Sonar Image Translation Using End to End Network for Underwater Recognition

With the rapid development of sonar technology, the classification of underwater sonar images is of great significance. In order to recognize underwater sonar images more effectively, side scan sonar is used for data acquisition. After pre-processing, six types of sonar images are selected. Combining with the characteristics of underwater sonar images, relevant theories and methods of convolutional neural network are studied to establish an underwater sonar CNN recognition model, which achieves 98.5% recognition success rate. Experiments show that the recognition model can improve the accuracy, speed and robustness of underwater sonar image classification, and can meet the practical application requirements.

Introduction
At present, the classification methods of underwater sonar images are mainly to extract the texture features of underwater sonar images by different methods and to complete the classification by matching the features. However, due to different feature extraction methods and different underwater sonar images used for classification, underwater sonar image classification methods cannot be widely used. By obtaining the common features of the same type of images, an excellent model which is widely used in underwater sonar image classification is trained and used to identify, classify or predict underwater sonar images in practice.

Data Set Preparation
Underwater sonar images belong to a small sample data set. The dataset is divided into six categories, including rocks, wrecks, rocks, sandlines, terraces, and cracks. After obtaining the original data, it cannot be directly used as the training data of the model, and some preprocessing operations are needed [1]. After the standard naming, the data set was grayed into 1200 training sets and 600 test sets. According to the central limit theorem, environmental noise can be approximated by Gaussian distribution. Gaussian white noise with mean of 0 and standard deviation of 25, 35 and 45 was added to the image to simulate environmental noise. Pretreatment mainly studies the denoising method of environmental noise.

Pretreatment
Some indicators are needed to evaluate the denoising effect of the algorithm. The commonly used evaluation criteria are peak signal-to-noise ratio (PSNR), mean square error (MSE) and average absolute error (MAE) [2]. The denoising effect of terrace image is shown in figure 1. The PSNR of BM3D is still the highest among the four algorithms, and both MSE and MAE are lower than the other three algorithms. As shown in table 1, the median filter is better than the gaussian filter in the rock denoising effect, but the gaussian filter in the terrace image is slightly better than the median filter.

Network Structure
In order to find the area of interest of the sonar image and align it in the coordinate system, ROInet takes the original sonar image as input and outputs the area of interest image of the sonar image. As can be seen from the following figure 2. The first half of ROInet is a pre-trained VGG16 network, splice after Pool3, and the top is LRN, which is used as the feature extractor of the network. In this way, L2 normalized feature maps can be generated to retain the spatial information in sonar images. The second part of the feature map connected to ROInet is a fully connected homing network. The two hidden layers of FC1 and FC2 have 1024 and 512 neurons. After these two layers, relu activation function and Dropout are used to avoid the overfitting phenomenon of neural network.The local network is composed of feature extraction network and regression network, and then output normalized coordinates. The normalized coordinates θ are forwarded to the grid generator which transforms a regular, square grid to a deformed grid based on θ and the normalization range is between -1 and 1. A bilinear sampler takes the deformed grid as an input and samples the original hand image to form a regular grid of pixels, which is the ROI image [3]. ROInet is connected to FERnet, which is responsible for the extraction and recognition of sonar image features. To extract sonar image features, the ROI image was fed into another independent CNN, which was also trimmed after Pool3, with VGG-16 of LRN at the top. Although the feature extraction network is the same between them, there is no weight sharing. LRN is an output layer, is space said sonar ROI. Spatial dimensions are defined in the grid generator in ROInet. So LRN connects to a Dropout layer and a full connection layer FC4, and the final output is a 512-dimensional vector .The vector is passed to the other Dropout layer and the full connection layer FC5, which returns the sonar image tag.

Training Strategy
In this experiment, at the beginning, in order not to destroy the convolutional layer of pre-training, the best way is to train only the top layer of random initialization.At the same time, you can train the bottom layer. In the first stage, the localised network is trained to initialize ROInet, which is used to extract ROI of sonar images. In the second phase, FERnet is trained based on ROI.In the third stage, the model was trained end-to-end, and the ROInet initialized in stage I was connected with FERnet for training. In short, stage III is end-to-end training, and Stage II is not. In the second stage, sonar ROI is input, and in the third stage, complete sonar images are input.While training the final model, the ROI input to FERnet depends on the input sonar image and ROInet [4].
As can be seen from the following table 2. In S1-S5 strategy, after ROInet is connected to FERnet, FERnet conducts training according to the sonar image training network. In the S0 strategy, only FERnet is trained separately. The top levels of module C, FC4 and FC5, participate in the training of all strategies.After 20 epochs, the local network was microadjusted and training was stopped at 40 Epochs. ROInet extracts ROI images and inputs them into FERnet. Of course, strategy S0 is not an end-to-end exercise. In the S1 strategy, all layers of FERnet and ROInet were off, parameters in the C module only updated N epochs and closed Drop1 and Drop2.Strategy S2 is the same as strategy S1, but Drop1 and Drop2 are open. Strategy S3 was the same as S2 and S4, but after 20epoch, the learning rate was reduced to 0.0001, and the B module in ROInet was fine-tuned. S3 closed Drop1 and Drop2, and S4 opened Drop1 and Drop2.Finally, strategy S5 was the same as strategy S4, but after 35 epochs, Drop1 and Drop2 were turned off [5].

Experiment and Analysis
ADAM optimizer is used in this network, with momentum of 0.8, batch size of 64 and learning speed of 0.001. After network training, it will converge. The ROI image is 224×224 resolution. In ROInet, adjust the size of the input image for 56×56.ROI image size is set to 112×112 pixels, so size is 14×14×256. Drop1, Drop2, Drop3 and Drop4 value is set to 0.5, 0.1, 0.2 and 0.5.Leakyrelu was used as the activation function, and the parameter value in Leakyrelu was 0.2 [6] .In layer FC1-FC5, the weights and biases are randomly sampled from a Gaussian distribution of 0 mean and 0.001 variance [7].
In order to verify the robustness of the model, different noise levels are added to the data in the normal test data set to simulate different underwater environments [8]. As can be seen from the following table 3, with the increase of noise intensity, the PSNR value keeps going down and the image quality becomes worse and worse. When the number of test samples remains unchanged, the effective recognition number and the recognition rate decrease [9].
The results show that the test results of this method in the existing data set are superior to other recognition and classification methods. Meanwhile, the change of recognition rate of various methods with the change of noise intensity is recorded in table 3. Without adding noise, the recognition rate reached 98.5%. With the increase of noise intensity, the accuracy of all recognition methods decreases. When the standard deviation is over 55, the recognition rate of the model is lower than 60%; when the standard deviation is 95, the recognition rate is 27.1%; the recognition rate of other methods is basically lower than 10%, and the robustness of the model is stronger than that of other methods. The BP model has the lowest recognition rate and low robustness, and the classification accuracy of SVM and AdaBoost is lower than that of other convolutional neural network models, which proves the advantage of applying the convolutional neural network and the feasibility and practicability of applying the model in this paper to sonar images [10].

Conclusion
In this paper, six kinds of sonar images are selected and combined with the characteristics of underwater sonar images, relevant theories and methods of convolutional neural network are studied, and an underwater sonar CNN recognition model is established to classify these six kinds of targets. In the pretreatment, BM3D algorithm can remove the environmental noise better than the other three algorithms. Five training strategies were compared and tested to choose the best one. The experimental results show that the robustness of the model is tested by adding noise, and the recognition rate and robustness of the model are higher than those of other methods after comparative analysis. General research of image data from the same waters, the experimental conclusion to some extent, the lack of universality, want the future to the sonar image can collect more data, or experimental study results are integrated to more waters, establish the database of different waters and classification criterion, let different bottom sediment classification of underwater environment problem can get differentiation treatment, also let the unknown underwater environment according to the forecast.


Phase Locking in Millimeter Wave Frequency Synthesizers-Design overview of Charge Pump Phase Locked Loops

Phase Locked Loops are key blocks which are widely adopted in all area of electronics, especially transceivers in wireless communication systems. The application of Phase Locked Loop varies from generation of local oscillator signal for upconversion and down conversion, generation and distribution of clock signals and jitter reduction. The most extensive use of Phase Locked Loop is for frequency synthesis. The requirements of synthesizer architectures depend on various system requirements and specifications which are based on regulatory standards. The design of Phase Locked Loop components involves the consideration of various techniques to resolve the nonidealities at front end high frequency components as well as back end low frequency components. This paper presents the background and importance of a Phase Locked Loop, various approaches over the years, design choices for each block and practical design methodology for Charge Pump Phase Locked Loops. This paper also presents the system level design of Phase Locked Loop and supply noise interactions among sub modules inside a charge pump Phase Locked Loop. Key–Words: Phase locking, Charge Pump, Voltage Controlled Oscillator, Frequency dividers, Loop filter Received : June 2019, Revised : Nov 2019, Accepted : May 2020

Introduction
Phase locking is an important technique widely employed in majority of analog, digital and RF systems. It is an integral part especially in high speed serial links for clock and data recovery as well as in clock synthesizers. Modern processors and system-on-chips utilize multiple Phase Locked Loops (PLL) to cater their varying demands. Conventional PLLs are implemented using Charge Pump architecture as shown in figure 1.  [1] and [2]. A typical Charge Pump implementation of PLL consists of a phase and frequency detector (PFD), a charge pump (CP), a loop filter (LF), a voltage controlled oscillator (VCO) and divider in feedback to use it for varying applications. This paper presents an overview of operating principles, basic architecture, and selection of circuit blocks and design methods of Charge Pump Phase Locked Loop based on CMOS technology. The paper is organized as follows. Section 2 gives the fundamentals of Phase Locking, s domain representation of charge pump PLL and frequency synthesizer and figures of merit of PLL. Section 3 and 4 describes the front end and back end components of PLL and its design choices. Section 5 gives the practical design methodology and Section 6 presents system level design of PLL. Finally Section 7 and 8 sums up the state-of-the-art performance of PLLs and supply noise interaction in a charge pump PLL and section 9 concludes the paper.

Fundamentals of Phase Locking
PLL is generally characterized in terms of phase and frequency variables. The best analogy to understand PLL is by using a voltage follower as given in figure 2. As the feedback forces Vout to be equal to Vin in a voltage follower, in a PLL feedback forces output and input phases to be equal. Using the phase variables, all the analysis of any feedback systems like stability analysis or noise analysis could be extended to PLLs also [3]- [5].

Narrowband PLL
In Narrow-band PLLs, [11]- [16] VCOs are implemented using resonant characteristics of LC tank circuit. The long-term jitter performance of narrowband PLLs makes them a superior choice over wideband PLLs. At the same time, these types suffer from a very low tuning range which is only 10-20% of the center frequency. These PLLs consume more area for realizing on chip inductors.

PLL as a frequency synthesizer
PLL fundamentally corrects the phase difference between the two periodic input signals and the output of the PLL is always a multiplication of the input reference frequency [17]. The output frequency or synthesized frequency can be expressed as Depending on the N value, synthesizers are classified as an integer synthesizer or a fractional synthesizer [18]- [22].

S domain model of PLL as a frequency synthesizer
Even though PLLs are nonlinear feedback systems, their operation can be approximated by linear analysis using Laplace transforms. All the individual blocks are represented in s domain to get the complete model. The Fig 3 represents the simplified s domain model of synthesizer [23]- [26]. The type and order of PLL is mostly determined by the number of poles located at origin and the number of poles in the system. All VCOs have a pole at origin which makes all PLL atleast type 1. In order to track the phase, another pole is added by a capacitor C 1 at dc. This makes the overall PLL a type 2. But the presence of two poles at origin causes instability. So a resistor R 1 in series with C 1 capacitor is added which introduces a zero and hence stabilizes the loop. The current pulses from charge pump may further produce ripples in the control line of VCO [27]- [31]. The ripples deteriorate the spectral purity and generate spurs. This can be minimized by the addition of another capacitor C 2 , which makes the PLL a type 2, third order synthesizer. Higher order and types of PLL are possible, but seldom used because of loop stability issues. The figure 3 shows the s domain representation of a Type II order 3 system.
The phase frequency detector and charge pump are combined and represented by K P F D . The loop filter used here is of the order two.K V CO , VCO conversion gain represents the tuning or sensitivity of the frequency which is measured in rad/s [32], [33]. The open loop transfer function

Figures of merit of a PLL frequency synthesizer
The design of a PLL is a detailed process which involves multitude of variables. In the preliminary design phase, limiting the number of such variables help in deciding the architecture of each block and adopting the technology node. The three fundamental figures of merit considered in the design of PLL are phase noise, spurs, lock time [34]- [39].

Phase Noise
It is the random deviation of a frequency tone that is spread around the center frequency. Ideally the tone of a PLL is characterized by an impulse, whereas the real one is spread in the frequency domain shown in the figure 4.A transfer function based study will help to estimate the contribution of phase noise of each block to the total phase noise of the PLL. The figure 5 shows the typical phase noise of PLL components.

Spurious Emissions / Spurs
The two fundamental reasons for reference spurs are mismatches of currents in a charge pump and the leakage currents. Banerjee [17] and Maxim [18] proposed two models to predict the spurious emissions. The expression proposed by Banerjee is used to calculate the spurs.

Lock time
Lock time specifies the maximum time that can be used in the commutation of a channel to another. This is defined by IEEE 802.11a standard and must be less than 1 ms. In a frequency synthesizer lock time is determined by a set of parameters identified in the loop.

PLL front end components and design choices
The components of a frequency synthesizer operate at varying range of frequencies.
The prescalars, which are the first stage frequency dividers and voltage controlled oscillators typically operates at high frequency and are together known as front end high frequency components [40]- [43].

Prescalars / Frequency dividers
The first frequency divider (FD) stages in a synthesizer are called as prescalars. They are not treated separately for a low frequency synthesizer, but for a mm wave synthesizer, prescalar needs to operate at the highest frequency of the loop and thus its design is challenging.
The figure 6 shows the basic classification of prescalar for mm wave frequencies. It is categorized as analog, digital and a combination of both known as hybrid. The choice of different topologies depends on various requirements like power consumption, locking range, phase noise and complexity of design. The basic analog frequency divider if first proposed in [44] is a regenerative type where it employs a mixer based on a Gilbert cell. These types of dividers does not self oscillate. Moreover the performances of these dividers are not satisfactory above 60 GHz [45][46][47][48][49][50]. The locking range which is based on eqn 1 is less for these types and power requirement is also more.
The promising analog divider which is widely used is of injection locking type where the oscillators are forced to oscillate from its resonant frequency. By properly choosing the amplitude and frequency of the sinusoid, the circuit will oscillate at the injection frequency; the figure 7 shows the injection locking principle. These dividers can be adopted for high frequency division but at the cost of lock range and design complexity.

Digital frequency dividers
The digital dividers are of two types -static and dynamic. Static dividers are mostly based on edge The maximum operating frequency of a static FD is given by where τ pd is the propagation delay from input to output. [25]- [27] Dynamic dividers are different from static in the use of latches and true single phase clock dividers in the circuit topology. If static is good for better locking range, dynamic is good for its low power consumption.

Travelling Wave frequency dividers
This topology was first proposed in [51] with bipolar technologies and later with CMOS technologies. This topology offers many advantages like differential outputs and less number of transistors leading to less parasitics. The figure 8 shows a travelling wave FD, where it consists of three differential amplifiers. The operation resembles that of a bistable differential amplifier that can switch between master and slave state. The analog nature of the circuit can be identified by studying the current transitions between different branches of the circuit.

Voltage Controlled Oscillator
Voltage Controlled Oscillator (VCO) [52]- [53], [63] is considered as the heart of any synthesizer that operates at the highest frequency. It provides the actual oscillations at the output. The spectral purity of the synthesizer depends on VCO and its ability to reject common mode noise. The lock range of the PLL is determined by the tuning range of VCO. Figure 9 shows the classification of VCOs [54].
Ring oscillator is a popular resonator less VCO, where it uses cascade stages of inverters. The tuning of these kinds of oscillators is done by varying the transconductance of the delay stages. Typically a tuning range of more than 30% is possible with these types. But tuning can be achieved at the cost of spectral purity and phase noise performance. The ring oscillators reported are limited to a maximum of 15 GHz in bulk CMOS processes and therefore not a choice for mm wave frequency synthesizers [55].
Another category is resonator based VCO [54], where a tank circuit is employed. The main advantages of using a resonator based VCO is it can work close to the maximum frequency f max of a given technology [56]. The frequency of oscillation is determined by where L and C are the inductance and capacitance. The design of oscillator with low phase noise can be done using the Leeson's phase noise model as shown in figure 10 [57] - [59]. This model shows the variation of phase noise based on the frequency offset, center frequency and the Q factor of the LC tank.  The circuit in figure 11 shows a CMOS based VCO. The oscillation frequency is varied by the tuning mechanism and is usually accomplished by the capacitors.
The voltage swing in the CMOS cross-coupled oscillator is twice the voltage swing in the NMOS cross-coupled oscillator. From (1), the phase noise decreases as the voltage swing increases. In order to have lower phase noise, CMOS topology is better than NMOS type.

PLL Back end Components & design choices
Compared to front end components, PLL back end components including Phase Frequency Detector, Charge pump and feedback divider chain operates at lower frequencies.
This section presents an overview and design choices for back end circuits.

Feedback divider
Feedback divider generates an output which is very close to synthesizer reference frequency. The input for the divider chain comes from the prescalar. In most of the cases the prescalar frequencies will be in the range of GHz and feedback dividers need to divide those frequencies into MHz range to get close to reference frequency.
A dual modulus divider is a widely used circuit to change the division ratio in a frequency synthesizer. These circuits are used when area is a concern and are commonly implemented in MOS Current Mode Logic MCML or Source Coupled Logic (SCL). The different approaches in the design of CML dividers relate the gate delay to the power consumption, with the constraint of a given output swing and voltage gain of the differential pairs, in order to satisfy the requirement on the noise margin. Phase noise is an impor-tant criterion in the design of dividers, as there is a tight compromise between low phase noise and high frequency of operation. A popular CML based topology is shown in figure 12.

Phase Frequency Detector, Charge Pump and Loop filter
The components PFD, CP and loop filter forms the rest of the PLL back end whose combined task is to provide a stable dc tuning voltage to the VCO so that synthesizer can move towards lock state. PFD is a circuit which detects both the phase and frequency between two signals and generates an output in proportion to them. The simple and widely used implementation is with two edge triggered D flip flops. PFD implementation has a potential problem known as dead zone problem when the phase error is very small which leads to incorrect operation of subsequent stages or even zero loop gain. This dead zone prevents the synthesizer to work until the phase error reaches a certain minimum value. Many PFD implementations are reported [59][60][61] aimed at robust designs in improving the various characteristics of PFD like operating frequency, dead zone, complexity, and symmetry. The next component in the line is the charge pump which converts the output signal of PFD into a charge and thereby responsible for moving control voltage of VCO up or down by pumping current in or out of the loop filter as shown in figure 14. The increase in V ctrl is given by The average charge dumped per cycle is given by The transfer function of the PFD and the charge pump is given by P D(s) = K P D = I CP 2π (12)

Practical considerations for the design of charge pump
The design of charge pump is done in such way that reduces mismatches between up and down currents and thereby nullifies charge deposition at the loop filter. Fluctuations or variations in the filter voltage due to current mismatch in the charge pump result in reference spurs or discrete spurs in the output spectrum of the frequency synthesizer, called as reference feedthrough. In addition, to maintain a constant loop-bandwidth, the magnitude of the charge pump output currents must be independent of the output voltage. Authors in [5] proposed a charge pump for low-voltage PLLs that combines a replica biasing technique and a feedback structure. Current matching is improved over a wide output voltage range. Apart from the mismatches, output impedance of the charge pump is also considered invariably.
The loop filter, the last circuit in the back end, suppresses the high frequency components at the charge pump output. The overall loop dynamics and stability depends on loop filter. . It consists of a resistor in series with a capacitor C 1 both of which are in parallel with another capacitor C 2 . The capacitor C 1 introduces a pole which is balanced by the resistor introducing a zero so that it is stable in the given frequency range. The addition of C 2 is to suppress the ripples in the control voltage, V ctrl . This may lead to the system becoming unstable. Hence, the capacitor and resistor values are chosen such that they ensure optimum stability and maximum high frequency noise component rejection. The transfer function of the loop filter is given by The

Practical Design Methodology
The most crucial part in the design of a frequency synthesizer is the determination of practical and realizable specifications for the back end and front end circuits. This section proposes a practical design methodology to determine the initial requirements for the various specifications that need to be considered for the design. Table 1 shows specifications to be targeted in the design of a mm wave frequency synthesizer.

Identifying Initial requirements of a synthesizer
The first step in the design of a synthesizer is to develop the initial requirements. A sample initial requirement for a mm wave synthesizer is as shown in Table 2.

Architectural selection
With the initial requirements, architecture selection can be done for both front end and back end circuits. Depending on the target specifications architectures can be selected and simulated in block level and later.

Synthesizer integration
The overall circuits can be simulated in any of the available CAD tools. Transistor level simulation of complete synthesizer is a tedious process. Due to the large simulation time, front end and back end circuits are simulated separately before integration. The figure 16 shows practical methodology for the design of synthesizer. It is a good practice to start with the system level simulation before implementing individual blocks in circuit level.
6 System level design of PLL This section discusses the system level design of PLL using the CPPSim toolkit. The CPPSim package includes two tools namely, PLL Design Assistant and Sue2. Initially the designing is done using PLL Design Assistant which considers the ideal conditions. Further, Sue2 is used to complete the system level design by including the non-idealities like charge pump mismatch and non-zero phase error. A black box approach in order to understand the operation of PLL is performed using the PLL Design Assistant tool by CPPSim software. The PLL design assistant tool provides a graphical user interface to design PLL at the transfer function level. This section presents the analysis of results obtained using design assistant tool. With the PLL design assistant, noise performance of the PLL under different configurations was examined and we decided on the system parameters needed to meet the GSM phase noise specifications and also to meet the settling time requirements. Also, the impact of parameter variations was analyzed along with its effects on phase noise and settling time. Fig 17 to Fig 19 shows the results of phase noise under various conditions and closed loop step response and stability analysis of a PLL designed with PLL design assistant tools.

State-of-the-art in CMOS PLL
This section compares different CMOS frequency synthesizers implemented so far in diverse technology processes. An overview of recent state-ofthe-art synthesizers representing their technology node and fundamentals figures of merit is given in Table 3. The frequency is expressed in Giga Hertz, technology node in nm, phase noise measured at 1 MHz offset in dBc per Hz and power in milli watts.

Conclusion
Phase locking as described in this paper is one of the inevitable approaches for frequency synthesis. This paper presented the background and importance of a Phase Locked Loop, various approaches over the years, system level design approaches, practical design methodology, architectures for Charge Pump PLLs. The system level design of a Charge Pump Phase locked loop was presented with PLL design assistant tool and supply noise interactions among sub modules inside a charge pump PLL are also discussed.


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Computer Science
Business

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IT Services and Crowdsourcing in Support of the Hellenic Cadastre: Advanced Citizen Participation and Crowdsourcing in the Official Property Registration Process

Greece is one of the few European countries that has not yet a fully operating Cadastre at national coverage. Cadastral surveying and property registration have been completed only for about 8% of the Greek territory, which includes about 18% of the total property rights. At the remaining areas, cadastral surveying and property registration is still under development and is expected to be completed by the end of 2021. The cadastral survey is a participatory procedure in which property right holders, or their representatives, are expected to declare the land parcels on which they have property rights and submit all documents that prove these rights. The time frame available for the declaration is 3 months, which is specific for each municipality. Right holders are now enabled to provide both spatial and descriptive information, regarding their properties and property rights, without the need of a professional. That includes the parcel’s boundary lines along with the legal documents required to validate the declaration. Each right holder, or a representative, has the option to submit a declaration over the internet through a web application without the need of a professional. Within the frame of a PhD research, several concerns were addressed such as usability, security, and scalability in order for the outcome to support its scope while respecting binding constrains. As a result, two web applications have been created to officially support the Hellenic Cadaster property rights declaration process. The suggested approach was considered a big success in terms of both public acceptance and usability as shown from the statistics collected during the first eight months of its formal operation.

Introduction
The Hellenic cadastral project was initiated in 1995. To choose the most appropriate methodology and technical specifications for its compilation, several issues have been seriously considered by the local experts and authorities. Traditionally, cadastral field surveying involves the participation of right holders and/or other local residents to adjudicate rights, right holders, and parcel boundaries in the field, or the demarcation of the parcel boundaries on the ground prior to the air photography, if photogrammetric procedures are to be implemented. However, field surveying of each cadastral parcel was considered to be unaffordable for Greece. Additionally, the demarcation of every boundary node on the ground prior to the aerial photography was also considered to be complicated and costly for a nation-wide cadastral survey. Besides, it was realized early that technology improves fast so it would not be wise to prescribe by law the exact methodology to be used, but it was preferable to make it flexible for the private sector to choose the fit-for-purpose method (e.g., field survey or ISPRS Int. J. Geo-Inf. 2020, 9,190; doi:10.3390/ijgi9040190 www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2020, 9,190 2 of 17 photogrammetry or combination of them) according to the situation. Thus, the technical specifications prescribed only the required accuracies of the final product. It was also decided that the project would be compiled by the private sector, through a tender procedure, in several phases and it would start with pilot studies (mainly in urban areas where most property rights exist) to investigate the problems. Contractors were expected to compile basemaps and right holders were asked to submit their rights (deeds /titles and any other additional document/surveying plan) and identify the parcel on the basemap. In the earlier phases, right holders were asked to either visit the local cadastral survey office and locate the parcel boundaries on an aerial photo or an orthophoto with the help of the cadastral surveyor and/or local experts at the office. At a later phase, right holders could use the internet to enter a single point within the parcel. In the latter case, that single point was used by the contractors to identify the actual parcel boundaries using the deed information as a guideline. This approach worked well for urban areas where the majority of parcel boundaries and buildings are easily identifiable on photos/images.
The last phase of the project began in early 2019 and was the most difficult one as it mainly concerned rural areas where parcel boundaries are not demarcated on the ground and patterns on images are confusing due to similar vegetation; also, some are invisible. Still, field-surveying the whole territory would not be affordable.
Using the same methodology for the rural areas was proved to be not feasible mainly because of two reasons: (a) the use of internet and digitization of a single point within the parcel was not sufficient and could not help the contractor to identify the actual boundaries of rural parcels, so the right holders had to visit the cadastral declaration office, and (b) identification of the rural parcels on orthophotos at the office, was not at all a simple job; neither for the right holders nor for the personnel of the cadastral survey office who had to dedicate a long time helping each right holder, especially the elderly; in that case, the procedure became unprofitable and not successful.
It was then obvious that a larger involvement of crowdsourcing methodology is the only viable solution (fit-for-purpose approach) within the projects' budget and time constraints. For this purpose, a methodology and a technical tool had to be developed to facilitate such a process. The research methodology that was followed to reach a definite result was: • Perform research in international literature • Test the crowdsourcing methodology to ensure that the demanding Cadastre requirements are met • Establish a committee of experts to consult and co-design the application • Develop the appropriate software to meet the requirements • Test the software through various environments • Production The main objective of this paper is to describe the development of this technical tool (two applications) that enables crowdsourced identification and digitization of parcel boundaries and electronic submission of all rights and other documents by the right holders/citizen alone, without the need of professional involvement. This crowdsourced identification of parcel boundaries should be possible either in the field or from home/work. The tool should be simple to use, should serve all cases, and should be able to support multiple users simultaneously. The tool should also allow spatial data validation, an automated checking of the crowdsourced spatial data for the majority of the cases, so that all gross errors in the geospatial location of the property units will be avoided.
First, a literature review of the state-of-the-art in applying crowdsourcing in land administration is given; then a brief description of the considerations, criteria, and requirements set for the design of such a tool follows; the system architecture and functioning mechanism is briefly explained; the automated controls of the submitted data are described; and constraints, factors to consider, problems introduced, and solutions given are discussed, in a real-life example of using a crowdsourcing methodology officially for the collection of cadastral information.

Literature Review
Since the launch of the internet and the creation of the online world in the 1990s, a new branch of economics called the digital economy or internet economy or web economy gradually emerged and has been defined as the branch of economics studying zero marginal cost of intangible goods over the internet. Digital networking and communication infrastructures have provided a global platform over which people and organizations devise strategies, interact, communicate, participate, collaborate, and search for information. New terminology has appeared in the everyday life of e-government (electronic government), e-business, e-commerce, e-democracy, e-participation, and m-government (mobile government meaning services provided through mobile phones and wireless internet) [1,2]. The various National Mapping Agencies (NMAs) have been experimenting on providing e-services (electronic services) to people as well as m-services (mobile services provided through wireless internet for mobile phones) [3] and crowdsourcing as the extended use of Internet and smart and affordable devices became a fact [2,4].
The Hellenic Cadastral project has been implemented through several phases with quite different procedures [5]. Initially the project had little dependence on automation and IT processes. Thus, the right holders had to bring all available documents (titles/deeds and surveying plans) and visit the contractors' offices (private companies responsible for the data collection) to submit their cadastral declaration on-site.
In 2007, during the phase of a major assessment of the project's progress, it was decided to modernize the Cadastral Agency according to the current international trends. Main goal of this upgrade was to develop e-services and processes in order to (a) minimize the required human effort and (b) increase efficiency and maximize the benefits of the available technologies and the cloud. All contractors were connected, real time, to centrally allocated IT systems over secure networks. Right holders were given the option to submit all documents for their property rights from a distance, through Internet, within the comfort of their home or office.
In the following years, an international in-depth research was initiated investigating the options of introducing crowdsourcing technology and the use of smart phones in the cadastral surveying for the collection of cadastral data directly by non-professionals [6][7][8]. Some publications have been published on investigating: the potential of data collection through crowdsourcing for land administration and on defining a draft procedure on how this may be achieved [9,10]. In parallel, research was focused on various critical success factors when using crowdsourcing technology. Investigating the citizens' motivation over a potential participation [11] examined the key factors needed to promote people's interest in crowdsourcing. Other studies focused on assessing the OpenStreetMap contributors and their contributions [12] or comparing accuracy between crowdsourced data and official mapping [13,14]. In order to evaluate crowdsourced data, other studies focused on defining measures and indicators for evaluating VGI quality [15]. Land administration has different constraints and demands so some studies examined the option of using OpenStreetMap as a basemap for cadastral surveying [16] and promoted the need to build a conceptual framework for utilizing VGI in land administration [17]. Crowd sourcing nowadays is bound to mobile devices driving other studies to examine the potential of mobile applications using an accurate orthophoto as basemap in an effort to check the reliability, achieved geometric accuracy, and usability of the method for the compilation of accurate, assured, and authoritative cadastres such as the Hellenic Cadaster [18]. Various technological solutions for citizens' participation into cadastral mapping [19], as well as policy solutions [20] have been tested with good results. The applicability of crowdsourced geospatial information to real cadastral data, as tested in New Zealand Cadastre [21] and in Greece, based on the gained experience so far [22] which portrayed promising results as well. The research in using crowdsourcing in land administration was continued in order to check the achieved geometric accuracies and to assess the potential of applying crowdsourced methodology for accurate and reliable cadastral surveying for the initial registration both in urban and rural areas in countries with developed economies that are still under cadastral surveying [23] and on investigating new roles for professionals and citizens or right holders for accurate and reliable 2D crowdsourced cadastral surveys [24]. Other studies focused on the efficiency of applying crowdsourcing in land administration in the post conflict and developing world [25][26][27][28], in order to speed up the cadastral surveying and the first registration process in the economies in transition [29,30]. Once researchers and cadastral surveyors were convinced about the reliability and/or the efficiency of this technology international research was continued on developing a methodology for ensuring and motivating increased people participation in the cadastral surveying [31]. Research in this field also continues in investigating tools and methods for using crowdsourcing and smart phones or other low-cost technology for the compilation of 3d reliable cadastral surveys [32][33][34]; as well as in introducing it in the general work of the land surveyors and in the official map production [3,35].
Today, many processes are fully automated and improved IT services are provided to both contractors and right holders for the compilation of the Hellenic Cadastre. Contractors can now upload their deliverables, using digital certificates, and wait for the automated quality control results for both the descriptive and the spatial information. Right holders can use the Internet to get information about all aspects of the cadastral process beginning with the declaration of property rights and ending with the final result of the process. No legal reforms were needed in order to introduce such IT and crowdsourcing services into the Hellenic Cadaster. However, so far not all citizens are eligible to have access in the cadastral surveying procedure and data; the Hellenic Cadaster is not an open cadaster yet. For this project, only the right holders and/or their representatives participate and submit a declaration of property rights through the new e-services.
In the project phase prior to 2016, the right holders should only declare/digitize a single point on an orthophotomap (inside the parcel), in order to spatially locate the parcel of interest. This information, along with all submitted descriptive data, was passed to the contractor for further processing. The contractor had the responsibility to identify the parcel boundaries, taking into account all the provided additional information. That is a feasible approach for urban parcels, where the parcel boundaries are visible and can be easily recognized on the orthophoto, but it is totally insufficient for the rural parcels [10]. The last cadastral phase, following 2016, targeted mainly on the rural parcels for the remaining Greek territory. This is the largest program ever commissioned in Greece that addresses the majority of the right holders. Experience from previous phases had already raised two major challenges:

•
The existing official web site for the cadastral declarations collection was too difficult to be used by the right holders, and • Selecting a single point inside the parcel was not adequate to locate and identify the parcel sufficiently. For rural parcels, all parcel boundary points should be identified on the orthophotomap and digitized.
To address the above-mentioned challenges, using the experience derived from the above-mentioned international literature research in the field of crowdsourcing in land administration, an in-house spatial tool with two new IT services were created, in 2019. The Cadastral Declaration Web Site (CDWS) and a parcel location helper for mobile phones. The scope of these services was to enable the large group of right holders, who vary in terms of age, gender, education, and digital skills, to easily identify and locate their property parcel and to complete the cadastral declaration process with less location gross errors, without any need for professional support.
The data collected would then be delivered to the contractors in order to corelate contract data with spatial data and decide on the actual parcel boundaries based on both citizen's delineation and submitted contract information. The contractors would submit their proposed parcel fabric at a later time, within approximately one year after the declaration period was over. Those parcels would then be publicly exposed, along with the contract information, for all citizens to see and submit their objections, ISPRS Int. J. Geo-Inf. 2020, 9,190 5 of 17 if any. The contractors would then evaluate each objection and propose a solution based on the new information received. The new proposal will go through independent committees, composed of both lawyers and surveyors, in order to decide on the final status of both spatial and legal information. That status can only be changed at courts.

Main Requirements for the Design of the Spatial Tool
A team of cadastral experts from the Hellenic Cadastral Agency of various fields, such as surveyors, IT experts, lawyers, media experts, and help desk specialists was created to help in the software design phase. The design phase lasted for several months, until the compilation of a flow chart that was both sufficient and easy to follow. It was agreed that the general requirements that every software tool, planned to be used for such a nation-wide project, should satisfy for its smooth function refer to its "usability", "security", and "scalability". Within this concept, the design of the tool was thoroughly discussed. The team provided the necessary input for the creation of several mockups resembling the actual process.
Crowdsourced parcel boundaries and property rights declared by non-experts is a challenge especially in the rural areas, where legal boundaries are not recognizable and/or demarcated on the ground. Land use-parcel boundaries do not coincide with property-parcel boundaries in the rural areas as in several property parcels the same type of agricultural products may be cultivated (e.g., olive trees, orange trees, vineyards, vegetables, etc.). The tool under development should offer an easy way for completing the declaration process with minimal effort by the user.
In terms of "usability", the agreed main guiding characteristics are: (a) the User Interface should provide distinct steps in a flow, (b) the system should be able to indicate all possible errors per step and in general, and (c) all searches should be single field based, meaning that each user would enter the information that was known to him/her and the system should try to correlate with all available data such as localities, points of interest, etc.
"Security" is a common requirement of all software packages used in the National Mapping Agencies. For the Hellenic Cadaster project there is a special legal constraint in the spatial location process. For reasons of national security, high resolution, accurate orthophotos are not allowed to be exposed as web services either using OGC or proprietary standards without enhanced security. That meant that a security tier should be introduced to provide the enhancement needed which was more than authentication and Secure Sockets Layer (SSL). This made the technological selection more complicated, introducing "scalability" issues.
The cadastral data collection period for this phase of the project was planned to begin at the beginning of 2019 in order to support declarations for approximately thirty million expected property rights. Contractors would need to use about 6,000 concurrent user positions in all their offices, while it was estimated that about thirty percent of all declarations would be submitted through the Internet. The system had to be able to provide stable and fast services while using the security constraints enforced by the National Security Agency.
Vector portrayal was another big concern in terms of scalability. Various vector layers were expected to be drawn on the orthophotograph for each user. Standard pre-caching could not be applied because the vector layers are frequently changing, and it would take a long time to cache all the information especially in larger scales. On the other hand, dynamic vector image creation (upon request) was not applicable as well, because it would require a vastly larger infrastructure in terms of hardware, since each user request would require server resources to dynamically create images.
Furthermore, the required capacity support of the system to enable 3D terrain viewing for all users added another layer of difficulty in addressing the "scalability" issues. Finally, the system should also provide an automated quality control of the submitted data to avoid gross errors and to allow on-line payment of registration fees.

System Architecture
The agreed requirements proved that neither commercial off-the-shelf (COTS) nor open source software could address all issues especially in the case of dynamic vector data imaging for thousands of concurrent users using specific hardware restrictions. Available software offered either pre-caching or dynamic creation of images or vectors, which, as previously explained would require a larger infrastructure than what was available. Security constraints such as specific Time to Live (TTL) and encryption per request and per user for the orthophotos induced another layer of complexity that the existing software did not offer. Existing software provides SSL encryption, user login, and roles for protection of requests on spatial web services. When utilizing the above restrictions, it is still possible to create an automated process to extract basemap information from those services. National security requires that the web services exposing accurate basemap information should not be prone to automatic extraction, even if authentication is being utilized.
To address the aforementioned issues, a new system was built from scratch specifically designed to resolve these problems. The new system introduced two tiers: a server tier and a client tier.
The client tier is a new JavaScript library API named (WebAPI), similar to openLayers or Google Maps, in terms of functionality and usage that was created from scratch. WebAPI is responsible for creating encrypted, per user, requests to the server tier and receiving bulk proprietary data. To support thousands of concurrent users, the WebAPI is designed to accept the bulk data and use the clients' CPU power to de-cypher and portray the results for both 2D and 3D terrain presentations. Using client-side rendering mechanisms, the server tier load is mitigated to the clients thus allowing many more users than what the current COTS or Open Source software allow when installed on the same hardware.
Furthermore, WebAPI uses proprietary mixed vector-image caching mechanisms to relieve the server load and transfer the processing to the client. The main idea behind the vector caching was that the whole jurisdiction of Greece was divided into a grid with a square block side of about 1.4 km. Each time vector data is updated, something that happens frequently, the system caches, in a proprietary vector format, the affected blocks. Caching duration is normally a matter of a few seconds up to a few minutes, according to the size of the affected area. For small scales (1:20,000 and lower), image caches are created again within a few seconds up to a few minutes. The WebAPI introduced the idea of mixed vector-raster caching and it can handle it automatically for all scales. If a user works at a small scale, then the raster caching will be activated, otherwise the vector caching will be activated with the WebAPI, filtering the displayed results based on bulk blocks.
Since this WebAPI is designed to offload the server utilization, the existing server-side hardware can support many more users simultaneously. In fact, the WebAPI initiated a "handshake" with the server, upon connection, exchanging unique key information in an encrypted format. Each client request is then validated by the server and is served only for the specific user. If the same user repeats the same request after one minute, the server will deny serving it in order to protect potential "trojan infections" (unauthorized access request). The WebAPI, along with the vector caching, use cached raster tiles as orthophotos for the various scales. When the user navigates on the orthophotomap, the WebAPI produces a proprietary user specific encrypted request for each tile and for each layer.
The server-side tier is a custom-made map server built to feed the client-tier. The server is built using C++ and .Net and is deployed in windows servers.
The above mechanism allows increased traffic utilization, but it requires minimized server-side usage, and thus it has successfully served more than 3 billion tiles for more than 10 million visitors during 2019. An explanatory diagram is presented in Figure 1.
raster tiles as orthophotos for the various scales. When the user navigates on the orthophotomap, the WebAPI produces a proprietary user specific encrypted request for each tile and for each layer.
The server-side tier is a custom-made map server built to feed the client-tier. The server is built using C++ and .Net and is deployed in windows servers.
The above mechanism allows increased traffic utilization, but it requires minimized server-side usage, and thus it has successfully served more than 3 billion tiles for more than 10 million visitors during 2019. An explanatory diagram is presented in Figure 1.

New Online Cadastral Declaration Web Site
The new Cadastral Declaration Web Site (CDWS) started its operation at the beginning of February 2019. It is a great improvement over the previous service in terms of functionality, usability, endurability, interoperability, technology, and security. The e-declaration process is presented in Figure 2.

New Online Cadastral Declaration Web Site
The new Cadastral Declaration Web Site (CDWS) started its operation at the beginning of February 2019. It is a great improvement over the previous service in terms of functionality, usability, endurability, interoperability, technology, and security. The e-declaration process is presented in Figure 2. The CDWS utilizes the TAXIS authentication system, the system that keeps the Greek citizens' records for taxation purposes. Within TAXIS, each citizen has a unique identification number, which identifies him/her as a person. Using the TAXIS credentials, the Cadastral Agency can directly identify the person without any further need for input. This way all the information collected by the The CDWS utilizes the TAXIS authentication system, the system that keeps the Greek citizens' records for taxation purposes. Within TAXIS, each citizen has a unique identification number, which identifies him/her as a person. Using the TAXIS credentials, the Cadastral Agency can directly identify the person without any further need for input. This way all the information collected by the Cadaster can later be used by the Ministry of Finance, or any other public service, for property valuation and taxation or other purposes.
Upon a successful login, the user can choose among three options: (a) declaring a new property, (b) viewing or editing previous declarations, or (c) reading the manual for communication by email with the support center.
Each property right must be declared separately by the following five distinct steps (Figure 3). Each step should be completed in the order of appearance to provide the easiest and simplest process flow. There are steps that are linked in such a way that one is a prerequisite to another, as in the case of attaching documents to the declared rights, but in general, an experienced user could process each step separately where no prerequisites exist. Each step can be temporarily saved separately so that declaration can be continued at a later time.
ISPRS Int. J. Geo-Inf. 2020, 9,190 8 of 17 Each property right must be declared separately by the following five distinct steps (Figure 3). Each step should be completed in the order of appearance to provide the easiest and simplest process flow. There are steps that are linked in such a way that one is a prerequisite to another, as in the case of attaching documents to the declared rights, but in general, an experienced user could process each step separately where no prerequisites exist. Each step can be temporarily saved separately so that declaration can be continued at a later time. The first step of the declaration process refers to the choice of the province-prefecture and municipality in which the property right/parcel resides. This information will be cross-checked with the geo-located parcel at a later stage to ensure the correct location. The citizen must choose among the predefined values in two dropdown lists. The second step of the declaration process is the declaration of the right holder's personal data, such as full name, parent's name, etc.; contact information is also asked, so that the right holder can be reached whenever needed. In case a property right is declared by a legal representative of the right holder, or through other means of representation, then all additional data should be declared, too, in this step. The users must confirm the validity of all information entered and allow the use of this information for the compilation of cadastre before continuing.
The third step is the most important one in the declaration process because it includes information about the actual property unit. Cadastral information about right holders and property rights are related to a particular 2D land parcel; each parcel takes a unique cadastral code number. This number shows information about the municipality unique code, the sector unique code, the block of plots unique code, and a unique plot/parcel number that increments within each block of plots. Once a land parcel is being developed (usually urban plots are developed), legal subdivisions of the land parcel/plot may also happen, and/or multi-story buildings with individual condos may be constructed. In such cases, new individual cadastral property units are created. Each individual property unit then takes a new unique cadastral code number; this code number makes this property unit individually transferable. The cadastral map though shows only the land parcels/plots and not the building footprints or the individual 3D property units that may exist within them. This 3D strata information is only stored as alphanumerical and not as graphical. Each property right must be declared separately. Thus, the right holder first should locate the land parcel on the map and declare The first step of the declaration process refers to the choice of the province-prefecture and municipality in which the property right/parcel resides. This information will be cross-checked with the geo-located parcel at a later stage to ensure the correct location. The citizen must choose among the predefined values in two dropdown lists.
The second step of the declaration process is the declaration of the right holder's personal data, such as full name, parent's name, etc.; contact information is also asked, so that the right holder can be reached whenever needed. In case a property right is declared by a legal representative of the right holder, or through other means of representation, then all additional data should be declared, too, in this step. The users must confirm the validity of all information entered and allow the use of this information for the compilation of cadastre before continuing.
The third step is the most important one in the declaration process because it includes information about the actual property unit. Cadastral information about right holders and property rights are related to a particular 2D land parcel; each parcel takes a unique cadastral code number. This number shows information about the municipality unique code, the sector unique code, the block of plots unique code, and a unique plot/parcel number that increments within each block of plots. Once a land parcel is being developed (usually urban plots are developed), legal subdivisions of the land parcel/plot may also happen, and/or multi-story buildings with individual condos may be constructed. In such cases, new individual cadastral property units are created. Each individual property unit then takes a new unique cadastral code number; this code number makes this property unit individually transferable. The cadastral map though shows only the land parcels/plots and not the building footprints or the individual 3D property units that may exist within them. This 3D strata information is only stored as alphanumerical and not as graphical. Each property right must be declared separately. Thus, the right holder first should locate the land parcel on the map and declare all other information about the property unit, and the rights separately. The right holder in order to declare a property right must begin the process by first choosing (from a list) the type of property unit to which this particular property right referes. When the user choses a certain type of property unit, the system offers a different flow that includes only the required information based on that choice.
For example, the simplest type of a property unit is that of a land parcel/plot. In this case, the system will pop up a new window where the right holder can enter the respective information.
The declaration of a property right on a property unit, in general, requires four types of information: • Parcel geospatial location, area size, and usage (by choosing among predefined land-use types) Rights on the buildings and/or on the land parcel (and the percentage of them e.g., full ownership or co-ownership) •

Relation of rights with the submitted legal documents/proofs
The most difficult process, for the user, is the geospatial-location of the land parcel, in which the property unit and property rights exist, on the orthophotomap. Following the filling of some basic information such as address, postal code, locality etc., the user must locate the property unit on a new window on the location service (LS) shown in Figure 4.
ISPRS Int. J. Geo-Inf. 2020, 9,190 9 of 17 all other information about the property unit, and the rights separately. The right holder in order to declare a property right must begin the process by first choosing (from a list) the type of property unit to which this particular property right referes. When the user choses a certain type of property unit, the system offers a different flow that includes only the required information based on that choice. For example, the simplest type of a property unit is that of a land parcel/plot. In this case, the system will pop up a new window where the right holder can enter the respective information.
The declaration of a property right on a property unit, in general, requires four types of information: • Parcel geospatial location, area size, and usage (by choosing among predefined land-use types) Rights on the buildings and/or on the land parcel (and the percentage of them e.g., full ownership or co-ownership) • Relation of rights with the submitted legal documents/proofs The most difficult process, for the user, is the geospatial-location of the land parcel, in which the property unit and property rights exist, on the orthophotomap. Following the filling of some basic information such as address, postal code, locality etc., the user must locate the property unit on a new window on the location service (LS) shown in Figure 4.  Search (1) is possible by locality, street name, point of interest, postal code, rural or urban administrative distributions, agricultural subsidy, etc. The system will try to find the best matches and will present the information found (3). The user can then click on each match in order to be navigated to the corresponding location.
The contractor, responsible for the specific area, has already provided a draft set of predefined land parcels that are displayed on top of the orthophoto, using all existing information in public agencies such as municipalities, planning offices, forest agency, etc. If the user agrees with the predefined parcel, then a simple click on it is sufficient for the declaration. If the user does not agree with the draft predefined land parcel, they can either edit the predefined land parcel boundary or digitize a totally new land parcel. All the tools required for editing and digitization have been provided using the toolbar on the orthophotomap. The user can also upload a parcel in several predefined formats (dxf, csv, gml) (2).
In parallel to the Hellenic Cadastre, another big project is close to completion in Greece, the compilation of Forest Maps. This information is vital to each right holder, especially in the non-urban areas, because if a specific land parcel unit resides within a "forested land", then other complicated legal paths need to be followed that may even lead the right holder to completely lose interest in declaring the property right. According to legislation in Greece, forest and forested lands belong to the state unless there is a legal sequence of deeds/titles since 1884.
To help the users, the system will display either the final/ratified forest map (if existing) or the temporary/draft forest map as an overlay with the parcel declared ( Figure 5). When the user digitizes or uploads a land parcel, the system will check whether this is located within a forest polygon in a temporary or a final forest map. In this case, the system will present the area of overlap and suggest to the user to contact the cadastral office for further details ( Figure 6). ISPRS Int. J. Geo-Inf. 2020, 9,190 10 of 17 and will present the information found (3). The user can then click on each match in order to be navigated to the corresponding location. The contractor, responsible for the specific area, has already provided a draft set of predefined land parcels that are displayed on top of the orthophoto, using all existing information in public agencies such as municipalities, planning offices, forest agency, etc. If the user agrees with the predefined parcel, then a simple click on it is sufficient for the declaration. If the user does not agree with the draft predefined land parcel, they can either edit the predefined land parcel boundary or digitize a totally new land parcel. All the tools required for editing and digitization have been provided using the toolbar on the orthophotomap. The user can also upload a parcel in several predefined formats (dxf, csv, gml) (2).
In parallel to the Hellenic Cadastre, another big project is close to completion in Greece, the compilation of Forest Maps. This information is vital to each right holder, especially in the non-urban areas, because if a specific land parcel unit resides within a "forested land", then other complicated legal paths need to be followed that may even lead the right holder to completely lose interest in declaring the property right. According to legislation in Greece, forest and forested lands belong to the state unless there is a legal sequence of deeds/titles since 1884.
To help the users, the system will display either the final/ratified forest map (if existing) or the temporary/draft forest map as an overlay with the parcel declared ( Figure 5). When the user digitizes or uploads a land parcel, the system will check whether this is located within a forest polygon in a temporary or a final forest map. In this case, the system will present the area of overlap and suggest to the user to contact the cadastral office for further details ( Figure 6).   A high accuracy three-dimensional map of the area along with the two-dimensional map is available, so that the right holder can get a more realistic overview of the actual territory (Figure 7), as presented in detail in [36].  A high accuracy three-dimensional map of the area along with the two-dimensional map is available, so that the right holder can get a more realistic overview of the actual territory (Figure 7), as presented in detail in [36]. A high accuracy three-dimensional map of the area along with the two-dimensional map is available, so that the right holder can get a more realistic overview of the actual territory (Figure 7), as presented in detail in [36].  Once the right holder locates a parcel, the system will check if this resides within the minicipality boundaries selected in the first step. If not, it will ask the user to either change the selected municipality in the first step, or relocate the parcel within the correct municipality boundaries. That way, the gross errors detected in the previous cadastral phases of the Hellenic Cadastral project are now avoided. In the previous phases, right holders could not easily locate the land parcel on the orthophotomap, especially in the rural areas, due to similar patterns of olive trees, vineyards, or bushes [10,36]. Because of that, in some cases, parcels were declared in the wrong municiplities.
Following the successful location of the land parcel, other descriptive information such as buildings, if applicable, and other co-owners (if existing) should be declared. The type of property right being declared, such as full ownership, partial ownership, easment, confiscation, court decisions etc., must be chosen among predefined lists.
Each declared property right must be associated with one or more legal deeds/titles. The system has a very efficient and detailed flow based on the user interaction that will only allow valid entries per case. In case the property right must be associated with more than one legal document, the system will guide the user by allowing the repetition of the process. Each legal title has lots of metadata that must be filled out such as the type, creator, etc.
To make things simpler, the system will also ask the user whether he/she wishes to declare more property rights on more property units/parcels in the same municipality. Based on the answer, different flows will follow, allowing easy complition of the process.
In the simplest case, where the user only wishes to declare property rights on one property unit/parcel, the system will automatically correlate the declared rights to the declared legal documents without any further user interaction. If the user has more properties in the same municipality or the same land parcel, the process of associating each right with the respective legal documents must be followed.
The fourth step (as shown in Figure 2) is about uploading the scanned documents according to the information entered in the previous steps. The system expects certain documents, based on the right-document associations that were created in previous steps.
The fifth step is the final one. The system checks for the validity of all information declared and reports an error status ( Figure 8) if problems are detected. In the latter case, each step must be revised and corrected, based on the indicated errors, before submission of the declaration and online payment is allowed. After the whole process is completed, only "read only" access is permitted.
ISPRS Int. J. Geo-Inf. 2020, 9,190 12 of 17 Once the right holder locates a parcel, the system will check if this resides within the minicipality boundaries selected in the first step. If not, it will ask the user to either change the selected municipality in the first step, or relocate the parcel within the correct municipality boundaries. That way, the gross errors detected in the previous cadastral phases of the Hellenic Cadastral project are now avoided. In the previous phases, right holders could not easily locate the land parcel on the orthophotomap, especially in the rural areas, due to similar patterns of olive trees, vineyards, or bushes [10,36]. Because of that, in some cases, parcels were declared in the wrong municiplities.
Following the successful location of the land parcel, other descriptive information such as buildings, if applicable, and other co-owners (if existing) should be declared. The type of property right being declared, such as full ownership, partial ownership, easment, confiscation, court decisions etc., must be chosen among predefined lists.
Each declared property right must be associated with one or more legal deeds/titles. The system has a very efficient and detailed flow based on the user interaction that will only allow valid entries per case. In case the property right must be associated with more than one legal document, the system will guide the user by allowing the repetition of the process. Each legal title has lots of metadata that must be filled out such as the type, creator, etc.
To make things simpler, the system will also ask the user whether he/she wishes to declare more property rights on more property units/parcels in the same municipality. Based on the answer, different flows will follow, allowing easy complition of the process.
In the simplest case, where the user only wishes to declare property rights on one property unit/parcel, the system will automatically correlate the declared rights to the declared legal documents without any further user interaction. If the user has more properties in the same municipality or the same land parcel, the process of associating each right with the respective legal documents must be followed.
The fourth step (as shown in Figure 2) is about uploading the scanned documents according to the information entered in the previous steps. The system expects certain documents, based on the right-document associations that were created in previous steps.
The fifth step is the final one. The system checks for the validity of all information declared and reports an error status ( Figure 8) if problems are detected. In the latter case, each step must be revised and corrected, based on the indicated errors, before submission of the declaration and online payment is allowed. After the whole process is completed, only "read only" access is permitted.

Property Unit Geo-Spatial Location with a Mobile Phone
To enable the right holders to locate their land parcels without the need of hiring a professional surveyor, a web application for GPS enabled mobile devices such smart phones was created. Every right holder can access this web applicaton, while on the field, and use it to record the land parcel boundaries of interest. These records can be uploaded in the location service providing the user with a draft boundary polygon. The user can use the recorded measurements as such or alter the boundaries to better match the accuracy of the basemap and continue with the declaration. The official basemap is an orthophotomap.
Upon visiting the web application, a valid email address must be provided. This address will be used by the system to send all recorded information. In conformance with GDPR directive Regulation (EU) 2016/679 [37], the use of email for cadastral purposes must be accepted.
The web application will try to locate the mobile devices' location with the highest possible accuracy and present its position on an orthophoto.
The user should stand steel for a few seconds on top of each point that needs to be measured before taking a measurement. Normally the points will be the nodes of the polygon of the land parcel being declared. The process should be repeated for each node. The map will continously move and center to the user's position. Wrong measurements can be deleted. While the process is being repeated, the map will gradually draw the parcel's boundary ( Figure 9). ISPRS Int. J. Geo-Inf. 2020, 9,190 13 of 17

Property Unit Geo-Spatial Location with a Mobile Phone
To enable the right holders to locate their land parcels without the need of hiring a professional surveyor, a web application for GPS enabled mobile devices such smart phones was created. Every right holder can access this web applicaton, while on the field, and use it to record the land parcel boundaries of interest. These records can be uploaded in the location service providing the user with a draft boundary polygon. The user can use the recorded measurements as such or alter the boundaries to better match the accuracy of the basemap and continue with the declaration. The official basemap is an orthophotomap.
Upon visiting the web application, a valid email address must be provided. This address will be used by the system to send all recorded information. In conformance with GDPR directive Regulation (EU) 2016/679 [37], the use of email for cadastral purposes must be accepted.
The web application will try to locate the mobile devices' location with the highest possible accuracy and present its position on an orthophoto.
The user should stand steel for a few seconds on top of each point that needs to be measured before taking a measurement. Normally the points will be the nodes of the polygon of the land parcel being declared. The process should be repeated for each node. The map will continously move and center to the user's position. Wrong measurements can be  When the digitization of the parcel boundary is completed, the user should complete the process by giving a friendly name, such as "granddad's parcel" for the measurement to help distinguish more than one measurement.
The system will automatically create an email message that will include the following information: • An image with the outline of the land parcel on it • A csv file with the coordinates measured that can be used as input in area (2) in the LS • The friendly name that was given by the user for the specific measurement • A unique system identifier for the measurement Each measurement is stored in the central database of the Hellenic Cadaster and includes the geometric accuracy information as declared by the mobile device. The measurements will be given to the contractors along with the unique identifiers in order to evaluate how reliable these measurements are based on their accuracy, and thus helping them produce more accurate boundaries.

Conclusions
Both of the above described focused IT services have already been in operation for more than eight months. Statistics from the previous cadastral phases indicated that online submission of declarations was less than 1% of the total declarations submission by the right holders.
So far the usage of modern and more focused IT services in the current phase shows that about one in every three declarations is being delivered over the internet. This percentage is impressive if we also consider the fact that the geospatial location of properties in the last phase of the program is far more difficult since it mainly involves rural properties difficult to be recognized due to similar land-use patterns.
During the declaration period, professionals such as surveyors were so busy supporting property owners through the process that it literally became a bottleneck. Many citizens were forced to locate the properties by themselves either on the basemap or by using a GPS enabled mobile device, because the response time of the professionals was not fast enough to meet the deadlines. Because of the large demand and lack of existing man power, other professionals, that were not previously involved in Cadastre related processes, such as civil engineers, were used by property onwers.
Various professionals, such as surveyors, engineers, architects, and many others including even tax accountants, have been staunch supporters of these services as they can earn money helping the right holders with the declaration submission from their offices; their personal data are not recorded though. Usage statistics indicate that many right holders as well, far more than the previous generations, used the services by themselves, without getting any professional support. Such statistics are not collected by the system though.
Up to January 2020, more than 1.1 million declarations have been submitted online from more than 750.000 right holders. About 1.8 million polygons have been entered online through the LS and more than 76.800 points have been registered through the mobile service.
Mobile service usage was expected to be higher. As for the moment, it seems that most of the right holders have either digitized their properties in LS, or hired a professional to produce a survey map. Lack of proper advertising of such a service is also a problem since most of the right holders are still unaware of the existance of a mobile service for the geo-spatial location of land parcels.
Crowdsourcing in the cadastral surveying has been widely accepted by citizens producing positive media reports. Everyday communication between the help center and the users suggests that most people have questions rather on legal issues than on spatial/technical issues.
Beside the web applications developed solely for the cadastral declaration submission, the same technology described in chapter 4, was used to build another web application where everyone could use the service without the need for authorisation, digitize polygons, and produce extracts. Contractors, cadastral surveyors, and even some media have suggested to the right holders, that did not want or could not use the Internet to submit the declarations, to either provide a survey map of the parcel/plot or to try to deliniate the parcel boundaries using the aforementioned mobile web application. More than 1.7 million extracts were created that were most probably used to assist declarations in Cadastral offices, adding more value to the created web services.
Crowdsourcing methodology and its current implementation for the Hellenic Cadastre was a great success and fullfilled the expected goals. The tool created is being used to cover all spatial web aspects of all business processes of the Hellenic Cadastre, including Cadastre creation, Forest Maps ratification, Cadastre operation, and G2C services. The customer-oriented custom developed spatial services have been proven as stable, fast, and reliable in serving many people with much content.
The developed WebAPI is currently being shared with other public agencies supporting their needs and providing high efficiency and functionality at minimal costs while fullfilling National Security restrictions. WebAPI is currently evolving, through new versions, including new requests from the Hellenic Cadastre and from other agencies. The WebAPI will be used as part of the National Spatial Data Infrastructure (NSDI) as a way to deliver definitive spatial information for all public sector. WebAPI supports several OGC standards, such as WMS, making it very easy for usage along with any other Map Server Commercial or not. WebAPI is expected to expand its usage in the following years as more public sector agencies will be using it to share their spatial data in G2C or G2G services.