves as a critical reflection on the current state of SAR research in Indonesia, calling for a more concerted effort to bridge the gap between technical studies and actionable policy formulation.

Methodology for Evaluating Applicability of Real Estate Information Systems

2025-05-14T09:32:01+02:00

This article proposes a methodology for evaluating the applicability of real estate information systems. Despite the fact that such systems have been created by different agencies and for different reasons, all of them should effectively deliver their designed functionalities. These functionalities are determined by the types of users. Real estate information systems have been developed on the assumption that they would be accessible to all citizens; therefore, the applicability of these systems should be examined. In this context, applicability is defined as the degree to which a product, system, or service enables users to achieve their goals in an efficient, effective, and satisfactory manner. A universal methodology for evaluating the applicability of a given system has been proposed and validated in this study. Certain limitations of real estate information systems that could affect their use in land administration procedures were also identified.

Evaluation of Post-Processing Kinematic (PPK) Accuracy in Urban Area in Turgutlu, Manisa, Türkiye

2025-06-19T13:34:47+02:00

In recent years, global navigation satellite systems (GNSSs) have emerged as a prominent technology for geolocation applications and services in urban settings. Urban environments should also be classified under difficult situations. Densely populated metropolitan areas such as urban centers obstruct the receipt of GNSS signals; these obstacles often result in the congestion of line-of-sight (LOS) signals and give rise to the receipt of diffracted or reflected echoes (often known as the multipath phenomenon). PPK (post-processing kinematic) is a GNSS data-processing method that achieves high-accuracy positioning by correcting errors in raw positioning data. Post-processing is widely used in applications that require precise geospatial information, such as surveying, mapping, and UAV operations. This research aims to evaluate the accuracy of the PPK application method in urban areas. For this aim, surveys were carried out in Turgutlu’s province of Manisa on July 15, 2020, in Türkiye. The analysis compared the PPK surveys’ results with those that were obtained from static surveys. PPK is very effective in difficult situations, but we were likely to encounter certain accuracy problems. Nevertheless, it is worth noting that achieving urban surveys with an accuracy from ±1 cm to ±2 cm may not always be feasible due to the challenging circumstances that might result in moresignificant inaccuracies from ±10 cm to ±100 cm for both the horizontal and vertical components.