Terrain Mapping Research Articles

Surveying and mapping of large-scale 3D digital topographic map based on oblique photography technology

In the fields of geographic information systems and cartography, accurate topographic maps are crucial. Although traditional topographic mapping methods have certain accuracy, they have limitations in capturing features and building details. Therefore, this article proposes a large-scale 3D digital terrain mapping method based on oblique photography technology. Firstly, the UAV route is planned and image control points are arranged, to collect large-scale geographical object data by using UAV oblique photography technology. Then Smart 3D software is used to complete the 3D modeling of real large-scale topographic map through data preprocessing, air-to-air survey, control point import, 3D modeling, texture mapping and other steps based on the obtained geographical object data. The 2D and 3D linkage integrated mapping mode is adopted to obtain geomorphic information and elevation information, complete the data collection of indoor features, confirm the features that cannot be read on both sides of the indoor industry, complete the field supplementary mapping and adjustment, and realize the mapping of large-scale 3D digital topographic map. The experimental results show that this method can use oblique photography technology to collect data and establish a three-dimensional model of large-scale 3D digital terrain maps. The mean square error of accuracy detection is 0.202m, the mean square error of elevation is 0.257m, the mean square error of plane is 0.201m, and the mean square error of elevation is 0.197m. It has high drawing accuracy and excellent aerial triangulation performance.

An Adaptive Two-Dimensional Voxel Terrain Mapping Method for Structured Environment

Accurate terrain mapping information is very important for foot landing planning and motion control in foot robots. Therefore, a terrain mapping method suitable for an indoor structured environment is proposed in this paper. Firstly, by constructing a terrain mapping framework and adding the estimation of the robot’s pose, the algorithm converts the distance sensor measurement results into terrain height information and maps them into the voxel grid, and effectively reducing the influence of pose uncertainty in a robot system. Secondly, the height information mapped into the voxel grid is downsampled to reduce information redundancy. Finally, a preemptive random sample consistency (preemptive RANSAC) algorithm is used to divide the plane from the height information of the environment and merge the voxel grid in the extracted plane to realize the adaptive resolution 2D voxel terrain mapping (ARVTM) in the structured environment. Experiments show that the proposed mapping algorithm reduces the error of terrain mapping by 62.7% and increases the speed of terrain mapping by 25.1%. The algorithm can effectively identify and extract plane features in a structured environment, reducing the complexity of terrain mapping information, and improving the speed of terrain mapping.

Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system

ABSTRACT A deep learning (DL) terrain classification system, the Novelty and Anomaly Hunter – HiRISE (NOAH-H) was used to produce a terrain map of Mawrth Vallis, Mars. With it, we digitised the extent and distribution of transverse aeolian ridges (TARs), a common type of martian aeolian bedform. We present maps of the site, classifying terrain into descriptive classes and interpretive groups. TAR density maps are calculated, and the network output is compared to a manually produced map of TAR density, highlighting the differences in approach and results between these methods. Even when mapping on a small scale, humans must divide the terrain into coherent patches in order to map a large area in a reasonable time frame. Conversely, the speed of DL systems enables mapping on the pixel scale, producing a more detailed product, but one which is also “noisier”, and less immediately informative. There are pros and cons to both approaches.

Cooperative behavior of a heterogeneous robot team for planetary exploration using deep reinforcement learning

As we continue exploration of the Lunar and Martian surfaces and push farther into unknown and unstructured environments, employing a team of distributed heterogeneous robots will increase the odds of success by enabling more complex task planning that utilizes the complementary capabilities and synergy of the team members. A requirement to reap these potential benefits is effective cooperation and collaboration between the members of a robot team. Defining a metric for effective cooperation is a difficult task that will depend on the composition of the team, the task to be performed, and the working environment. This paper establishes a method for determining the reward criteria (figures of merit) that can be used for training the robot swarm through reinforcement learning techniques. A hierarchical framework of rewards is used which, at the lowest level, measures the success of an individual robot in performing its task. The success of all robots performing different subtasks is then measured using the Quantified Cooperation Assessment (QCA) metric which was introduced in our previous research of multi-robot collaboration. Finally, the mission-level success and overall reward is determined by weighting each task using its priority within the overall mission context. A common reward for each of the robotic teammates is then applied within the learning process, which emphasizes group performance over that of an individual and encourages cooperative behavior. This cooperation framework is trained in a grid-based environment representing an exploration mission on a planetary surface by a heterogenous team of robots consisting of a landing craft, a traditional rover, and several small agile robots. The robots are trained concurrently, but with individual policies developed for each agent resulting in a decentralized control scheme. Once trained, the control policies were evaluated in several test environments consisting of novel terrain maps and regions of interest. An average success rate of 90 % was seen in the test environments demonstrating the robustness of the trained policies. The robots have been trained to not only complete the mission but also perform it in a cooperative manner as well as show the scalable and resilient behavior.

From celebration to contamination: Analysing microplastics released by burst balloons

Air balloons are a ubiquitous presence in our daily lives, and their rupture may release a substantial quantity of debris, as investigated herein. We employ Raman imaging to capture the fragments resulting from balloon explosions, enabling the identification and direct visualisation of minute microplastic particles / fragments with an improved signal-to-noise ratio for precise quantification. To circumvent the generation of misleading confocal Raman images, we recommend employing terrain mapping to scan the three-dimensional surface of the sample. It is important to acknowledge that the analysis of microplastics at the micro-scale inherently poses limitations in terms of throughput, as it necessitates a trade-off between low and high magnifications. We conduct explosive experiments on ten-to-hundred balloons, collecting debris from various angles and positions. Our investigation involves the random testing of multiple samples / sample positions at the micro-scale, with subsequent extrapolation to estimate the total amount of microplastics. The amalgamation of these results through statistical analysis indicates that each balloon explosion can potentially release tens-to-thousands of microplastics, highlighting a concern that has hitherto received insufficient attention. The characterisation approach, particularly the random Raman scanning method in combination with SEM and the statistical analysis on accumulated samples employed in this report, has the potential to serve as a useful tool in future research on microplastics and even nanoplastics.

Drone Lidar Deep Learning for Fine-Scale Bare Earth Surface and 3D Marsh Mapping in Intertidal Estuaries

Tidal marshes are dynamic environments providing important ecological and economic services in coastal regions. With accelerating climate change and sea level rise (SLR), marsh mortality and wetland conversion have been observed on global coasts. For sustainable coastal management, accurate projection of SLR-induced tidal inundation and flooding requires fine-scale 3D terrain of the intertidal zones. The airborne Lidar systems, although successful in extracting terrestrial topography, suffer from high vertical uncertainties in coastal wetlands due to tidal effects. This study tests the feasibility of drone Lidar leveraging deep learning of point clouds on 3D marsh mapping. In an ocean-front, pristine estuary dominated by Spartina alterniflora, drone Lidar point clouds, and in-field marsh samples were collected. The RandLA-Net deep learning model was applied to classify the Lidar point cloud to ground, low vegetation, and high vegetation with an overall accuracy of around 0.84. With the extracted digital terrain model and digital surface model, the cm-level bare earth surfaces and marsh heights were mapped. The bare earth terrain reached a vertical accuracy (root-mean-square error, or RMSE) of 5.55 cm. At the 65 marsh samples, the drone Lidar-extracted marsh height was lower than the in-field height measurements. However, their strongly significantly linear relationship (Pearson’s r = 0.93) reflects the validity of the drone Lidar for measuring marsh canopy height. The adjusted Lidar-extracted marsh height had an RMSE of 0.12 m. This experiment demonstrates a multi-step operational procedure to deploy drone Lidar for accurate, fine-scale terrain and 3D marsh mapping, which provides essential base layers for projecting wetland inundation in various climate change and SLR scenarios.

Automated remote sensing tools to counter illicit maritime activity: vessel detection, bathymetry and topography from WorldView imagery

ABSTRACT Illicit maritime activity, such as piracy and smuggling, is a global issue that often occurs in areas where interdiction authorities are sparse, ground-based monitoring technologies like radar lack range and coverage, and dark vessels abound. Our objective was to assess vessel congregation patterns, identify likely beaching areas based on terrain maps, and use them to narrow the search for potential smuggling transfer and overland routes in a specific region along the Puntland coast of Somalia. To accomplish this goal, we developed automated protocols applied to WorldView satellite imagery for (1) vessel detection and size classification, (2) shallow-water bathymetric characterization, and (3) coastal topography mapping. Utilizing a single sensor for vessel detection and topographic and bathymetric extraction at high spatial resolution and at a high re-visit rate provides a simplification to near-shore characterization for monitoring purposes. The extracted topography and bathymetry are then presented as a single, comprehensive perspective of a coastal region. The vessel-detection algorithm identified all vessels larger than approximately 15 metres in length (35 of 35), but misidentified six artefacts (i.e. false positives), resulting in an overall accuracy of 85%. The combined vessel and terrain maps facilitated the identification of a potential beaching and overland transportation route location.

Mapping intertidal topographic changes in a highly turbid estuary using dense Sentinel-2 time series with deep learning

Intertidal mudflats are an important component of the coastal geomorphological system at the interface between ocean and land. Accurate and up-to-date mapping of intertidal topography at high spatial resolution, and tracking of its changes over time, are essential for coastal habitat protection, sustainable management and vulnerability analysis. Compared with ground-based or airborne terrain mapping, the satellite-based waterline method is more cost-effective for constructing large-scale intertidal topography. However, the accuracy of the waterline method is affected by the extraction of waterlines and the calibration of waterline height. The blurred boundary between turbid water and mudflats in the tide-dominated estuary brings enormous challenges in accurate waterline extraction, and the errors in estuarine water level simulations prevent the direct calibration of waterline heights. To address these issues, this paper developed a novel deep learning method using a parallel self-attention mechanism and boundary-focused hybrid loss to extract turbid estuarine waterlines accurately from dense Sentinel-2 time series. UAV photogrammetric surveys were employed to calibrate waterline heights rather than the simulated water levels, such that the error propagation is constrained effectively. Annual intertidal topographic maps of the Yangtze estuary in China were generated from 2020 to 2022 using the optimized waterline method. Experimental results demonstrate that the proposed deep learning method could achieve excellent performance in land and water segmentation in time-varying tidal environments, with better generalization capability compared with benchmark U-Net, U-Net++ and U-Net+++ models. The comparison between the generated topography and UAV photogrammetric observations resulted in an RMSE of 13 cm, indicating the effectiveness of the optimized waterline method in monitoring morphological changes in estuarine mudflats. The generated topographic maps successfully identified hotspots of mudflat erosion and deposition. Specifically, the mudflats connected to the land predominantly experienced deposition of 10–20 cm over the two-year period, whereas the offshore sandbars exhibited instability and significant erosion of 20–60 cm during the same period. These topographic maps serve as valuable datasets for providing scientific baseline information to support coastal management decisions.

Research on land cover classification of multi-source remote sensing data based on improved U-net network

In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent.

Research on Scenario Modeling for V-Tail Fixed-Wing UAV Dynamic Obstacle Avoidance

With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.

A Millimeter-Wave Radar-Aided Vision Detection Method for Water Surface Small Object Detection

Unmanned surface vehicles (USVs) have wide applications in marine inspection and monitoring, terrain mapping, and water surface cleaning. Accurate and robust environment perception ability is essential for achieving autonomy in USVs. Small object detection on water surfaces is an important environment perception task, typically achieved by visual detection using cameras. However, existing vision-based small object detection methods suffer from performance degradation in complex water surface environments. Therefore, in this paper, we propose a millimeter-wave (mmWave) radar-aided vision detection method that enables automatic data association and fusion between mmWave radar point clouds and images. Through testing on real-world data, the proposed method demonstrates significant performance improvement over vision-based object detection methods without introducing more computational costs, making it suitable for real-time application on USVs. Furthermore, the image–radar data association model in the proposed method can serve as a plug-and-play module for other object detection methods.

A highlight removal method for autonomous recovery of cable-free seismographs in field environments

The cable-free seismograph is an instrument for capturing seismic wave signals, which is popularly employed in underground resource exploration tasks in complex field environments due to its high flexibility. However, the efficiency of exploration operations is low because the arrangement and recovery of instruments are done manually. Therefore, autonomously arranging and recovering cable-free seismographs will hold great significance. Nevertheless, the instrument surface will produce highlight under intense illumination in field environments, which can have a negative impact on the accuracy of segmentation and contour extraction of the instrument. It will significantly reduce the visual positioning accuracy of the instrument and ultimately fail in recovery tasks. To address this problem, we analyze the characteristics of highlight on cable-free seismographs in field environments and develop a method to remove the highlight by weighting the color features of the non-highlight pixels in the neighborhoods of highlight pixels. We realize the detection of highlight pixels based on the classical modified specular-free model and propose an image terrain map model to select the weighted neighborhoods of highlight pixels to ensure that no background pixels are involved in the process of highlight removal. The experimental results show that our method meets the requirements for contour extraction and object segmentation in the autonomous instrument recovery tasks. Furthermore, it outperforms currently existing highlight removal methods in the task of highlight removal for cable-free seismographs in field environments.

Raman imaging to identify microplastics released from toothbrushes: algorithms and particle analysis

Microplastics are small plastic fragments that are of increasing concern due to their potential impacts on the environment and human health. The source of microplastics is not completely clear and might originate in daily lives such as from toothbrushes. When toothbrushes are used to clean teeth, small plastic debris and fragments can be potentially released into mouths directly or environment indirectly. This study aims to examine the release of microplastics from toothbrushes, using Raman imaging to identify and visualise the plastic debris with an increased signal-noise ratio via hyper-spectrum analysis. Using algorithms to convert the hyper-spectrum to an image, the plastic can be distinguished from the co-formulated titanium oxide particles that are not uniformly distributed along the plastics. The non-uniform distribution can lead to the bias results if a single spectrum analysis is conducted at one position rather than imaging analysis to scan an area. The potential false image originating from the off-focal position for the confocal Raman is overcome using the terrain map to guide the Raman imaging. The imaging analysis balancing between the low magnification to capture the overview and the high magnification to test the details is also discussed. While the release amount of microplastics from the toothbrush is estimated at thousands daily with the expected variation, the results of this study have confirmed the release of microplastics in daily lives. The imaging analysis approach along with algorithm can help to identify the chemical elements of microplastics from the complex background, which can benefit the further research on microplastics towards risk assessment and remediation.

Planning the Optimal Reference Flight Path of an Aircraft Using a Terrain Map

In connection with the development of aviation technology, the expansion of the functionality of modern aircraft and the complication of the tasks being solved, additional stringent requirements are imposed on the accuracy of air navigation. Along with high accuracy, navigation autonomy is also required. The autonomy of aircraft navigation implies the navigation of an aircraft without the use of active radar facilities. In this article, to calculate the optimal reference flight path of an aircraft in a horizontal plane, the Bellman dynamic programming method is proposed using a digital terrain map. A technique, algorithms have been developed, and a numerical experiment has been carried out using fragments of a digital terrain map. High accuracy and autonomy of navigation are necessary, among other things, to ensure stealth (invisibility) of aircraft by ground-based radar facilities of a potential enemy in low-altitude flight mode. The best stealth of an aircraft flight in a low-altitude flight mode is achieved not only by flying around, but also by avoiding obstacles, due to the shielding properties of the earth’s surface. Autonomy of navigation provides periodic correction of the reference flight path during long-term flight, and high accuracy prevents aircraft collisions with the earth’s surface in low-altitude flight mode. The purpose of this work is to select the optimal reference trajectory (route) of an aircraft flight in the low-altitude flight mode. This article discusses a method developed based on the principle of dynamic programming for calculating the reference flight path in the horizontal plane depending on the DEM and the implementation of the algorithm based on the proposed method with a specific numerical example. The method makes it possible to calculate the optimal flight route, which provides the greatest secrecy of the aircraft in the low-altitude flight mode using the screening properties of the earth’s surface irregularities.

Quadcopter Prototype Stability Assessment With Pid Controller And Euler-Lagrange Approach

Abstract The increasing use of drones in various fields has led to their popularity in developed countries due to their ease of use and manufacture. This Miniature Pilotless Aircraft has numerous beneficial usages such as express shipping, gathering information, crop monitoring, cargo transport, storm tracking, geographic mapping of inaccessible terrain, search and rescue operations, among others. This study aims to investigate the stability of a quadcopter through simulations based on the mathematical model that describes the quadcopter’s dynamic and flight mechanics, using the Euler-Lagrange approach. It conducts simulations in MATLAB and present the principles that govern quadcopter stability, focusing on setting the PID coefficients to achieve optimal stability. This study provides insights into the principles of drone mechanics and stability, enabling us to better understand the quadcopter’s behavior and performance.

Integrating Electric Vertical Takeoff and Landing Aircraft into Public Airspace: A Scenario Study

The successful deployment of Advanced Air Mobility (AAM) requires the safe integration of electric vertical takeoff and landing (eVTOL) aircraft in non-segregated airspace. However, a lack of practical deployment demonstrations can slow the regulatory progress. This study presents a practical deployment framework for the integration of eVTOL aircraft into public airspace, in accordance with the Federal Aviation Administration (FAA) special airworthiness criteria. This study focused on a corporate use case to provide a comprehensive scenario for enhancing intra-company mobility. The scenario supports early implementation and demonstration goals by removing reliance on air traffic management systems or air traffic controller separation services. The practical deployment framework included facility selection, route planning, terrain mapping, collision avoidance, aircraft selection, infrastructure requirements, vertiport placement, electric chargers, ground handling, operational staff, and cost estimates. Planners can use the framework as a template to inform the implementation of real-world AAM services using eVTOL aircraft.

Retracted: SLAM 3D Digital Terrain Mapping with SqueezeNet Driven by Road Traffic Data

Retracted: SLAM 3D Digital Terrain Mapping with SqueezeNet Driven by Road Traffic Data

The Terprom® Digital Terrain System (DTS)

Developed specifically for tactical military aircraft operations, TERPROM® is the world leading Digital Terrain System, offering a range of capabilities that can provide both safety and tactical benefits to the aircraft. The system is an established and well-proven product that has been in production since 1991. To date it has been selected by fourteen Nations worldwide for use on numerous platforms. The number of military aircraft equipped with TERPROM® is now over 5000 and growing. TERPROM® provides the following functions: Terrain Referenced Navigation (TRN), Predictive Ground Collision Avoidance System (PGCAS), Advanced Terrain Avoidance Cueing (ATAC), Obstruction and Wires Warning and Cueing (OWWC), Database Terrain Following (DBTF), Air to Ground Ranging Functions, Terrain Awareness Display (TAD). At the core of the system is the Terrain Referenced Navigation (TRN) capability which navigates by combining aircraft navigation data (e.g. inertial and, or GPS) and radar altimeter measurements of height above ground level (AGI), with terrain heights alms from the stored map data, to produce a navigation solution referenced to the terrain map data. This navigation solution then allows the other TERPROM® functions to use the map data to provide their safety and tactical capabilities. Three variants of TERPROM® have been developed to satisfy the airborne platform market: - Fast Jet, Military Transport, Helicopter. TERPROM® is a living product that has and continues to receive significant internal investment to increase its capability, functionality and performance to support future opportunities. Current areas of development include: Use of higher resolution map data which will increase the performance of TERPROM® and offer enhanced capability to platforms flying nap of the earth, enhanced capability in urban areas and could also reduce any platform reliance on GPS. Integration with active forward looking sensors to provide additional information to the Navigation, Situational Awareness and Ranging functions and also to provide new capabilities such as real time mapping of unmapped terrain features and obstructions.

Architecture of Shelly beach ridges in a sheltered beach environment in Punta Perdices beach, San Antonio bay, Argentina

On the northern coast of the San Matías Gulf, Argentina, San Antonio Bay is developed, with an approximate area of 160 km2. The region has a diversity of depositional systems, both inside and outside the bay, which are morphologically characterized by the deposits of plains and tidal channels, beach ridges, beaches and dunes. In this environment relatively protected by the Villarino Peninsula, against the high energy of waves coming from the gulf, the circulation of water is governed by a regime of semidiurnal macro-tides with syzygy amplitudes of up to 9 m, which enter through the single channel, communicating the bay to the ocean. The edges of the Villarino Peninsula have a large number of beach ridges, in the form of extensive modern bioclastic deposits, where bivalves with whole, fragmented, and gastropod valves predominate. The objective of this work is the development of models of depositional architecture of the coquinas of the Villarino Peninsula, through terrain mapping (DGPS) and acquisition of lines by ground penetration radar (GPR), in addition to understanding the Holocene-modern sedimentary dynamics of this region. Surveys with GPR were carried out in the sector of Punta Perdices Beach in the Peninsula Villarino sectors of bioclastic deposits, in a total of 23 GP R lines, where there are several sets of beach ridges (Holocene) with an average width of 100 m, which extend for more than 1 km, located in the sheltered area of the bay. The processed and interpreted GPR data were used to investigate the lateral and vertical relationships of the facies, the depositional geometries and geophysical signatures, developed by progradation mechanisms induced by the hydrodynamics of waves and tides. The genesis of these a lot of bioclasts deposits are discussed, mainly due to the macro tide regime of the region and the littoral drift of the semicircular bay, where the geomorphological features of an actual coastal terrace were described. Depositional models of coquinas in Pleistocene, Holocene, and actual beach environments are scarce in the literature. The obtained results can be analyzed and compared to analogues of hydrocarbon deposits.

Determining recharge area in ULUBELU geothermal field, LAMPUNG, Indonesia using stable isotope data

The Ulubelu geothermal field is located in Lampung Province, Indonesia, about 125 km west of Bandar Lampung City. This field is utilized as a power plant with an installed capacity of 220 MW operated since 2012. The Ulubelu geothermal system is a high enthalpy water-dominated reservoir and has temperature around 265 °C. This study aims to determine the elevation of the recharge area in the Ulubelu geothermal system based on the values of δ2H and δ18O isotopes to support the geothermal use sustainability. Major ions, δ2H, δ18O, and δ13C(DIC) isotopes analysis were carried out on hot springs, lake water, cold springs, and rainwater. The content of the major anions and cations indicates that the hot springs from the Way Panas Group are chloride-type water originating from the reservoir meanwhile the Danau Hijau and Pagar Alam groups are condensates acid-sulphate fluids. Moreover, the calculation using a Na/K and silica geothermometer shows that the reservoir has a temperature around 229 ± 4 °C. Analysis of the δ2H and δ18O isotopes content informs that the origin meteoric water seeped into the geothermal reservoir was δ18O −6.65‰ and δ2H −39.5‰ ranging from 1108 to 1570 masl elevation. Overlaying with the surface terrain map depicts that the recharge area originates from the peak of Mt. Rendingan in the north of Ulubelu and Mt. Sula in the west with 42.9 km2 total area. In conclusion, this study elucidates the recharge area elevation in the Ulubelu geothermal system, offering crucial insights for sustainable geothermal resource management and reservoir origin understanding, thus highlighting its potential for sustainable energy production.