As one of the most important sectors of the global economy, the construction industry has continuously sought to optimize its processes. However, construction remains characterized by a highly fragmented value chain, low productivity, and limited digitalisation. Introducing Cyber-Physical Construction Systems offers a way to address these challenges by enabling a more flexible, automated, and collaborative approach. In this paper, a cyber-physical design-to-fabrication workflow was developed using terrestrial laser scanning as a sensing tool, augmented reality as a design interface, and an extrusion concrete printer as an actuator. As a result of the cyber-physical construction workflow, two benches were digitally fabricated as a case study by combining reclaimed bricks with 3D concrete-printed components. The developed workflow enables designers to use augmented reality to generate an adapted path for 3D-printed structures. The study highlights the potential of Cyber-Physical Construction Systems to improve automation and efficiency while fostering human-robot collaboration through human-in-the-loop approaches. However, the case study revealed key challenges, including gaps in automation and scalability, that must be addressed for broader implementation. Future work should focus on refining the integration of sensing technologies, improving communication between digital and physical systems, and optimizing collaborative workflows to leverage the benefits of Cyber-Physical Construction Systems in construction fully.

Given the substantial growth in the use of additive manufacturing in construction (AMC), it is necessary to ensure the quality of printed specimens which can be much more complex than conventionally manufactured parts. This study explores the various aspects of geometry and surface quality control for 3D concrete printing (3DCP), with a particular emphasis on deposition-based methods, namely extrusion and shotcrete 3D printing (SC3DP). A comprehensive overview of existing quality control (QC) methods and strategies is provided and preceded by an in-depth discussion. Four categories of data capture technologies are investigated and their advantages and limitations in the context of AMC are discussed. Additionally, the effects of environmental conditions and objects' properties on data capture are also analyzed. The study extends to automated data capture planning methods for different sensors. Furthermore, various quality control strategies are explored across different stages of the fabrication cycle of the printed object including: (i) During printing, (ii) Layer-wise, (iii) Preassembly, and (iv) Assembly. In addition to reviewing the methods already applied in AMC, we also address various research gaps and future trends and highlight potential methodologies from adjacent domains that could be transferred to AMC.

Computer-Aided Design is ubiquitous in todays world, as almost every manufactured object begins as a digital model across industries. At the same time, advances in 3D sensing have made point clouds a dominant form of raw 3D data. Recovering the CAD model of a physical object from its point cloud scan has two major applications: reverse engineering, where physical or hand-crafted prototypes need to be reconstructed automatically as editable digital models, and quality control, where recovering the CAD description of a manufactured object helps quantify and understand deviations introduced during the production process. Thus, converting unordered point clouds into structured CAD models is increasingly important for modern applications. Deep learning has enabled major progress in computer vision for both 2D and 3D data, and new datasets facilitate data-driven CAD reconstruction. Building on this foundation, we develop an end-to-end model that reconstructs CAD models from point clouds and introduce a segmentation approach that decomposes them into individual extrusions. These partial shapes increase data diversity, improving the generalization and robustness of deep learning models. Our strategy thereby provides a simple, yet effective way to increase reconstruction performance of deep learning models.

As additive manufacturing becomes more integrated into construction workflows, new demands are emerging for digital monitoring, quality control, and process control across the entire production chain. Rather than relying on conventional end-of-process inspection, this contribution presents a new, structured view of quality control for additive manufacturing in construction. The approach integrates sensing, geometric assessment, and robotic control throughout production and assembly. Four stages are distinguished: online, layer-wise, pre-assembly, and assembly. The combination of sensor systems, geometric processing, and control strategies across these stages enables early error detection, direct process feedback, and improved reliability in robotic construction. Particular attention is given to on-site assembly scenarios, where localization and registration to BIM/FIM are required to verify placement, alignment, and integration in digitally coordinated, multi-robot environments. The paper also addresses emerging challenges for mobile and in-situ fabrication, including print-while-drive operation and quality control under dynamic, moving-platform conditions. Consequently, digital monitoring and control are core components of scalable additive construction, not auxiliary functions.

The architecture, engineering and construction (AEC) industry is constantly evolving to meet the demand for sustainable and effective design and construction of the built environment. In the literature, two primary deposition techniques for large-scale 3D concrete printing (3DCP) have been described, namely extrusion-based (Contour Crafting-CC) and shotcrete 3D printing (SC3DP) methods. The deposition methods use a digitally controlled nozzle to print material layer by layer. The continuous flow of concrete material used to create the printed structure is called a filament or layer. As these filaments are the essential structure defining the printed object, the filaments' geometry quality control is crucial. This paper presents an automated procedure for quality control (QC) of filaments in extrusion-based and SC3DP printing methods. The paper also describes a workflow that is independent of the sensor used for data acquisition, such as a camera, a structured light system (SLS) or a terrestrial laser scanner (TLS). This method can be used with materials in either the fresh or cured state. Thus, it can be used for online and post-printing QC.

Accurate and efficient structural health monitoring of infrastructure objects such as bridges is a vital task, as many existing constructions have already reached or are approaching their planned service life. In this contribution, we address the question of the suitability of UAV-based monitoring for SHM, in particular focussing on the geometric deformation under load. Such an advanced technology is becoming increasingly popular due to its ability to decrease the cost and risk of tedious traditional inspection methods. To this end, we performed extensive tests employing an 18.5 m long research reinforced concrete bridge that can be exposed to a predefined load via ground anchors. Very high resolution image blocks have been captured before, during and after the application of controlled loads. From those images, the motion of distinct points on the bridge has been monitored, and in addition, dense image point clouds were computed to evaluate the performance of surface-based data acquisition. Moreover, a geodetic control network in stable regions is used as control information for bundle adjustment. We applied different sensing technologies in order to be able to judge the image-based deformation results: displacement transducers, tachometry and laser profiling. As a platform for the photogrammetric measurements, a multi-rotor UAV DJI Matrice 600 Pro was employed, equipped with two RTK-GNSS receivers. The mounted camera was a PhaseOne iXM-100 (100 MP) with an 80 mm lens. With a flying height of 30 m above the terrain, this resulted in a GSD of 1.3 mm, while a forward and sideward overlap of 80% was maintained. The comparison with reference data (displacement transducers) reveals a difference of less than 1 mm. We show that employing the introduced UAV-based monitoring approach, a full area-wide quantification of deformation is possible in contrast to classical point or profile measurements.

Geometric quality inspection is an essential process in digital construction that provides insight into the conformity of the fabricated components to their designed models. It is even more important and challenging in modern 3D concrete printing processes where the realization of complex and intricate objects is possible. Using the right sensors for data capture is one of the key factors in the success and reliability of inspection results. Geometric inspection after printing makes it possible to update the digital model, adjust the next production step, or reject the fabricated objects if the deviation exceeds tolerances. This research investigates three different approaches, namely Terrestrial Laser Scanning (TLS), Terrestrial Photogrammetry (TP), and hand-held Structured Light Scanning (SLS), for the quality inspection of two medium-sized digitally fabricated concrete components. We compared the results of the data captured by each sensor with the respective other two sensors using cloud-to-mesh (C2M) distances. In all cases, the Root Mean Square Error (RMSE) is less than 1mm which is acceptable for the majority of applications within the realm of digital construction. Considering the geometric performance and other parameters –such as time, cost, and flexibility–to name a few, we conclude that hand-held SLS is an optimal choice for geometric inspection of small to medium-sized objects.

Disaggregated data on housing and household economic conditions, particularly in developing countries, are often inaccessible to urban planners and local users, and conducting independent in-person surveys is prohibitively expensive. This impedes the development of precisely informed policies and targeted infrastructure investments, services, and incentives that are essential for fostering equitable and sustainable cities. Using Kigali and Musanze cities in Rwanda, this study introduces a novel deep learning-based approach that combines limited expert annotation, self-training, and instance image segmentation to generate building-level housing wealth data. Our experiment included state-of-the-art instance segmentation based on You Only Look Once (YOLO) and Masked-attention Mask Transformer (Mask2former) models. To check the validity of the predictions, over ten thousand samples from the official cadastre-based property taxation database were used to evaluate whether the observed patterns in actual property values align with the model’s predictions. Importantly, our method successfully detected approximately 70% of the existing buildings within their respective perceived wealth classes. Buildings predicted to belong to the high-wealth class exhibited mean property values 2.5 times higher in Kigali and 1.7 times higher in Musanze than those classified as low-wealth, thereby demonstrating the model’s ability to capture spatial patterns of housing wealth. The predicted maps reveal that housing wealth growth in urban cores parallels increased low-wealth housing in peri-urban areas, a likely result of gentrification, an insight that could inform more inclusive urban development strategies and support policies aimed at curbing urban sprawl. Our findings demonstrate that combining minimal expert labeling of economically meaningful visual features with self-training and multi-class instance segmentation offers an effective and rapid approach for generating precise planning data in contexts that lack detailed local census information. Moreover, while leveraging advanced deep networks, our methodology was designed to be simple and easily replicable.

Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study investigates whether leveraging multi-temporal data for crack segmentation can enhance segmentation quality. Therefore, we compare a Swin UNETR trained on multi-temporal data with a U-Net trained on mono-temporal data to assess the effect of temporal information compared with conventional single-epoch approaches. To this end, a multi-temporal dataset comprising 1356 images, each with 32 sequential crack propagation images, was created. After training the models, experiments were conducted to analyze their generalization ability, temporal consistency, and segmentation quality. The multi-temporal approach consistently outperformed its mono-temporal counterpart, achieving an IoU of 82.72% and a F1-score of 90.54%, representing a significant improvement over the mono-temporal model’s IoU of 76.69% and F1-score of 86.18%, despite requiring only half of the trainable parameters. The multi-temporal model also displayed a more consistent segmentation quality, with reduced noise and fewer errors. These results suggest that temporal information significantly improves the performance of segmentation models, offering a promising solution for improved crack identification and long-term monitoring of concrete structures, even with limited sequential data.

Abstract The Kenyan rift system is prone to deformation due to various geological processes and human activities, such as overexploitation of groundwater and exploitation of geothermal energy. Crustal deformation monitoring is essential for understanding the geodynamics of the rift and assessing potential hazards as the rift passes through densely populated areas. However, retrieving InSAR displacement measurements along the Kenya rift system is challenging due to the high variability in tropospheric delay caused by its location in the tropics and the significant topographic variations along the rift. Here, we leverage Sentinel-1 data to analyze both local and large-scale deformation in the rift using multi-temporal InSAR processing with an improved tropospheric correction method, which we previously proposed with a modification on variance covariance modeling. We provide evidence for the previously reported ground displacement of 5 $$-$$ - 7.5 cm southwest of the Suswa volcano as being a misidentification of a tropospheric delay, that was dominant on March 27, 2018. Moreover, we observe episodic ground displacement at Suswa and Longonot, attributable to magma movement. Our results indicate that Suswa experienced a 9 cm movement towards the satellite line of sight between 2018-2020, while Longonot moved 4 cm towards satellite line of sight between 2016-2018 and also underwent 3 cm displacement away from the satellite line of sight between 2019-2021. In addition, we observe land subsidence associated with geothermal exploitation in the range of 1 $$-$$ - 3.6 cm/yr in Olkaria as well as in multiple locations in Nairobi at a rate of up to 6 cm/yr, primarily attributed to groundwater overexploitation. Moreover, we detect undocumented ground displacement at the Chalbi salt flat and along the Elgeiyo escarpment, at rates of up to 4.8 cm/yr and 5.7 cm/yr, respectively. Long-wavelength, low-amplitude deformation at the Turkana depression aligned well with the current kinematics of the Kenyan rift system. We find that localized deformations, caused by magmatism and human activity, dominate the south segment of the rift, whereas large-scale low-amplitude deformations dominate the north segment. In summary, our results emphasize the importance of tropospheric delay correction in retrieving InSAR-derived displacement measurements along the Kenyan rift system. Graphical Abstract

We present egenioussBench, a visual localisation benchmark built on geospatial reference data: a city-scale airborne 3D mesh and a CityGML LoD2 model. This pairing reflects deployable mapping assets and supports true scalability beyond traditional SfM-based approaches. The query data comprise smartphone images with centimetre-accurate, map-independent ground truth obtained via PPK and GCP/CP-aided adjustment. From 2,709 images, we derive a non-co-visible subset by estimating the full co-visibility matrix from rendered depth and selecting a maximum independent set; the released data include a test split of 42 non-co-visible images with withheld ground truth and a validation split of 412 sequential images with poses, e.g. for training of pose regressors and self-validation. The benchmark features a public leaderboard evaluated with binning metrics at multiple pose-error thresholds alongside global statistics (median, RMSE, outlier ratio), ensuring fair, like-for-like comparison across mesh- and LoD2-based methods. Together, these design choices expose realistic cross-view and cross-domain challenges while providing a rigorous, scalable path for advancing large-scale visual localisation. We make the evaluation code and data availeable at https://github.com/fratopa/egenioussBench and https://www.egeniouss.eu/

The soil freeze-thaw (FT) state plays a significant role in cold ecosystems by influencing hydrology, geomorphology, ecology, thermodynamics, and soil chemistry. As climate change alters the frequency and timing of FT events, regions, such as the Tibetan Plateau and other subarctic permafrost zones, face significant environmental shifts, including land subsidence, altered hydrological processes, and carbon balance disturbances. This study aimed to detect frozen, thawed, and transition periods, with a particular emphasis on the identification of critical transition periods. A new approach called Percentile-based Freeze-Thaw Identification (PFTI) was applied to identify the three FT periodsusing C-band Synthetic Aperture Radar (SAR) data, specifically using Sentinel-1 VV and VH polarizations in both ascending and descending orbits. The PFTI approach is based on the Seasonal Threshold Approach (STA) with some modifications. We assessed the performance of PFTI against STA and validated it using the in-situ soil FT index. PFTI slightly improved the STA by 4% to 6% in detecting FT cycles over the Nagqu area of the Tibetan Plateau and Stordalen Mire in northern Sweden from 2017 to 2022. Moreover, PFTI demonstrated an accuracy of 94% without and 70% with transition periods in the Stordalen Mire, and 77% without and 50% with transition periods in the Nagqu area, as validated by in situ measurements. This study demonstrates the potential of PFTI on a monthly scale for detecting frozen, thawed, and transition periods on a regional scale to better monitor shifts due to the impacts of climate change in the most vulnerable regions.

3D Concrete printing requires much more elaborate quality control procedures compared to conventional concrete processing. Due to the various process steps, and the corresponding variation in material behaviour, time-, and length-scales, a single quality indicator and measurement technique (similar to the ‘slump test’ for traditional construction) cannot be selected. Instead, three families of quality indicators have been established: homogeneity during material production and deposition (quality variations), material evolution during printing (transient material behaviour), and macroscopic features and geometric conformity during printing and of the final object (geometry). For each family, quality assessment techniques which have been proven in other fields or for different applications, have been successfully transferred and adapted to the 3DCP process. In some cases, completely new methods have been developed. This paper aims to provide the state-of-the-art in such quality assessment methods, indicating high potential methods and research gaps across all scale levels of 3D concrete printing processes.

The project „Optical 3D Bridge Inspection”, which is part of DFG’s priority program “100+”, aims to capture surface geometry and damages of prestressed concrete bridges with high-resolution optical tools to assist the structural health monitoring process. In a multi-scale and multi-epoch approach, the building structure is recorded in total with UAV-based cameras in millimetre-resolution – extracting deformation and areas of interest where damages are visible – and in those hotspots, an even higher resolved image block and also a micrometre-resolution structured light scanner (SLS) capture is taken. Detected damages are compared between multiple epochs, monitoring their development. In an experiment, we demonstrated all measurements and their linking possibilities on a reinforced concrete plate under controlled load. The global image block showed that even the smallest cracks with a width of 0.05mm were visible in the images with a spatial resolution of 0.16mm per pixel. Also, the three-dimensional reconstruction based on the images was able to mirror the plate accurately in all epochs. However, it was shown that manually applied speckles reduced the noise drastically, compared to areas in which the surface only consisted of blank light concrete with only a few microfeatures. The underlying deformation was nevertheless accurately reconstructed in all areas. It is shown that the SLS can measure the width and detailed shape of a crack, which enabled us to track changes between multiple epochs of different load. With feature-based matching of photogrammetry and SLS results, we can combine the advantages of the fast global UAV-based image approach and the micrometer-resolution local SLS scans and use them to support each other. In this paper we report about those experiments in detail and analyze them. Based on that, we formulate a possible user story to apply both techniques to support structural health monitoring of bridges and decision-making in maintenance.

Container cranes are of key importance for maritime cargo transportation. The uninterrupted and all-day operation of these container cranes, which directly affects the efficiency of the port, necessitates the continuous inspection of these massive hoisting steel structures. Due to the large size of cranes, the current manual inspections performed by expert climbers are costly, risky, and time-consuming. This motivates further investigations on automated non-destructive approaches for the remote inspection of fatigue-prone parts of cranes. In this paper, we investigate the effectiveness of color space-based and deep learning-based approaches for separating the foreground crane parts from the whole image. Subsequently, three different ML-based algorithms (k-Nearest Neighbors, Random Forest, and Naive Bayes) are employed to detect the rust and repainting areas from detected foreground parts of the crane body. Qualitative and quantitative comparisons of the results of these approaches were conducted. While quantitative evaluation of pixel-based analysis reveals the superiority of the k-Nearest Neighbors algorithm in our experiments, the potential of Random Forest and Naive Bayes for region-based analysis of the defect is highlighted.

Augmented Reality (AR) offers new opportunities for Citizen Science (CS) projects regarding data visualization, data collection, and training of participants. Since limited research on the usage of AR in CS projects exists, an online survey is conducted in this study by reaching out to CS project managers to determine the extent of its current use. The survey can identify areas where CS project managers themselves see the greatest potential for AR in their projects and reasons that exist against the use of AR. A total of 53 CS project managers participated in the survey and shared their opinions and concerns. Of all participating CS projects, only three are currently using AR. However, 27 CS projects indicated that AR could be beneficial for their project. Especially projects with a geographic focus, in which participants are involved in the process of collecting spatial data, expressed this opinion. Particularly in the areas "data visualization" and "attraction/motivation of participants" the projects identified potential for AR. Arguments against the use of AR named by 23 CS projects include remote study areas, financial considerations, and the lack of a practical use case. This study shows initial trends regarding the use of AR in CS projects and highlights specific use cases for the application of AR.

Accurate and up-to-date building and road data are crucial for informed spatial planning. In developing regions in particular, major challenges arise due to the limited availability of these data, primarily as a result of the inherent inefficiency of traditional field-based surveys and manual data generation methods. Importantly, this limitation has prompted the exploration of alternative solutions, including the use of remote sensing machine learning-generated (RSML) datasets. Within the field of RSML datasets, a plethora of models have been proposed. However, these methods, evaluated in a research setting, may not translate perfectly to massive real-world applications, attributable to potential inaccuracies in unknown geographic spaces. The scepticism surrounding the usefulness of datasets generated by global models, owing to unguaranteed local accuracy, appears to be particularly concerning. As a consequence, rigorous evaluations of these datasets in local scenarios are essential for gaining insights into their usability. To address this concern, this study investigates the local accuracy of large RSML datasets. For this evaluation, we employed a dataset generated using models pre-trained on a variety of samples drawn from across the world and accessible from public repositories of open benchmark datasets. Subsequently, these models were fine-tuned with a limited set of local samples specific to Rwanda. In addition, the evaluation included Microsoft’s and Google’s global datasets. Using ResNet and Mask R‑CNN, we explored the performance variations of different building detection approaches: bottom-up, end-to-end, and their combination. For road extraction, we explored the approach of training multiple models on subsets representing different road types. Our testing dataset was carefully designed to be diverse, incorporating both easy and challenging scenes. It includes areas purposefully chosen for their high level of clutter, making it difficult to detect structures like buildings. This inclusion of complex scenarios alongside simpler ones allows us to thoroughly assess the robustness of DL-based detection models for handling diverse real-world conditions. In addition, buildings were evaluated using a polygon-wise comparison, while roads were assessed using network length-derived metrics. Our results showed a precision (P) of around 75% and a recall (R) of around 60% for the locally fine-tuned building model. This performance was achieved in three out of six testing sites and is considered the lowest limit needed for practical utility of RSML datasets, according to the literature. In contrast, comparable results were obtained in only one out of six sites for the Google and Microsoft datasets. Our locally fine-tuned road model achieved moderate success, meeting the minimum usability threshold in four out of six sites. In contrast, the Microsoft dataset performed well on all sites. In summary, our findings suggest improved performance in road extraction, relative to building extraction tasks. Moreover, we observed that a pipeline relying on a combination of bottom-up and top-down segmentation, while leveraging open global benchmark annotation dataset as well as a small number of samples for fine-tuning, can offer more accurate RSML datasets compared to an open global dataset. Our findings suggest that relying solely on aggregated accuracy metrics can be misleading. According to our evaluation, even city-level derived measures may not capture significant variations in performance within a city, such as lower accuracy in specific neighbourhoods. Overcoming the challenges of complex areas might benefit from exploring alternative approaches, including the integration of LiDAR data, UAV images, aerial images or using other network architectures.

This paper presents a method for filament extraction as a step in Shotcrete 3D printing (SC3DP) for quality control using Terrestrial Laser Scanning (TLS) after the printing process. The proposed approach involves comparing a Point Cloud (PC) generated from TLS data with the original design model using Cloud-to-Model (C2M) distance to obtain a coloured deviation map. This deviation map is then rasterized into an image with the same size as the object, with each pixel's colour representing the C2M distance. The method incorporates denoising techniques, such as bilateral filtering, and applies Canny edge detection to identify the filaments’ contour. Morphological operations are used to extract only the horizontal edges relevant to the planned printing path. To connect isolated edges, an algorithm based on the M-estimator sample consensus (MSAC) algorithm is employed to fit lines accurately. The proposed method achieves an average precision score of 76% in detecting printing filaments of a shotcrete wall. The results demonstrate a reliable quality control capability and the potential for early identification of manufacturing-related issues.

As part of the digitization of the AEC industry, the Digital Twin concept is becoming increasingly important. Originating in the manufacturing industry, the concept at its core involves a bidirectional coupling of the physical product and its digital counterpart with the aim of keeping the two in sync. Without appropriate capabilities to realize such synchronization, the concept always remained as an unattainable vision for the AEC industry. Adapting additive manufacturing (AM) for construction, however, creates unique opportunities to realize this vision by enabling automation in both directions, from digital to physical product and vice versa. As a fully automatable manufacturing method where robotic processes are typically controlled by the digital representation of the product, AM realizes the digital-to-physical link for this purpose. Conversely, based on the same digital representation of the product, the acquisition of the physical implementation of the manufacturing process can be automated, enabling the physical-to-digital connection. This paper uses three AM application scenarios to illustrate, on the one hand, the need for automating quality control and, on the other hand, to describe approaches for its realization. In particular, the benefits of synergy between automated quality control (QC) and fabrication information modeling (FIM) to form a digital-physical-digital loop are explored.

<p>The Kenyan Rift system hosts various forms of land use, including residential, commercial, and agricultural. In addition, the geology of the Kenyan Rift, the geodynamic setting of the quaternary volcanoes along the rift axis, and the high temperatures associated with the hot asthenosphere along the Kenyan Rift system are favourable for the occurrence of geothermal fields, some of which have already been harnessed for the generation of electricity. The interplay of human activities along the Kenyan Rift system can cause deformation, which is also prone to deformation due to geophysical activities such as volcanism and magmatism. In our study, we utilized both conventional and optimized multitemporal InSAR analyses based on the SBAS method to quantify human-induced deformation along the Kenyan Rift. By directly estimating the tropospheric delay from Sentinel-1 SAR data, the optimized approach can reduce errors in InSAR derived displacement measurements. Nairobi, located on the eastern flank of the Kenyan Rift, has experienced significant deformation caused by urbanization and the overexploitation of groundwater. A maximum subsidence rate of approximately 55 mm/yr. was observed in one of the eight deformation units that are mainly located in residential areas. Njoro town and Nakuru town industrial zone have also been shown to be undergoing land subsidence of approximately 20 mm/yr. and 10 mm/yr., respectively, both of which are associated with the overexploitation of groundwater resources. In addition, land subsidence in the range of 20 mm/yr was observed at several flower farms in Naivasha, which can also be attributed to the overexploitation of groundwater. At Olkaria, we observed land subsidence in the seven geothermal fields in the range of 22-50 mm/yr., we also observed at Menengai Crater Land subsidence and uplift of approximately 8 mm/yr. and 6 mm/yr. respectively. There is a significant deformation in the Kenyan Rift as a result of human activities, and these results indicate that InSAR can be used to monitor deformation in regions that were previously unmonitored due to the associated costs of using other geodetic monitoring techniques. Similarly, correct estimation of tropospheric delay in InSAR not only leads to better time-series displacement estimation with a more apparent temporal trend but also reveals subtle deforming regions that are otherwise obscured by tropospheric delay in the conventional method.</p>