5 DX Examples That Streamline Civil Engineering Survey Work! Boost Productivity with AI and IoT

In recent years, as the construction industry faces labor shortages and responses to work-style reforms, digital transformation (DX) has been rapidly advancing even in various survey tasks in the civil engineering field. By introducing AI, IoT, cloud, and smart sensors into tasks such as topographic surveying, geological surveys, environmental monitoring, traffic surveys, and underground structure exploration, efficiency, labor-saving, and improved safety are being realized. This article introduces five examples of DX utilization in civil engineering survey work. For each case, we explain the “background and challenges,” “introduced technologies,” “results and effects,” and “future prospects,” clarifying the objectives of adoption, specific technologies, and the effects obtained. At the end of the article, we also touch on use cases of simple surveying with LRTK using a smartphone and a high-precision GNSS receiver, and consider further potential for improving survey work efficiency.

1. Topographic Survey DX with Drones × 3D Surveying

Background and Challenges

Topographic surveying, essential for civil engineering works, was traditionally performed by surveyors on site using total stations and levels, with several people measuring points one by one to create topographic maps. On large sites, surveying could take several weeks, and work in mountainous areas or on steep slopes always involved danger. Because it required manpower and time and the density of measurement points was limited, understanding the entire site often relied on experience-based inference. Furthermore, amid a shortage of skilled personnel due to a declining birthrate and aging population, obtaining rapid and accurate topographic information with limited staff has become an issue.

Introduced Technologies

To address these challenges, 3D surveying using drones (UAVs) has emerged. High-resolution cameras or LiDAR laser scanners are mounted on small unmanned aircraft to automatically measure terrain from the air. By capturing numerous aerial photos with a drone and performing photogrammetry analysis, orthorectified images and detailed 3D terrain models can be generated. With LiDAR-equipped drones, high-density point cloud data can be obtained even on slopes covered with trees or in complex terrain. Acquired data can be processed automatically in the cloud for visualization and analysis without being physically present at the site. The Ministry of Land, Infrastructure, Transport and Tourism is also promoting drone surveying as part of “i-Construction,” and major construction companies have already been actively introducing it for road works and dam construction surveying.

Results and Effects

The impact of drone-based topographic survey DX is enormous. Survey time has been greatly shortened; wide-area surveys that used to take surveying teams weeks can now be completed in days or, in some cases, hours. For example, at a dam construction site, drone surveying without establishing control points obtained high-precision surface data in a short time. As a result, information necessary for design and construction planning can be consolidated quickly. In addition to efficiency gains, safety has improved. Drones can safely capture conditions remotely at cliff sites or disaster zones where human entry would be dangerous. Reducing high-altitude tasks and eliminating the need for workers to remain on site lowers occupational accident risks. The obtained 3D models are intuitive and easy to understand, smoothing explanations and consensus-building with clients. Photos and point cloud data can be used directly for progress management and as-built (deliverable) reporting, reducing the effort required to prepare reports. Furthermore, because cross-sections can be cut freely and distances and volumes measured on the acquired point cloud data, the need for additional re-measurements is reduced and productivity has dramatically increased.

Future Prospects

Drone × 3D surveying technology will continue to evolve and likely become the standard for survey tasks. In the future, autonomous flight and automatic analysis are expected to become commonplace, enabling “one-stop surveying” where a drone autonomously flies the specified survey area and completes data acquisition through model creation. If real-time capture and processing allow the generation of 3D models on site to immediately check terrain changes and construction progress, the boundary between surveying and construction management will be further reduced. Integrating the obtained data with BIM/CIM (construction management using 3D models) and automating reconciliation with design drawings can improve efficiency in plan revisions and as-built inspections. With the development of smaller, higher-performance sensors and more advanced cloud analysis, applications to continuous monitoring and infrastructure inspection will expand. Drones are expected to play an active role in maintenance of bridges and tunnels, contributing to labor-saving and sophistication of surveys. Topographic survey DX will continue to advance as a foundation for future unmanned construction and smart construction technologies.

2. Geological Survey DX Evolved by Sensors and AI

Background and Challenges

Geological surveys for civil planning and disaster prevention require understanding ground composition and stability. Traditionally, data were collected fragmentarily by boring surveys and sampling, and experts inferred stratum composition. However, point-based surveys cannot fully capture wide-area subsurface structures, risking overlooking unknown discontinuities or weak zones. Moreover, field inspection by human staff is indispensable to identify potential landslide or debris flow locations associated with earthquakes and heavy rain, and such work demands great effort in mountainous areas. Reliance on veteran technicians' knowledge led to personalization and inefficiency.

Introduced Technologies

In this field, DX combining IoT sensor data collection and AI analysis is progressing. One example is applying AI to microtremor exploration, which estimates underground structures by measuring tiny, continuous vibrations of the ground. By installing multiple vibration sensors and collecting ground motion data, machine learning analysis can automatically generate 3D subsurface structure models that were previously interpretable only by experts. In practice, a surveying company succeeded in visualizing underground geological structures and creating 3D ground models understandable to the general public using a microtremor exploration device and proprietary AI. Recently, disaster-prevention IoT sensors for real-time ground monitoring have also been introduced. Systems use digital inclinometers (tilt sensors) installed on slopes to detect slight displacements and transmit the data wirelessly to the cloud. By detecting early signs of slope movement during heavy rain and issuing alerts, anomalies can be sensed without human patrols. The vast data from these sensors are centrally managed in the cloud, and AI learns abnormal patterns to aid in extracting hazardous spots and predicting collapses.

Results and Effects

DX introduction in geological surveys has led to a dramatic improvement in the accuracy and efficiency of ground information acquisition. AI-generated 3D ground models show the subsurface conditions in three dimensions that were not visible on traditional boring logs or plans. This makes it intuitive to analyze, for example, how weak strata are distributed within a slope or which areas are high-risk when compared with past disaster history. The ability to extract hazardous ground locations from data without relying solely on expert experience is a major achievement. In one study, technologies were developed that combine satellite-derived topographic data and historical disaster records with AI-analyzed subsurface models to automatically identify likely landslide-prone areas. Also, the use of disaster-prevention IoT sensors enabled a continuous monitoring system, significantly reducing the need for human patrols by municipal staff. Even during nighttime or heavy rain, slope changes can be detected remotely, enabling quick decisions on resident evacuation or preemptive road closures. Field trials showed that multiple inclinometers installed on slopes detected small landslide occurrences during heavy rain, and later on-site verification confirmed that collapses had indeed occurred—capturing early deformations that would likely be missed by human patrols. This contributed to early warning and damage mitigation. Overall, sensor + AI geological survey DX enables objective ground evaluation based on data, contributing to reduced labor and improved reliability.

Future Prospects

Future prospects for geological survey DX include further data integration and improved predictive accuracy. Platforms that combine geological sensors, drone-derived topographic data, satellite remote sensing information, and meteorological data to comprehensively capture ground condition changes will be built. For example, AI could simultaneously analyze surface changes (satellite imagery) and subsurface changes (sensor data) to predict the probability of landslide occurrence in real time. The spread of low-cost sensors and LPWA communications (low-power wide-area) will expand smart monitoring to remote areas and small-to-medium sites that were previously difficult to measure. Municipalities could deploy sensor networks around landslide-prone zones and aging infrastructure to realize preventive maintenance via continuous monitoring × AI analysis. Additionally, leveraging vast ground and disaster data accumulated in the cloud will enable higher-accuracy ground risk assessment services and the provision of 3D geological maps. Geological survey DX will continue to develop as an essential foundational technology for disaster prevention/mitigation and infrastructure maintenance.

3. Hydrology and Meteorological Observation DX: Strengthening Disaster Response with IoT Multi-Point Monitoring

Background and Challenges

Observing river water levels, rainfall, and meteorological data is crucial for disaster prevention and infrastructure operation. Traditionally, small and medium rivers often lacked hydrological observation stations, and many municipalities relied on staff patrolling sites for visual checks. During heavy rain, municipal staff sometimes risked going to check river conditions to make decisions. However, human patrols lack real-time capabilities and spatial coverage, and simultaneous, widespread heavy rainfall at night can overwhelm response capacity. With the recent increase in typhoons and localized heavy rain, the burden on municipal staff has grown and the lack of rapid evacuation decision materials has become a serious issue. Similarly, on construction sites, meteorological observations often depended on workers checking rainfall manually, making objective stop-work decisions and efficient schedule replanning difficult.

Introduced Technologies

To address these issues, IoT-based multi-point automatic hydrological and meteorological observation systems have been introduced nationwide. Specifically, sensors such as river water level gauges, rain gauges, and anemometers are installed at multiple hazardous or wide-area locations, and data are aggregated to the cloud via wireless communication. Using LPWA (Low Power Wide Area) network technologies (such as Sigfox or LoRaWAN) that enable low-power, long-distance communication, sensor networks can be economically deployed in mountainous areas and small rivers with limited power and communication infrastructure. In one municipal field trial, 13 ultrasonic small IoT water level gauges were installed at five small and medium rivers previously affected by flooding, constructing a system that transmits water level data to the cloud at 5-minute intervals. Staff can check water levels for each river in real time from municipal PCs or smartphones via a web app. The system also automatically sends alerts when certain thresholds are exceeded, enabling immediate detection of anomalies even at night. Private companies have also launched IoT meteorological observation services. Rain and wind sensors installed on sites send data via LTE to a cloud-based unified management system. These services are used for construction site safety management, enabling centralized monitoring of weather information for multiple sites from the office and email notifications to stakeholders when thresholds are exceeded. For river monitoring, there are also cases combining remote cameras and AI. Network cameras installed on bridges have their footage analyzed by AI to detect signs of rising water or flooding, allowing risk assessment even where water level gauges are not available.

Results and Effects

Hydrology and meteorological DX via IoT multi-point monitoring has made real-time and spatially comprehensive disaster information acquisition possible. Even small rivers that previously had few water level gauges can now be monitored 24/7 through sensor networks, allowing continuous tracking during heavy rain. As a result, municipal staff can monitor rivers safely from indoors without running around at night, and earlier evacuation advisories can be issued in areas with many vulnerable residents. One town reported that after installing IoT water level gauges, “onsite patrols became unnecessary, reducing staff burden and speeding up decision-making.” Since data are recorded in the cloud, temporal change analysis is also easy. Analyses such as where water levels began to rise first and when the peak occurred help plan countermeasures for future floods and improve hazard map accuracy. Initiatives also share observation data in real time with residents and other organizations; publishing water level and rainfall information on the internet allows residents to make their own evacuation decisions and upstream/downstream municipalities to coordinate responses, supporting wide-area disaster prevention. On construction sites, IoT meteorological observation enables work stop/restart decisions based on objective data. For example, setting a rule such as “automatically alert and suspend work if rainfall exceeds a certain threshold for one hour” enforces safety management without relying on human judgement. This has led to fewer near-miss incidents and optimized schedules. Overall, IoT-based hydrology and meteorological observation DX strengthens disaster response capability and operational efficiency, providing reassurance and leeway to stakeholders.

Future Prospects

Going forward, further utilization of observation data and automated control will advance. For example, systems that use multi-point water level data analyzed by AI to predict river flooding risk and warn municipal disaster staff in advance are anticipated. Linking water gates and drainage pumps with IoT observation systems to perform automatic gate operation and drainage based on sensor information is also becoming technically feasible. Indeed, some platform providers are trialing the use of water level sensor information via interfaces to support remote decisions about water gate operation. In meteorology, the construction of mesh sensor networks by private meteorological companies and research institutions is expected. Deploying many inexpensive temperature, humidity, and rainfall sensors in urban areas will generate high-resolution meteorological data useful for monitoring localized heavy rain and urban heat island effects. In the future, big-data analysis of these vast measured datasets can enable AI to detect early signs of abnormal weather and contribute to improving meteorological models. Public dissemination of IoT-collected disaster information via smartphone apps and the like is also important. If such information is delivered in real time so that anyone can grasp local risk on the spot, autonomous evacuation behavior during disasters will be promoted. Hydrology and meteorological observation DX will move beyond merely accumulating data to a phase of smart utilization, becoming a powerful tool for disaster prevention/mitigation and safe social infrastructure operation.

4. Traffic Survey DX: 24/7 Automatic Counting with AI Cameras

Background and Challenges

Traffic surveys on roads and in urban areas provide basic data essential for urban planning, site selection of commercial facilities, and traffic safety measures. Traditionally, these surveys were mainly conducted by surveyors stationed at sites, manually counting passing vehicles and pedestrians with hand counters. Because personnel were placed at specific intersections during limited time periods such as morning and evening rush hours, the data obtained were temporally and spatially limited. Counting by human observation allowed room for mistakes and subjectivity, challenging data accuracy. Additionally, cost burdens such as personnel and traffic guidance arrangements were large, making frequent surveys difficult. Consequently, detailed data such as “pedestrian trends in front of a particular store” or “annual traffic volumes including seasonal variations” were not usually obtainable, making fine-grained urban insights and long-term analysis difficult.

Introduced Technologies

Recently, DX using AI-equipped cameras and IoT communication for traffic surveys has progressed. Specifically, smart cameras installed on streets capture footage that is analyzed by AI in real time to automatically count people and vehicles. A startup developed a traffic survey service using an AI image recognition algorithm called “IDEA.” It installs edge-processing IoT cameras (cameras with built-in small computers and communication modules) under eaves and such; the camera analyzes the number of people and vehicles and attribute information of passersby (estimated age groups and gender, etc.) within the camera itself, and only the result data are transmitted to the cloud. Because video is not transmitted, communication bandwidth is low and privacy is protected. On the cloud side, a dashboard stores and visualizes collected data, allowing users to view real-time traffic graphs and heat maps in a browser. With such solutions, once a camera is installed, continuous traffic data can be obtained 24 hours a day, 365 days a year. Local governments have already begun adopting this; for example, Mie Prefecture started installing AI cameras at 10 major roads from fiscal 2021 to continuously measure traffic and publish the data on the prefectural website. Advances in AI now enable higher-level analyses such as vehicle classification (passenger car vs. large vehicle, bicycle vs. pedestrian), speed estimation, and measurement of queue length (congestion length). In the future, integration with pavement sensors or communication beacons could further develop into a real-time traffic monitoring system.

Results and Effects

Traffic survey DX using AI cameras has realized objective data collection without relying on manpower. A major effect is the dramatic improvement in data coverage and continuity. Where only fragmentary information on specific dates and locations was previously available, AI cameras can collect data every day year-round and regardless of day or night. This accumulates insights previously unattainable, such as patterns of traffic variation by day of week and season and detailed hourly trends. In one case where an AI counting camera was installed in front of a commercial building, the owner discovered new findings like “there are many pedestrians on weekday afternoons but surprisingly few on weekends” and “pedestrian numbers drop sharply in rainy weather while vehicle traffic increases.” Moreover, continuous measurement at pinpoint locations that were difficult with human surveys has become easy. Instead of being limited to major intersections, AI cameras can be placed at arbitrary locations to, for example, measure long-term foot traffic in front of a vacant storefront being considered for a new store and use that for trade-area analysis. Data accuracy has also stabilized; AI counts using consistent logic eliminate human variability and oversight. Traffic data judged by the same standard 24/7/365 are unprecedented and are valuable information made possible by DX. As a result, personnel cost reductions and simplification of survey processes have been achieved. Previously, each survey required mobilizing several people and preparing equipment for tallying and aggregation, but once equipment is installed, cloud-based automatic aggregation and graphing complete the work. Staff only need to view the data, resulting in substantial labor savings. Furthermore, analyzing accumulated data enables new policy planning. Combining data from multiple AI cameras to map people and vehicle flows across an area can be applied to event attendance forecasting or optimizing public transport allocation, advancing data-driven urban planning.

Future Prospects

Traffic survey DX will evolve into a core technology for smart cities and traffic management. Data obtained from AI cameras are expected to be applied in real time to traffic signal control and congestion mitigation measures. For example, “smart traffic signals” that have been trialed in some municipalities use camera-detected traffic volumes for AI to automatically adjust signal cycles, aiming to reduce peak congestion and improve traffic flow while cutting CO₂ emissions. When autonomous vehicles become widespread, roadside AI camera data will also be an important data source for vehicle-to-vehicle and vehicle-to-infrastructure communication, providing real-time traffic flow data to vehicles for route optimization and driving assistance. Including analysis of pedestrian flows can contribute to crime prevention and generating urban vibrancy. In a pilot at a tourist destination, AI cameras in town measured pedestrian numbers and attributes, and the data were used for marketing. With privacy considerations and anonymization, numeric pedestrian data can evaluate event impact and shopping street vitality. Technically, further advances in AI image recognition alongside fusion with acoustic sensors and radar will enable all-weather operation. If traffic can be reliably measured at night and in bad weather, a more dependable data foundation will be built. Traffic survey DX will not only improve survey efficiency but will be an indispensable element in building digital twins of cities (virtual reproductions of urban spaces), greatly changing urban planning and mobility management.

5. Underground Structure Detection DX: Visualizing Buried Objects with Ground-Penetrating Radar and GPS

Background and Challenges

Various buried utilities (underground structures) such as water pipes, gas pipelines, and communication cables exist beneath roads and underground spaces. Pre-construction detection of underground conditions—“buried-object exploration”—is essential for safely performing civil engineering and excavation works. However, traditionally this work relied on skilled technicians performing manual operations using ground-penetrating radar (GPR) and metal detectors. Even when scanning the surface with GPR equipment, interpreting raw waveform data to determine the location and depth of pipes required advanced experience, and result variability was a problem. Old as-built drawings are often inaccurate, and accidents from damaging pipes due to exploration misses occur frequently. Also, survey results were typically recorded only with sketches on paper and were not effectively utilized as future assets. Inadequate visualization and recording of underground structures increased construction risks and costs.

Introduced Technologies

DX-based underground structure detection solutions have emerged. Modern GPR equipment comes in user-friendly forms such as small cart types and is integrated with GPS/GNSS receivers and IMUs (inertial measurement units). For example, a manufacturer’s “smart GPR” incorporates a high-sensitivity radar and high-precision GNSS to automatically acquire location information of underground buried objects just by scanning the surface. Operation is simple: a single scan run over the survey area maps detected underground pipes on the display in real time. Acquired data can be uploaded to the cloud for storage and analysis and shared and used as a digital 3D buried-object map. AI image recognition has also been introduced. AI performs real-time analysis of radar survey data and indicates, for example, that “this reflection is likely a water pipe,” enabling non-experts to infer hidden underground objects to some degree. Additionally, comprehensive construction consulting firms have started offering services that 3D-model gas pipelines and water/sewer pipes buried under roads. By combining high-precision survey instruments, existing drawings, and terrestrial laser scanners, subsurface structures are fully measured and converted into 3D data; operations began in some regions in 2021. These DX technologies have rapidly advanced visualization of the underground.

Results and Effects

The primary effects of underground structure detection DX are dramatic improvements in construction safety and efficiency. Using smart GPR equipment, buried-object exploration that used to take experts half a day can now be completed quickly by a single worker. With GNSS linkage, the coordinates of survey results are accurately recorded on maps, greatly reducing omissions and recording errors. This prevents accidental damage to pipes and cables during excavation and eliminates unnecessary costs for rework and restoration. On sites where exploration DX was adopted, staff reported that “because we knew the positions of buried objects in advance, we could accurately adjust placement during construction” and “we were able to proceed with excavation with confidence.” Furthermore, data assetization is a major benefit. Previously, survey outcomes were left in individual notebooks or paper drawings, but DX enables digital data sharing within and outside the organization. The resulting 3D buried-object maps can be used for future renovation work and maintenance, eliminating repeated surveys at the same locations. If a database of underground structures is maintained and appended with records of new installations or relocations, building a citywide underground infrastructure ledger becomes feasible. If stakeholders including administrations share data, the underground structures at a given site can be referenced immediately at the time of roadwork procurement, improving design and estimating accuracy. For instance, in the aforementioned 3D model services, bid estimates presented at tender were reported to be more accurate, reducing estimation errors for excavation volume and construction period. Overall, underground structure detection DX reduces safety and economic risks and brings efficiency and quality improvement throughout the lifecycle from planning and construction to maintenance.

Future Prospects

In the future, underground visualization technologies will become more advanced and an essential element of infrastructure management. One trend is sensor advancement: in addition to current electromagnetic radar, future ultra-high-sensitivity technologies such as quantum magnetic sensors are expected to detect deeper buried objects and small-diameter pipes. Research combining multiple sensor modalities to correct for geological influences and increase accuracy will also progress. The second trend is data integration. Integrating various 3D data of buried objects on GIS (geographic information systems) and overlaying them with aboveground terrain and building information will build integrated aboveground-and-underground digital twins, allowing instant comprehension of a city’s underground spaces and aiding planning for new facilities and assessing impacts on buried structures during disasters. AR (augmented reality) integration is also envisioned: pointing a tablet or smart glasses to a location could overlay the positions of pipes and structures buried there on the screen. If workers at construction sites can intuitively confirm hidden hazardous spots using AR, safety and work efficiency will dramatically improve. Administratively, requiring pre-excavation subterranean surveys and data submission as a standard process for construction procurement may spread. In some countries, subterranean surveys before excavation are mandated, and as DX technologies spread domestically, regulatory frameworks may evolve. Underground structure detection DX, though low-profile, will increasingly support the foundation of urban infrastructure and see broader development and utilization.

Conclusion: New Technologies Supporting On-Site DX and the Potential of Simple Surveying with LRTK

Above, we introduced five DX examples that contribute to streamlining civil engineering survey work. Each case leverages digital technologies such as AI, IoT, and cloud to achieve labor savings and accuracy improvements that conventional methods could not. While these DX efforts are focused on specific fields, a common key point is “accurately and easily acquiring, sharing, and utilizing on-site conditions as data.” Finally, as a technology that embodies this idea, we close by introducing the potential of simple surveying with LRTK (smartphone × high-precision GNSS receiver).

LRTK is a cutting-edge solution that makes real-time kinematic (RTK) GNSS positioning technology available on smartphones. By attaching a pocket-size high-precision GNSS receiver to a smartphone or tablet and launching a dedicated app, on-site positioning and 3D scanning are designed to be easily performed by anyone. Specifically, while the smartphone camera (and in some cases built-in LiDAR) captures surrounding photos and point clouds, GNSS provides real-time positioning, enabling generation on site of site data with coordinates that have centimeter-level accuracy (half-inch accuracy). For example, for a simple survey of a small plot, what used to require a surveyor to observe many points and create drawings can now be captured as point cloud data of the surrounding terrain and structures simply by walking around the site with an LRTK device. Because the obtained point clouds are assigned geodetic coordinates, overlaying them later in GIS or CAD yields minimal misalignment, allowing precise on-site verification. Since distances and heights can be measured on the point cloud data without using a tape measure at the site, additional re-measurements and revisits become unnecessary, directly contributing to labor savings.

A major advantage of LRTK is that such high-precision surveying can be performed easily by one person. Traditional RTK-GNSS surveying required setting up base stations and specialized knowledge to operate surveying equipment, but LRTK automates difficult settings in the app so the user only needs to walk with a smartphone to complete the work. The lightweight device allows field technicians to carry it daily and use it spontaneously when needed. It is useful for on-site verification of data obtained by drone surveys or underground exploration mentioned above. Survey results that are hard to interpret on paper drawings become obvious when a point cloud model is displayed on a smartphone and compared on site. If additional measurements are needed on site, LRTK can immediately obtain coordinate values and share the data via the cloud for flexible response. Accuracy is high—comparable to public surveying control points at about 1-2 cm (0.4-0.8 in)—and practical as an aid for construction management and as-built verification. Furthermore, LRTK includes functions to upload acquired data to the cloud for automatic 3D model generation and analysis, reducing post-office work time. Overall, LRTK is a revolutionary tool that enables “anyone, anywhere, immediately” to obtain high-precision surveying and positioning information, greatly streamlining coordinate acquisition and on-site verification in civil engineering surveys.

As shown, DX in civil engineering survey work is progressing on many fronts, but the important point is to connect these technologies for overall optimization rather than treating them as one-off measures. Combining drones, IoT sensors, AI analysis, and portable surveying technologies like LRTK can seamlessly connect the entire process from survey planning to data acquisition, analysis, and on-site feedback. By delegating repetitive tasks to digital technologies, technicians can focus on higher-value decision-making and creative work. DX-driven streamlining and labor-saving of survey operations will be key in increasingly important fields such as infrastructure maintenance and disaster response. Please use the cases and technologies introduced in this article as hints to consider DX measures your company or organization should undertake. By skillfully adopting cutting-edge technologies, the world of civil engineering surveys can continue to achieve both productivity improvements and quality enhancement.