Manual Survey Methods Research Articles

High-speed camera system for efficient monitoring of invasive plant species along roadways

Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent to roadways, considering that roadways represent lengthy corridors through which invasive species can propagate. Traditional manual survey methods for monitoring invasive plants are labor-intensive and limited in coverage. This paper introduces a high-speed camera system, named CamAlien, designed to be mounted on vehicles for efficient invasive plant species monitoring along roadways. The camera system captures high-quality images at rapid intervals, to monitor the full roadside when following traffic speed. The system utilizes a global shutter sensor to reduce distortion and geotagging for precise localistion. The camera system makes it possible to collect extensive data sets, which can be used for a digital library of the invasive species and their locations, but also subsequent training of machine learning algorithms for automated species recognition.

A Study on the Automation of Fish Species Recognition and Body Length Measurement System

The rapid depletion of fishery resources has led to the global implementation of Total Allowable Catch (TAC) systems. However, the current manual survey methods employed by land-based inspectors show limitations in accuracy and efficiency. This study proposes an automated system for fish species recognition and body length measurement, utilizing the RT-DETR (Real-Time Detection Transformer) model and ARCore technology to address these issues. The proposed system employs smartphone Time of Flight (ToF) functionality to measure object distance and automatically calculates the weight of 11 TAC-managed fish species by measuring their body length and height. Experimental results reveal that the RT-DETR-x model outperformed the YOLOv8x model by achieving an average mAP50 value 2.3% higher, with a mean recognition accuracy of 96.5% across the 11 species. Furthermore, the ARCore-based length measurement technique exhibited over 95% accuracy for all species. This system is expected to minimize data omissions and streamline labor-intensive processes, thereby contributing to the efficient operation of the TAC system and sustainable management of fishery resources. The study presents an innovative approach that significantly enhances the accuracy and efficiency of fishery resource management, providing a crucial technological foundation for the advancement of future fisheries management policies.

Analysis of Characteristics and Service Level of Pedestrian Facilities: Case Study of Ubud Tourist Destination, Gianyar Regency, Bali Province, Indonesia

The purpose of this study was to analyze the characteristics of sidewalks and pedestrians, the level of service and the type of pedestrian crossing facilities that are appropriate on Jalan Raya Ubud. Primary data collection using manual survey methods, including road geometric data, pedestrian volume, pedestrian speed, volume of road crossers and vehicles. Secondary data collection use literature study methods which include the location maps and population. Data analysis based on the guidelines of the Ministry of Public Works (2014). Based on the results of the analysis, the average width of the North and South sidewalks is 1.4 m with an average effective width of the North sidewalk is 0.9 m, while on the South sidewalk the average effective width of the sidewalk is 0.95 m. The largest pedestrian flow on the North and South Sidewalks on weekdays and holidays occurs in the afternoon with a total of 520 p/h with an average speed of 4.32 km/h, a density of 12 p/100 m2 and 8 m2/p pedestrian space for the North sidewalk, and the South sidewalk with a flow of 389 p/ h with an average speed of 3.72 km/h, a density of 10 p/100 m2, and 10 m2/p pedestrian space. On holidays, the flow is 744 p/ h with an average speed of 2.63 km/ h, a density of 28 p/100 m2 , and a pedestrian space of 4 m2 /org for the North sidewalk. While the South sidewalk has a flow of 351 org / hour with an average speed of 4.06 km / hour, a density of 9 org / 100 m2 , and a space of 12 m2 /org. The level of service of the North and South sidewalks on weekdays is included in the “B” category. The level of service of the North sidewalk on holidays is in the “C” category and the South sidewalk is in the “B” category.

A 10-m scale chemical industrial parks map along the Yangtze River in 2021 based on machine learning

Strengthening industrial pollution control in the Yangtze River is a fundamental national policy of China. There is a lack of detailed distribution of chemical industrial parks (CIPs). This Study utilized random forest (RF) and active learning to generate the distribution map of CIPs along the Yangtze River at 10-m resolution. Based on Sentinel-2 imagery, spectral and texture features are extracted. Combined with the Points of Interest (POI), a multidimensional feature space is constructed. By employing partitioned training, classification of CIPs map is achieved on Google Earth Engine (GEE). Technical validation along the entire Yangtze River demonstrates a model accuracy of 80%. Compared to traditional manual survey methods, this approach saves significant time and economic costs while also being timelier. As the first publicly available CIPs map within a 5-km range along the Yangtze River, this research will provide a scientific basis for the fine governance of chemical industries in the region. Additionally, it offers a model guide for the accurate identification of the chemical industry.

High-speed camera system for efficient monitoring of invasive plant species along roadways.

Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent to roadways, considering that roadways represent lengthy corridors through which invasive species can propagate. Traditional manual survey methods for monitoring invasive plants are labor-intensive and limited in coverage. This paper introduces a high-speed camera system, named CamAlien, designed to be mounted on vehicles for efficient invasive plant species monitoring along roadways. The camera system captures high-quality images at rapid intervals, to monitor the full roadside when following traffic speed. The system utilizes a global shutter sensor to reduce distortion and geotagging for precise localistion. The camera system makes it possible to collect extensive data sets, which can be used for a digital library of the invasive species and their locations, but also subsequent training of machine learning algorithms for automated species recognition.

Drone photogrammetry as a tool for modern conflict archaeology: a case study of a Second World War armament bunker in Bavaria

ABSTRACT Within this paper, a case study on the application of drone photogrammetry at the Second World War armament bunker Weingut I is presented. The aim of the project was the mapping of the preserved remains in advance to the installation of a memorial site. In particular, the preserved concrete arch and one of the ventilation shafts were mapped by this method in high quality. Therefore, even small details of the building structure as well as several damage spots needing remediation became visible. One important advantage of drone prospecting is an enhanced occupational safety and a cost and time reduction as no further manual resources are necessary. In areas without finalized clearing for Unexploded Ordnance, other manual survey method would be especially dangerous. By comparing the photogrammetric model of the bunker Weingut I with corresponding ALS Digital Surface Models (DSM) of varying resolution, the increase in information is shown.

Analisis Perbandingan Nilai Kondisi Jalan Menggunakan Metode SDI dan IRI Dari Alat Survei Roadroid

Good road infrastructure plays an important role in the economic development of a city. Therefore, the government must maintain and improve road conditions yearly to help attract investment and ultimately lead to overall economic growth. Presently, road condition surveys are conducted and assessed using the manual method with the Surface Distress Index (SDI), which is time-consuming and expensive due to the large number of access roads. In order to address this issue, a fast and cost-effective alternative survey approach is required, such as the International Roughness Index (IRI) method using the Roadroid application. Roadroid is an application installed on a smartphone and then attached to a car's windshield to record road conditions while the car is in motion. This study aims to compare the use of the Roadroid application with the manual survey method in terms of time and cost efficiency in identifying road conditions in the city of Palembang, which has 1204 roads. In this study, the Roadroid application results is compared to the road condition assessment results from the SDI methods. The results showed that the time required to obtain road conditions with manual survey and Roadroid application was 37.313 minutes and 4.123 minutes, respectively. The cost required to obtain road conditions was IDR 290.937.000 and IDR 68.006.000 for manual surveys and using the Roadroid application, respectively. Furthermore, the comparison of road condition assessment results between the SDI and IRI methods showed that they have average mean differences of 24.86%, so it can be concluded that the road conditions obtained in the IRI method using Roadroid have high accuracy with the conditions in the SDI method. From the results, it can be concluded that using the Roadroid application to obtain road conditions is more effective and efficient than manual surveys.

Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm

The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 104/hm2 and 3.37%, respectively, and for YFM4, 13.65 × 104/hm2 and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation.

An intelligent monitoring system of diseases and pests on rice canopy.

Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases and pests on rice canopy, a web client and a server. Each camera of the system can collect rice images in about 310 m2 of paddy fields. An improved model YOLO-Diseases and Pests Detection (YOLO-DPD) was proposed to detect three lesions of Cnaphalocrocis medinalis, Chilo suppressalis, and Ustilaginoidea virens on rice canopy. The residual feature augmentation method was used to narrow the semantic gap between different scale features of rice disease and pest images. The convolution block attention module was added into the backbone network to enhance the regional disease and pest features for suppressing the background noises. Our experiments demonstrated that the improved model YOLO-DPD could detect three species of disease and pest lesions on rice canopy at different image scales with an average precision of 92.24, 87.35 and 90.74%, respectively, and a mean average precision of 90.11%. Compared to RetinaNet, Faster R-CNN and Yolov4 models, the mean average precision of YOLO-DPD increased by 18.20, 6.98, 6.10%, respectively. The average detection time of each image is 47 ms. Our system has the advantages of unattended operation, high detection precision, objective results, and data traceability.

Application of airborne lidar technology in transmission line survey

In the survey of transmission line, the traditional aerial survey method is difficult to get the surface elevation through the vegetation, corn field and other surface attachments, while the manual survey method has the disadvantages of low efficiency and high cost in mountainous and hilly areas. In this paper, the airborne lidar surveying and mapping technology is used, combined with a 110kV transmission line engineering survey example, generating high-precision surface DEM based on the point cloud classification results and extracting the single wood information based on the point cloud to accurately count the amount of tree felling. A panoramic simulation line corridor engineering geographic information system is established, which provides technical support for the route scheme optimization and plane section extraction, and is applied to the project. It is of practical significance to evaluate the effect.

A counting method for the number of Sternolophus rufipes and Hydrochara affinis in a noisy trap image

Abstract A quantitative survey of water scavenger beetles Sternolophus rufipes and Hydrochara affinis in paddy fields is essential not only for evaluating the impact of climate change on ecosystems but also for quantifying the stability of paddy fields. Many researchers classify insects in insect traps visually and manually count the number of individuals in each species. This manual survey method is time-consuming, fatiguing, and tedious. In this paper, we present a simple method to classify and count beetles in noisy trap images. The proposed method uses the beetles' body size and spots made by the light reflecting off the backs of beetles. We verify the method using images of beetles attached to the insect trap. The results demonstrate that the number of individuals in each species as counted by the proposed method and the manually counted number are statistically identical, which means that our method is sufficient to replace the existing manual counting method. Additionally, we briefly discuss the limitations of this counting method and ideas that could complement them.

Performance evaluation of automatic vis-à-vis manual topographic survey for precision land levelling

Laser land levelling has contributed immensely to irrigation water saving to cope with declining ground water tables in South Asia. A topographic survey of a field is a pre-requisite to achieve high accuracy in land levelling. Presently, manual topographic surveys are conducted before using a laser land leveller in South Asia, which is cumbersome, time consuming and requires substantial operator skill. A novel sensor-based automatic topographic survey is potentially more precise and cost-effective. Therefore, a field study was conducted to compare the efficiency of a tractor operated sensor-based automatic survey system attached to a laser leveller compared with the conventional manual topographic survey. The automatic survey system consisted of automatic functioning of the laser operated power mast and scraper. The automatic survey method significantly reduced the earthwork and levelness index by 74.0 and 75.4% compared with 61.5 and 62.1% for manual topographic survey, respectively. The automatic survey improved the land uniformity coefficient by 78.4% compared with 42.4% for the manual survey. Average fuel consumption and field capacity were 56.5% and 48% higher for automatic survey compared to manual topographic survey method. The average survey cost was 38.2% lower for automatic survey compared with manual survey. The field capacity of the laser leveller (including the time taken for field survey) was significantly higher by 53% for automatic survey than manual survey. The total cost of operation of automatic survey method was 30.1% lower compared with conventional manual survey. Based on the results from this study, the newly developed automatic survey system is superior to conventional manual survey method in terms of precision in land levelling and economic gains.

TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

AbstractAutonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system—TrackerBots—to track and localize multiple radio‐tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio‐collars commonly used in the field is exploited to realize a low‐cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on‐board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real‐time and online approach through both extensive simulations and field experiments with five VHF radio‐tags on a grassland plain.

Automatic pavement defect detection using 3D laser profiling technology

Abstract Asphalt pavement defects e.g. cracks, potholes, rutting, often cause significant safety and economic problems, thus, to automatic detect these defects is vital for pavement maintaining and management. The fact that 3D defect detection methods is superior to traditional 2D methods and manual survey methods in terms of accuracy and comprehensiveness has been widely recognized. Based on 3D laser scanning pavement data, an automatic defect detection method is proposed to detect pavement cracks and pavement deformation defects information simultaneously in this paper. Specifically, a sparse processing algorithm for 3D pavement profiles is first designed to extract crack candidate points and deformations support points, these processing is based on the assumption that the cracks are microscopic local defects while deformations are macroscopic defects in profiles. Then, the crack information can be detected by combining the extracted candidate points and an improved minimum cost spanning tree algorithm. On the other hand, the deformation depth information is acquired based on the profile standard contours which are constructed by profile envelopes and deformation support points, the accurate location and classification information of deformation defects can be obtained by the regional depth property. Experimental tests were conducted using real measured 3D pavement data containing two categories of defects. The experimental results showed that, based on the 3D laser scanning data, the proposed method can effectively detect typical cracks under different road conditions and environments, with the detection accuracy above 98%. Furthermore, different types of deformation defects including potholes, rutting, shoving, subsidence, can also be accurately detected with location error less than 8.7%.

Genetic and Manual Survey Methods Yield Different and Complementary Views of an Ecosystem

Given the rapid rise of environmental DNA (eDNA) surveys in ecology and environmental science, it is important to be able to compare the results of these surveys to traditional methods of measuring biodiversity. Here we compare samples from a traditional method (a manual tow-net) to companion eDNA samples sequenced at three different genetic loci. We find only partial taxonomic overlap among the resulting datasets, with each reflecting a portion of the larger suite of taxa present in the sampled nearshore marine environment. In the larger context of eDNA sequencing surveys, our results suggest that primer amplification bias drives much of the taxonomic bias in eDNA detection, and that the baseline probability of detecting any given taxon with a broad-spectrum primer set is likely to be low. Whether catching fish with different nets or using different PCR primer sets, multiple data types can provide complementary views of a common ecosystem. However, it remains difficult to cross-validate eDNA sequencing techniques in the field, either for presence/absence or for abundance, particularly for primer sets that target very wide taxonomic ranges. Finally, our results highlight the breadth of diversity in a single habitat, and although eDNA does capture a richer sample of the community than traditional methods of sampling, a large number of eDNA primer sets focusing on different subsets of the biota would be necessary to survey any ecological community in a reasonably comprehensive way.

Innovative Techniques with a Multipurpose Survey Vehicle for Automated Analysis of Cross-Slope Data

Manual surveying methods have long been used in the field of highway engineering to determine the cross slope and the longitudinal grade of an existing roadway. However, these methods are slow, tedious, and labor intensive. Moreover, manual survey methods almost always require partial or full lane closure, which results in traffic delays, increases in costs, and inconvenience to the traveling public. In 2003, the Florida Department of Transportation acquired a state-of-the-art multipurpose survey vehicle (MPSV) that combines the advantage of an inertial profiler with the additional ability of simultaneously collecting images, cross slope, longitudinal grade, curvature, rutting, and roughness at highway speed. To complement the MPSV system, an automated analysis tool has been developed to process pavement geometry data for identifying areas with deficiencies in cross slope and longitudinal grade in a fast, efficient manner. In addition, this analytical tool computes a drainage path along the pavement surface. The program also displays the analysis results in two- and three-dimensional graphical formats, allowing for an easier interpretation of the data. The MPSV technology coupled with the developed cross-slope analysis tool provides an effective, practical, and cost-effective method to identify quickly potential problem-prone areas along a highway. This paper presents an innovative technique for the analysis of data on cross slope and longitudinal profile that helps identify areas with deficiencies in cross slope and surface drainage. Highway agencies can successfully implement similar systems and analysis methodology to complement and enhance their existing safety and pavement management programs.

MOBILIZED SURVEYING OF SOIL CONDUCTIVITY USING ELECTROMAGNETIC INDUCTION

Established and emerging geophysical technologies offer many promising applications for precise nearsurfacesurveying. Scientists are investigating these noninvasive surveying techniques to enhance soil mapping and research. Anoninvasive soil surveying system was developed to rapidly map soil characteristics. This system employs an allterrainutility vehicle towing a nonmetallic carriage that cradles a commercially available ground conductivity meter. Autonomousdata streams of timestamped soil conductivity data and global positioning system (GPS) data are immediately downloadedto a computer after a survey. Both data sets are automatically merged using the time stamp data as an index. Usinggeographical information software (GIS), conductivity maps of increased data density are produced onsite.<br><br>The mobile surveying system increased total conductivity sampling rate by a factor of >100, and increased data densityby a factor of >10 over a conventional manual survey method when operating over a 1ha open test site. For open fields thatcan be easily traversed with a utility vehicle, the mobile surveying system was found to greatly enhance data quality byincreasing data density, and to dramatically increase both data acquisition efficiency and data postprocessing speeds.

Assessment of Long-Term Pavement Performance Program Manual Distress Data Variability: Bias and Precision

The use of manual survey methods within the Long-Term Pavement Performance (LTPP) program for the collection of distress data has drastically increased both in intensity and in coverage over the past couple of years. Because these surveys are conducted by individual raters whose biases can lead to variability between raters, it was hypothesized that distress data variability existed and that it could potentially be quite large. Thus, the purpose of the presented study was to quantify manual distress data variability, with special emphasis on the bias and precision of the data. Results from seven LTPP program distress rater accreditation workshops conducted during the period from 1992 to 1995 were used as the only source of data. On the basis of analyses of these data, both the apparent bias and the precision for the common distress type-severity level combinations were quantified. It was also concluded from this study that individual rater variability for any given distress type-severity level combination is typically large and increases as the distress quantity increases; however, when all distress type-severity level combinations are viewed in terms of a single composite number such as the pavement condition index value, there is excellent agreement between the individual raters, the group mean, and the ground truth value, and individual rater variability is also quite small. Because LTPP program distress data are to be used in the development of pavement performance prediction models, improvements in variability are highly desirable to ensure that they serve their intended purpose. Recognizing that the LTPP program distress raters are experienced individuals, such improvements are not envisioned to come through additional training. It is the authors’ contention that the only way of achieving the desired improvement is through the conduct of group consensus surveys.

MINOR ROAD DETERIORATION: CAUSES, CONSEQUENCES AND MAINTENANCE OPTIONS.

The deterioration of minor roads has been studied variously in a number of local authorities for a period of between 5 and 9 years. Road conditions over 100 m lengths were observed by manual survey methods at 6-month intervals and by various machine surveys at yearly or 2-yearly intervals measuring profile, skid resistance and deflection. Throughout the period, sites which were chosen at the worst end of the condition ranges were removed from the study when they were adjedged to require treatment for maintenance and safety reasons. Their condition when they were 'lost' is known. A series of deterioration rates for varying causal factors are advanced for each authority and compared with data from the National Road Maintenance Conditions Survey. A maintenance treatment strategy is proposed based on conditions found on these minor road network test lengths. Reference is made to the repeatability and reproducibility of machine and manual surveys are presented. The research confirms the main causes of deterioration familiar to maintenance engineers and the average condition where remedial treatment was necessary. It corrobated the judgement of road engineers in prioritizing maintenance treatments and the associated financial allocations. A scientific approach is a necessary prerequisite to the building of economic models for the maintenance management of minor roads.